CN117042678A

CN117042678A - Devices, systems, and methods for monitoring symptoms of neurological disorders

Info

Publication number: CN117042678A
Application number: CN202280022918.9A
Authority: CN
Inventors: N·古艾尔哈杜; D·索夫列夫斯基
Original assignee: Neurodynamics Co ltd
Current assignee: Neurodynamics Co ltd
Priority date: 2021-02-22
Filing date: 2022-02-22
Publication date: 2023-11-10

Abstract

Disclosed herein is a system for controlling the delivery of electrical stimulation to a subject. The system includes a control device configured to send stimulation instructions to an electrical stimulation generator to cause the electrical stimulation generator to deliver transcranial electrical stimulation to one or more electrodes arranged to be positioned near a target area of the subject's brain. The stimulation instructions include stimulation parameter values. The control device is configured to receive sensor data from an optical sensor arranged to be positioned in the vicinity of the target area and to transmit an updated stimulation instruction containing an updated stimulation parameter value to the electrical stimulation generator to cause the electrical stimulation generator to modify a characteristic of the stimulation. The system is configured to analyze the sensor data to determine an activity metric and determine an updated stimulation parameter value based on the determined activity metric.

Description

Devices, systems, and methods for monitoring symptoms of neurological disorders

Technical Field

Embodiments relate generally to methods, devices, and systems for monitoring and, in some embodiments, treating symptoms of a neurological disorder, particularly symptoms of a neurobehavioral disorder, such as Attention Deficit Hyperactivity Disorder (ADHD), in a subject.

Background

Subjects with ADHD tend to exhibit symptoms such as difficulty concentrating, difficulty controlling impulsive behavior, and/or possible excessive or hyperactive. There are three main types of ADHD: mainly manifested as inattention, where the subject has difficulty following the instruction and concentrating, being easily distracted and confusing; is mainly manifested by hyperactive-impulsive, in which the subject is restless and impulsive; and a combination of manifestations, wherein the subject exhibits both types of symptoms.

Techniques for assessing ADHD symptoms include measuring psychometric tests that perform functional performance, such as test working memory, attention, and impulse control, and ADHD symptoms may appear to have decreased performance in these tests.

Brain electrical stimulation is known to have a significant impact on the cognitive process, with various different effects depending on the type of stimulation applied. According to Min-Fang Kuo and Michael a.nitsche, transcranial electrical stimulation effects on cognition (Effects of transcranial electrical stimulation on cognition) (2012) 43 (3), clinical EEG and neuroscience (Clinical EEG and Neuroscience) 192 to 199, (the entire contents of which are incorporated herein by reference), non-invasive brain electrical stimulation techniques can amplify and/or can simulate the neurophysiologic processes required during cognition and, in addition, different types of stimulation can produce different responses. For example, transcranial direct current stimulation (tDCS) can induce physiological changes that resemble changes in neuroplasticity of cortical function, which is considered critical for learning and memory formation. Many studies have shown the beneficial effect of tDCS on task performance. Furthermore, kuo and Nitsche (2012) observed that other techniques such as alternating current stimulation (tcacs) and random noise stimulation (tRNS) can regulate other cortical activity depending on the frequency of the electrical stimulation.

It is desirable to address or ameliorate one or more of the disadvantages or shortcomings associated with the prior art for treating neurological disorders such as ADHD and other neurological disorders in a subject, or at least to provide a useful alternative thereto.

Any discussion of documents, acts, materials, devices, articles or the like which has been included in the present specification is not to be taken as an admission that any or all of these matters form part of the prior art base or were common general knowledge in the field relevant to the present disclosure as it existed before the priority date of each claim of the present disclosure.

Disclosure of Invention

Some embodiments relate to a system for controlling delivery of electrical stimulation to a subject, the system comprising: a control device configured to: transmitting stimulation instructions to an electrical stimulation generator to cause the electrical stimulation generator to deliver transcranial electrical stimulation to one or more electrodes arranged to be positioned near a target area of the subject's brain, wherein the stimulation instructions comprise at least one stimulation parameter value; receiving sensor data from one or more optical sensors arranged to be positioned in proximity to the target area; and transmitting updated stimulation instructions containing one or more updated stimulation parameter values to the electrical stimulation generator to cause the electrical stimulation generator to modify one or more characteristics of the stimulation; wherein the system is further configured to: analyzing the sensor data to determine an activity metric; and determining the one or more updated stimulation parameter values based on the determined activity metrics.

The sensor data may include pre-stimulus sensor data acquired prior to delivering the stimulus to the one or more electrodes, during-stimulus sensor data acquired while delivering the stimulus to the one or more electrodes, and post-stimulus sensor data acquired after delivering the stimulus to the one or more electrodes, and wherein the system is configured to: determining pre-stimulus, during-stimulus, and post-stimulus values of one or more features from the pre-stimulus sensor data, during-stimulus sensor data, and post-stimulus sensor data, respectively; for each of the one or more features, determining a relative change in the value of the feature from (i) the pre-stimulus value to the post-stimulus value, (ii) the pre-stimulus value to the stimulus duration value, and (iii) the stimulus duration value to the post-stimulus value; providing the relative change in the values of the one or more features and the stimulation parameter values to an activity determination model; and determining the activity metric by the activity determination model.

The activity metric may indicate that sufficient stimulus has been delivered to the subject.

The one or more features may include: functional connectivity between pairs of optical sensor channels and/or statistical measures of data acquired from the optical sensor channels.

The one or more features may be extracted from sensor data obtained from stimulation electrodes surrounding the one or more electrodes and/or optical channel pairs between stimulation electrodes of the one or more electrodes.

The sensor data may include data acquired from the left prefrontal cortex of the subject, the medial prefrontal cortex of the subject, and/or a boundary region between the medial prefrontal and left prefrontal of the subject.

The sensor data may include data acquired from the subject's right prefrontal cortex, the subject's medial prefrontal cortex, and/or a boundary region between the subject's medial and right prefrontal lobes.

A system configured to determine the one or more updated stimulation parameter values based on the determined activity metrics may include: in response to determining that the activity measure is less than a threshold, increasing the stimulation parameter value and reapplying the stimulation at the increased stimulation parameter value; and in response to determining that the activity metric has reached the threshold, determining the stimulation parameter value as a user-specific calibrated stimulation parameter.

The system may further comprise a head-mountable array carrying optical sensors, and wherein the optical sensors are functional near infrared spectrum sensors (fNIRS). The head-mountable array may further carry the one or more electrodes.

The system may further comprise: an optical sensor module coupled to the one or more optical sensors, the optical sensor module configured to cause light to be emitted from respective emitters of the one or more optical sensors and to receive signals indicative of reflected light from respective detectors of the one or more optical sensors, wherein the signals are indicative of a cerebral hemodynamic response associated with neural activity in the target area; wherein the optical sensor module is configured to provide the sensor data to the control device, the sensor data being based on signals received from the respective one or more sensors; and wherein the optical sensor module is configured to operate in response to instructions received from the control device.

The control means may be configured to cause the optical sensor module to switch the emitters of the one or more optical sensors on and off at a relatively high frequency to produce a lock-in amplifier effect to improve the signal-to-noise ratio (SNR) of the respective detected reflected light signals.

Each of the one or more optical sensors may comprise an emitter and first and second detectors and form two detector channels, and wherein the control means is configured to demodulate the signals of the detector channels of each of the optical sensors.

Sensor data from multiple functional channels may be downsampled.

The sensor data may comprise measurement data including one or more of: (i) Reflected light intensities at two different wavelengths detected by the one or more sensors; (ii) an oxygenated hemoglobin (HbO) concentration; (iii) deoxyhemoglobin (HbR) concentration; (iv) total hemoglobin (ThB) concentration; and (v) measuring the relative change in any of (i) to (iv).

The control means may be configured to cause the stimulus generator to supply one or more of: (i) transcranial direct current stimulation (tDCS); (ii) transcranial alternating current stimulation (tcacs); (iii) transcranial random noise stimulation (tRNS); (iv) transcranial pulsed current stimulation (tcs); (v) transcranial random noise stimulation (tRNS); and (vi) and oscillation tDCS (otDCS).

The stimulation instructions may include one or more of the following: (i) a voltage; (ii) an electrical current; (iii) frequency; (iv) duration; and (v) offset.

The control device may be configured to cause the electrical stimulation generator to deliver relatively short electrical stimulation pulses to the one or more electrodes, and to cause the optical sensor module to record reflected signals from the respective sensors after the relatively short electrical stimulation pulses have been delivered to the one or more electrodes.

The control device may be configured to cause the electrical stimulation generator to deliver a relatively long electrical stimulation session to the one or more electrodes and to cause the optical sensor module to record the reflected signals from the respective sensors while the electrical stimulation is delivered to the one or more electrodes.

The control device may be configured to receive recorded data from the one or more optical sensors before, during and/or after delivery of the electrical stimulus to the one or more electrodes.

The control device may be configured to continuously monitor brain activity of the subject.

The control device may be configured to initiate a session in response to instructions received from a cognitive performance monitoring application disposed on a computing device in communication with the control device.

The system may further comprise a computing device or server in communication with the control device over a communication network, and wherein the computing device or server is configured to: receiving sensor data from the control device; analyzing the sensor data to determine an activity metric; determining one or more updated stimulation parameter values based on the determined activity metrics; and transmitting the updated stimulation parameter value to the control device.

The control device may be configured to transmit the sensor data to a computing device or server for processing and to receive updated stimulation parameter values from the respective computing device or server.

Some embodiments relate to a system for determining stimulation parameters to control delivery of electrical stimulation to a subject, the system comprising: one or more processors; and a memory containing executable instructions that, when executed by the one or more processors, cause the system to: receiving sensor data from the control device, the sensor data originating from one or more optical sensors positioned near a target area of the subject's head; analyzing the sensor data to determine an activity metric; determining the one or more updated stimulation parameter values based on the determined activity metric, wherein the updated stimulation parameter values are indicative of characteristics of transcranial electrical stimulation to be delivered by an electrical stimulation generator to the subject under control of the control device; and transmitting the updated stimulation parameter value to the control device.

The system may further include an activity determination model configured to receive as input features extracted from the sensor data and to provide as output an activity metric.

The control device may be configured to allow selection of a subset of electrodes to be used in applying the stimulus, thereby tailoring the control device to fit the head size of a particular subject. The control device may be configured to receive a head size indication from the subject via the user interface and determine a subset of electrodes to use based on the head size indication. The control device may be configured to: delivering at least a first test signal to each of one or more subsets of electrodes of the array; analyzing the test responses detected by the respective sensor modules; determining a suitable subset of electrodes for the subject based on the detected test response; and selecting a suitable subset of electrodes for applying stimulation to the head of the subject.

The system may further include a positioning feedback module configured to assist the subject in correctly positioning the array relative to the subject's head. The positioning feedback module may be configured to: determining one or more images of a subject wearing the array of control devices; detecting a position of one or more facial features of the subject within the one or more images; detecting a position of the array within the one or more images relative to the determined facial features; comparing the determined position of the array with a target position; and in response to determining that the location of the array falls within an acceptable range, determining that the array is properly positioned; and in response to determining that the position of the array falls within an acceptable range, determining that the array is incorrectly placed, providing feedback to the subject via the user interface to assist them in repositioning the array to achieve the target position.

Some embodiments relate to a method for controlling delivery of electrical stimulation to a subject, the method comprising: transmitting stimulation instructions to an electrical stimulation generator to cause the electrical stimulation generator to deliver transcranial electrical stimulation to one or more electrodes arranged to be positioned near a target area of the subject's brain, wherein the stimulation instructions comprise at least one stimulation parameter value; receiving sensor data from one or more optical sensors arranged to be positioned in proximity to the target area; analyzing the sensor data to determine an activity metric; determining one or more updated stimulation parameter values based on the determined activity metrics; and transmitting updated stimulation instructions containing one or more updated stimulation parameter values to the electrical stimulation generator to cause the electrical stimulation generator to modify one or more characteristics of the stimulation.

Some embodiments relate to a method for determining stimulation parameters to control delivery of electrical stimulation to a subject, the method comprising: receiving sensor data from a control device, the sensor data originating from one or more optical sensors positioned near a target area of a subject's head; analyzing the sensor data to determine an activity metric; determining the one or more updated stimulation parameter values based on the determined activity metric, wherein the updated stimulation parameter values are indicative of characteristics of transcranial electrical stimulation to be delivered by an electrical stimulation generator to the subject under control of the control device; and transmitting the updated stimulation parameter value to the control device.

Some embodiments relate to a non-transitory machine-readable medium storing instructions that, when executed by one or more processors, cause a computing device to perform the disclosed methods.

Some embodiments relate to a system for inferring symptom severity and/or behavioral progression in a subject undergoing treatment for one or more symptoms of a neurological disorder, the system comprising: a control device configured to receive sensor data from one or more optical sensors arranged to be positioned near a target area of the subject's brain; wherein the system is further configured to: determining task data comprising one or more scores associated with the performance of the subject in undertaking one or more respective tasks; determining a symptom severity or progression measure based on the sensor data and the task data; and outputting the symptom severity and/or progression measure.

The symptom severity and/or progression metric may comprise a plurality of scores, each score indicating a severity of a behavior or experience associated with the neurological disorder.

The neurological disorder may be ADHD and the symptom severity and/or progression measure comprises a score of one or more of: (i) an overall ADHD rating scale score, (ii) an ADHD core symptom score, (iii) a inattention score, (iv) a hyperactivity score, and (v) an impulsivity score.

In some embodiments, the system comprises a symptom severity and/or progress determination model configured to determine the symptom severity and/or progress metric based on the task data and the sensor data, wherein the symptom severity and/or progress determination model has been trained using data derived from a clinical population.

In some embodiments, the system includes a feature extraction module configured to determine one or more feature values from the sensor data and provide the feature values to the symptom severity and/or progress determination model. The one or more characteristic values may be indicative of functional connectivity between the pair of optical sensor channels and/or statistical measures of data acquired from the optical sensor channels.

One or more eigenvalues derived from sensor data acquired from an optical channel configured to measure activity at the right prefrontal cortex may be provided to the symptom severity and/or progression determination model to determine an overall ADHD symptom severity metric. Characteristic values based on the response time and omission error metrics of the task data may be provided to the symptom severity and/or progress determination model to determine an overall ADHD symptom severity metric.

One or more eigenvalues derived from sensor data acquired from an optical channel configured to measure activity at the right prefrontal cortex may be provided to the symptom severity and/or progression determination model to determine an ADHD core symptom severity metric.

One or more eigenvalues derived from sensor data acquired from an optical channel configured to measure activity at and/or toward, overlapping with or near the medial prefrontal cortex of the subject may be provided to the symptom severity and/or progression determination model to determine a measure of inattention severity. Characteristic values based on the response time and omission error metrics of the task data may be provided to the symptom severity and/or progress determination model to determine a concentration-free severity metric.

One or more eigenvalues derived from sensor data acquired from an optical channel configured to measure activity at the right prefrontal cortex, left prefrontal cortex, and/or regions overlapping the left prefrontal cortex and the medial prefrontal cortex of the subject may be provided to the symptom severity and/or progression determination model to determine a measure of hyperactivity.

One or more eigenvalues derived from sensor data acquired from an optical channel configured to measure activity at the medial prefrontal cortex of the subject and/or toward, overlapping with, or near the medial prefrontal cortex of the subject may be provided to the symptom severity and/or progression determination model to determine an impulse severity measure. Characteristic values of the response time metrics based on task data may be provided to the symptom severity and/or progress determination model to determine an impulse severity metric.

The system may further comprise a head-mountable array carrying optical sensors, and wherein the optical sensors are functional near infrared spectrum sensors (fNIRS). The system may further comprise: an optical sensor module coupled to the one or more optical sensors, the optical sensor module configured to cause light to be emitted from respective emitters of the one or more optical sensors and to receive signals indicative of reflected light from respective detectors of the one or more optical sensors, wherein the signals are indicative of a cerebral hemodynamic response associated with neural activity in the target area; wherein the optical sensor module is configured to provide the sensor data to the control device, the sensor data being based on signals received from the respective one or more sensors; and wherein the optical sensor module is configured to operate in response to instructions received from the control device.

The control means may be configured to cause the optical sensor module to switch the emitters of the one or more optical sensors on and off at a relatively high frequency to produce a lock-in amplifier effect to improve the signal-to-noise ratio (SNR) of the respective detected reflected light signals. Each of the one or more optical sensors may comprise an emitter and first and second detectors and form two detector channels, and wherein the control means is configured to demodulate the signals of the detector channels of each of the optical sensors.

Sensor data from multiple functional channels may be downsampled.

The system may be configured to determine a quality metric indicative of a quality of each detector channel of the one or more optical sensors, and in response to the quality metric falling below a quality threshold, to exclude sensor data from the respective detector channel when determining the symptom severity or progress metric.

The system may be configured to determine a subset of sensor data based on the time-stamped sensor data and the time-stamped task data, the subset of sensor data containing sensor data associated with a task acquired when the subject performs the task. The system may further include a feature extraction module configured to extract one or more features from the task-associated sensor data, and wherein determining a symptom severity or progress metric based on the sensor data and the task data includes determining the symptom severity or progress metric based on the one or more features and the task data.

The system may include a cognitive performance monitoring application configured to evaluate a subject performing one or more specific tasks; one or more task scores are assigned to the subject based on their performance in undertaking the task, wherein the task data includes the one or more task scores.

The control device may be configured to send stimulation instructions to the electrical stimulation generator to cause the electrical stimulation generator to deliver transcranial electrical stimulation to one or more electrodes arranged to be positioned near a target area of the subject's brain.

The head-mountable array may further carry the one or more electrodes.

The system may further comprise a computing device or server in communication with the control device over a communication network, and wherein the computing device or server is configured to: receiving sensor data from the control device; determining task data; determining a symptom severity or progression measure based on the sensor data and the task data; and outputting the symptom severity or progression measure. The control device may be configured to transmit the sensor data to the computing device or server.

The control device may be configured to allow selection of a subset of electrodes of the array to be used in applying the stimulus, thereby tailoring the control device to fit the head size of a particular subject. The control device may be configured to receive a head size indication from the subject via the user interface and determine a subset of electrodes to use based on the head size indication. The control device may be configured to: delivering at least a first test signal to each of one or more subsets of electrodes of the array; analyzing the test responses detected by the respective sensor modules; determining a suitable subset of electrodes for the subject based on the detected test response; and selecting a suitable subset of electrodes for applying stimulation to the head of the subject.

The system may further include a positioning feedback module configured to assist the subject in correctly positioning the array relative to the subject's head. The positioning feedback module may be configured to: determining one or more images of a subject wearing an array of the system; detecting a position of one or more facial features of the subject within the one or more images; detecting a position of the array within the one or more images relative to the determined facial features; comparing the determined position of the array with a target position; and in response to determining that the location of the array falls within an acceptable range, determining that the array is properly positioned; and in response to determining that the position of the array falls within an acceptable range, determining that the array is incorrectly placed, providing feedback to the subject via the user interface to assist them in repositioning the array to achieve the target position.

Some embodiments relate to a system for inferring symptom severity and/or behavioral progression in a subject undergoing treatment for one or more symptoms of a neurological disorder, the system configured to: receiving sensor data from a control device, the sensor data originating from one or more optical sensors positioned near a target area of a subject's head; determining task data comprising one or more scores associated with the performance of the subject in undertaking one or more respective tasks; determining a symptom severity or progression measure based on the sensor data and the task data; and outputs a measure of the severity or progression of the symptom.

Some embodiments relate to a method for inferring behavioral progression in a subject undergoing treatment for one or more symptoms of a neurological disorder, the method comprising: receive sensor data from one or more optical sensors arranged to be positioned near a target area of the subject's brain; determining task data comprising one or more scores associated with performance of the subject in undertaking one or more respective tasks; determining a symptom severity or progression measure based on the sensor data and the task data; and outputting the symptom severity and/or progression measure.

Some embodiments relate to a method for inferring symptom severity and/or behavioral progression in a subject undergoing treatment for one or more symptoms of a neurological disorder, the method comprising: receiving sensor data from a control device, the sensor data originating from one or more optical sensors positioned near a target area of a subject's head; determining task data comprising one or more scores associated with the performance of the subject in undertaking one or more respective tasks; determining a symptom severity or progression measure based on the sensor data and the task data; and outputting the symptom severity and/or progression measure.

Some embodiments relate to a server arranged to communicate with a control device for detecting brain activity of a subject over a communication network, the server being configured to: receiving sensor data from the control device, the sensor data indicating reflected light intensities at two different wavelengths detected by one or more sensors coupled to the control device when the sensors are positioned near a target region of a subject's brain and when the subject is assuming a particular task; receive one or more task scores from a cognitive assessment application deployed on a computing device associated with the subject, wherein the cognitive assessment application is configured to assess the subject performing a particular task and assign the one or more task scores to the subject based on their performance; providing the sensor data and the one or more task scores as inputs to a symptom severity or progress determination model; and determining, as an output of the symptom severity or progress determination model, a symptom severity or progress measure indicative of the severity of the symptom of the neurological disorder or the progress the subject has in treating the symptom of the neurological disorder.

Some embodiments may relate to a computer-implemented method of inferring behavioral progression in a subject undergoing treatment for one or more symptoms of a neurological disorder, the method comprising: receiving sensor data from the control device, the sensor data indicating reflected light intensities at two different wavelengths detected by one or more sensors coupled to the control device when the sensors are positioned near a target region of a subject's brain and when the subject is assuming a particular task; receive one or more task scores from a cognitive assessment application deployed on a computing device associated with the subject, wherein the cognitive assessment application is configured to assess the subject performing a particular task and assign the one or more task scores to the subject based on their performance; providing the sensor data and the one or more task scores as inputs to a symptom severity or progress determination model; and determining, as an output of the symptom severity or progress determination model, a symptom severity or progress measure indicative of the severity of the symptom of the neurological disorder or the progress the subject has in treating the symptom of the neurological disorder.

Some embodiments relate to a head-mountable apparatus comprising: an array comprising a plurality of optical sensor components disposed along a length of the array, each optical sensor component comprising an emitter and first and second detectors, wherein the emitter is disposed adjacent to the first detector to form a first relatively short channel and the emitter is disposed at a relatively greater distance from the second detector to form a first relatively long channel; an optical sensor module configured to cause light to be emitted from a selected emitter of the optical sensor component and to receive a signal indicative of reflected light from a first detector and a second detector of the selected optical sensor component, wherein the signal is indicative of a cerebral hemodynamic response associated with neural activity of the emitter detector in the targeted area; and wherein the array further comprises a plurality of electrodes, each electrode disposed between a pair of adjacent optical sensor components. Both the emitter and the first detector may be disposed toward a first end of the array, and the second detector is disposed at a second end of the array. The one or more electrodes may be configured to deliver electrical stimulation to the subject. The one or more electrodes may be configured to determine an electroencephalogram (EEG) signal from the subject.

Throughout this specification, the word "comprise", or variations such as "comprises" or "comprising", will be understood to imply the inclusion of a stated element, integer or step, or group of elements, integers or steps, but not the exclusion of any other element, integer or step, or group of elements, integers or steps.

Drawings

The various drawings in the drawings depict only typical embodiments of the disclosure and are not to be considered limiting of its scope.

FIG. 1 depicts a schematic diagram of an apparatus for monitoring and/or treating symptoms of a neurological disorder placed on a user according to some embodiments;

FIG. 2 depicts a block diagram of a system architecture for monitoring and/or treating symptoms of a neurological disorder and including the apparatus of FIG. 1, in accordance with some embodiments;

FIG. 3 depicts a block diagram of the device of FIG. 1, according to some embodiments;

fig. 4, 5 and 6 depict front and perspective views, respectively, of a mount of the apparatus of fig. 1, in accordance with some embodiments;

FIG. 7a depicts an electrode and optical sensor mount array of the device of FIG. 1, according to some embodiments;

FIG. 7b depicts an electrode and optical sensor mount array of the device of FIG. 1, according to some embodiments;

Fig. 8 depicts a process flow of a method of controlling transcranial electrical stimulation delivery according to some embodiments;

FIG. 9A is a graphical depiction of combined current versus time supplied by an electrical stimulation source of the system of FIG. 2 including transcranial direct current stimulation (tDCS) and transcranial alternating current stimulation (tACS) in accordance with some embodiments;

FIG. 9B is a graphical depiction of current versus time including transcranial direct current stimulation (tDCS), transcranial alternating current stimulation (tACS), transcranial pulsed current stimulation (tPCS), transcranial random noise stimulation (tNS), and oscillating tDCS (otDCS);

FIG. 10 depicts a process flow of a method of inferring behavioral progression in a subject undergoing treatment for one or more symptoms of a neurological disorder according to some embodiments;

FIG. 11 is an exemplary plot of oxygenated hemoglobin (HbO), deoxygenated hemoglobin (HbR), and total hemoglobin (ThB) concentrations derived from optical signals;

FIG. 12 depicts an electrode and optical sensor mount array of the device of FIG. 1, depicting a sensing region, in accordance with some embodiments;

FIG. 13 depicts a top view of an electrode array on a user's head according to some embodiments;

FIG. 14 depicts a top view of an electrode array on a user's head according to some embodiments;

FIG. 15 is an exemplary graph of probability of adequate stimulus versus current (mA);

16 a-16 e are exemplary graphs of actual symptom severity metrics/scores (as per ADHD rating scale questionnaire score) versus predicted symptom severity metrics/scores (as predicted by symptom severity determination model 327) for each of the categories of overall ADHD score (FIG. 16 a), primary ADHD diagnostic score (FIG. 16 b), inattention (FIG. 16 c), hyperactivity (FIG. 16 a), impulsivity (FIG. 16 e);

FIG. 17 is a schematic overview of a process of determining activity metrics using an activity determination model, according to some embodiments; and

fig. 18 is a schematic overview of a process of determining a symptom severity or progress metric using a symptom severity or progress determination model, according to some embodiments.

Detailed Description

Embodiments relate generally to methods and systems for monitoring and, in some embodiments, treating symptoms of a neurological disorder, in particular a neurobehavioral disorder, such as Attention Deficit Hyperactivity Disorder (ADHD), in a subject.

The application of electrical stimulation to the brain of a subject suffering from a neurobehavioral disorder is effective in inducing physiological changes that over time result in a neuroplastic change in cortical function, which is considered critical for learning and memory formation. However, the degree and duration of stimulation required to improve cognitive performance in such subjects may vary from task to task, and from subject to subject, within a particular period of time, for example, when performing a task. Some of the described embodiments disclose techniques for controlling the delivery of electrical stimulation to a subject and adjusting parameters of the electrical stimulation based on measured brain activity of the subject, which may be improved in response to the electrical stimulation.

In some embodiments, a system for controlling the delivery of electrical stimulation to the brain of a subject and monitoring or detecting a cerebral hemodynamic response associated with neural activity is provided. In some embodiments, a system for detecting a cerebral hemodynamic response associated with neural activity is provided. The system is further configured to determine an activity metric based on the brain hemodynamic response, and adjust one or more parameters of the stimulus based on the activity metric. Thus, in some embodiments, the system provides a "closed loop" monitoring of brain activity that allows for confirmation that the applied stimulus actually did reach the subject's brain and determines whether it is producing the desired effect. Action may also be allowed in the event that current is not passing or in the event that the current passing is insufficient to produce the desired effect. For example, the electrodes used to deliver the stimulation may be adjusted and/or the stimulation parameters changed to improve the application of the stimulation and the results achieved. This may improve patient outcome.

The system or at least a part of the system comprises a head-mounted or head-mountable device, such as a headset, carrying electrodes for delivering stimulation and/or sensors for monitoring brain activity. When wearing a headset to target a particular region of the brain, a subject may be required to perform a particular task or cognitive assessment. In some embodiments, a cognitive performance monitoring application deployed on a computing device (such as a smart phone) may be configured to cooperate with a system to coordinate tasks to be performed with operation of the system.

In some embodiments, the system or the apparatus of the system comprises a control device arranged for detecting or monitoring activity in the target zone via a plurality of optical sensors or optodes, such as functional near infrared spectroscopy sensors (fNIRS), positioned in a critical location on the head of the subject. For example, the head-mounted device may be configured to properly position the sensor relative to the subject's head. Performing a particular task by the subject will result in an activity in a particular portion of the subject's brain, and the activity is detected and monitored by the control device via the sensor.

The control device causes transcranial electrical stimulation (via an electrical stimulation generator) to be delivered to a plurality of electrodes placed at or near a particular location on the subject's head, such as the frontal area (forehead) of the head, in order to stimulate a particular region of the brain to treat or have a beneficial effect on a symptom of a neurobehavioral disorder such as ADHD. In specific areas of the brainIncreasing the likelihood of neuronal firing (Chhatbar et al, 2018;et al, 2018; islam, aftabuddin, moriwaki, hattori and Hori,1995; polania, nitsche and Paoulus, 2010). Repeated neuronal firing in this manner has been found to increase the strength of neuronal connections via long-range potentiation and up-regulation of brain-derived neurotrophic factors (Liebetanz, nitsche, tergau and Paul us,2002; cocco et al 2020; cavaleiro, martins, And Castelo-branch, 2020). This is consistent with the learning of Hebbian and neuroplasticity theory, which states that cells that discharge together will link together (Hebb, 1949; shatz, 1992).

Neuronal excitability of the subject changes in response to application of the electrical stimulus, and changes in cortical activity may be detected by the control device via the sensor. The system is configured to analyze measurement data (brain hemodynamic response data) from optical sensors, which may be positioned near the electrodes, based on or containing optical signals from the respective sensors. For example, the optical signal may be indicative of the intensity of reflected light at two or more different wavelengths, and the measurement data may comprise the optical signal and/or a curve indicative of a change in concentration of oxygenated hemoglobin (HbO), deoxygenated hemoglobin (HbR), and/or a combination of both (total hemoglobin ThB). Based on the analysis, the system determines an activity metric indicative of activity of the brain region being targeted that may have been affected by the stimulus, determines a value of a stimulation parameter based on the activity metric, and transmits stimulation instructions containing the stimulation parameter to an electrical stimulation generator to modify or regulate the stimulus being or to be delivered to the subject via the electrode.

In some embodiments, the control device of the system is configured to analyze the measurement data. The control device may also be configured to determine an activity metric indicative of activity of the brain region being targeted, determine a value of a stimulation parameter based on the activity metric, and transmit stimulation instructions containing the stimulation parameter to the electrical stimulation generator to modify or adjust the stimulation being or to be delivered to the subject via the electrodes.

In some embodiments, the control device may be configured to provide or stream measurement data to a computing device or server, for example, via a wireless communication network such as bluetooth. The computing device or server may be configured to determine an activity metric indicative of activity of the brain region being targeted, and determine a value of the stimulation parameter based on the activity metric. The computing device or server may transmit stimulation instructions containing stimulation parameters to the control device to cause the electrical stimulation generator to modify or regulate the stimulation being or to be delivered to the subject via the electrodes.

The activity metric may be derived from a feature or characteristic extracted from the optical signal received from the sensor, and it may be indicative of a biomarker associated with a change in cortical activity. For example, when a subject begins to assume a task and/or is subjected to delivered electrical stimulation, the resulting neural activity leads to physiological changes in the local network of blood vessels in the subject's brain, which may result in changes in Cerebral Blood Volume (CBV), rate of cerebral blood flow, and concentrations of oxyhemoglobin and deoxyhemoglobin per unit of brain tissue. The detected optical signals are indicative of these activity-related cerebral hemodynamic responses.

In some embodiments, a system (e.g., a control device, computing device, or server) may employ a univariate or multivariate activity determination model configured to receive as input features extracted from sensor signals and provide as output an activity metric. For example, the activity metric may indicate whether sufficient stimulus has been delivered to the subject, or a confidence score associated with whether sufficient stimulus has been delivered to the subject, to achieve a desired level of activity at the target zone. The activity metric may indicate whether the user's brain activity is considered to be sufficiently active, underactive, overactive, overresponsive, sufficiently responsive, or insufficient responsive.

In such embodiments, the system may determine the stimulation parameter or a change to the stimulation parameter based on the activity metric. In some embodiments, the system may employ a univariate or multivariate stimulus control determination model configured to receive the activity metric, and in some embodiments, to receive as input a previously applied stimulus parameter, and to provide as output an updated stimulus parameter value. The stimulation parameter may be merely an on/off parameter value or may comprise values of parameters such as frequency, duration, amplitude, etc. The control device transmits stimulation instructions containing the stimulation parameters to the electrical stimulation generator to adjust the stimulation delivered to the patient, for example, to interrupt the stimulation, or by adjusting characteristics of the stimulation. In some embodiments, the computing device or server may transmit the stimulation instructions to the control device.

In some embodiments, the activity determination model and/or the stimulation control determination model may be trained on subject-derived data such that the control device is configured to provide customized therapy to a particular subject. By customizing the model for the user, the model tends to be more accurate, resulting in an improved control device. In some embodiments, a global model for the activity determination model and/or the stimulation control determination model may be trained on a global dataset containing instances from a plurality of subjects. The global dataset may be filtered to include instances derived from subjects having some or more factors, such as age and gender, that are the same as the candidate subjects. The trained global model may then be refined based on data associated with or collected from the candidate subjects to provide a customized user-specific model. By training the model on a global dataset and refining the trained global model based on user-specific data, the resulting customized user-specific model can be determined based on a relatively small candidate subject dataset.

In some embodiments, the subject performs tasks or activities to activate specific areas of the brain to target stimulation effects to areas of the brain activated by these tasks. Such tasks may focus on behaviors or symptoms such as working memory, attention, and/or impulse control. Scores associated with those tasks (task data), as well as sensor data recorded before, during, and after execution of the tasks, may be analyzed by the system to determine symptom severity or progression metrics. In some embodiments, the system may be configured to determine severity scores for various symptoms, behaviors, and/or experiences of the neurobehavioral disorder.

The symptom severity or progression metric may be derived from a feature or characteristic extracted from the optical signal received from the sensor, and it may be indicative of a biomarker associated with a change in cortical activity. For example, when a subject begins to assume a task, the resulting neural activity leads to physiological changes in the local network of blood vessels in the subject's brain, which may lead to changes in Cerebral Blood Volume (CBV), the rate of cerebral blood flow, and the concentrations of oxyhemoglobin and deoxyhemoglobin per unit of brain tissue. The detected optical signals are indicative of these activity-related cerebral hemodynamic responses.

In some embodiments, the system (e.g., a control device, computing device, or server) may employ a univariate or multivariate symptom severity or progress determination model configured to receive as input feature and task data extracted from sensor signals and provide as output a symptom severity or progress metric. The measure of progression may be indicative of symptom severity relative to a previously predicted symptom severity, and thus indicative of the progression a subject has in treating the symptoms of the neurological disorder. For example, where the symptom severity or progress determination model 327 is configured to determine the symptom severity or progress of a characteristic or behavior associated with ADHD, the symptom severity or progress determination model 327 may provide as output values for one or more of: overall ADHD rating scale score, ADHD core symptom score, inattention score, hyperactivity score, and impulsivity score.

In some embodiments, information associated with a session for treating or monitoring symptoms of a neurobehavioral disorder assumed by a subject may be transmitted to a cognitive performance monitoring or assessment application or other application deployed on a computing device of the subject, such as a notification related to the process, or a metric of its physical condition and/or performance during the session.

The system may be used by a user on an ad hoc basis, for example, to help stimulate a particular region or area of the brain while performing a task, and thus may improve the user's immediate performance while performing a task. Thus, the system may provide short-term benefits to the user. For example, children may choose to use the system in doing their home jobs. In some embodiments, the system may be used to help monitor a particular region or area of the brain while performing a task, and thus may provide information about the user's neural activity at the particular region or area of the brain while performing the task. The system may be used on a regular basis, for example as part of a treatment plan, and thus may improve long-term cognitive performance of a user by increasing the strength of neuronal connections via long-range potentiation effects and upregulation of brain-derived neurotrophic factors.

Fig. 1 depicts an embodiment of a device 100 for monitoring and/or treating symptoms of a neurobehavioral disorder, such as ADHD. In some embodiments, the symptoms are targeted to working memory, attention, and/or impulse control.

In some embodiments, and as shown, the device 100 may include a mount 125 configured to be worn on the head by a subject or user 115. For example, the mount 125 may be a head-mounted strap or cap. Another example of an embodiment of the mount 125 of the apparatus 100 is depicted in fig. 4, 5 and 6, as discussed in more detail below. The device 100 may be a portable or wearable headset.

The device 100 may include one or more electrodes 120 configured to be attached near or on the head of the user 115 in a target area or region of interest. In some embodiments, and as shown, the electrodes 120 may be arranged in an array relative to one another, and/or may be carried by the mount 125. The electrodes may be configured to receive electrical stimulation from an electrical stimulation generator or source 350 (fig. 3) under the control of the control device 110 and deliver transcranial electrical stimulation or transcranial neural stimulation to a target area of the brain of the user 115. For example, the electrodes 120 may be configured to deliver supply currents such as transcranial direct current stimulation (tDCS), transcranial alternating current stimulation (tcacs), and/or transcranial random noise stimulation (tRNS) to the target region. Electrode 120 may comprise a plurality of individually attachable electrodes, or more than one electrode in an array configuration. Applying electrical stimulation to the brain affects brain activity, and thus, when the device 100 is worn by the user 115, the configuration of the electrodes 120 relative to each other and relative to the head of the user 115 and the location or position of the brain allows specific areas of the brain to be targeted by stimulation.

The apparatus 100 further comprises one or more optical sensors 130. The optical sensor 130 may comprise a functional near infrared spectrum sensor (fNIRS) configured to emit near infrared light (typically having a wavelength of 650 to 1000 nm) and measure brain hemodynamic responses associated with brain activity. Specifically, the optical sensor 130 is configured to emit light of two or more different wavelengths. To this end, each optical sensor 130 includes a light emitter 130A and a corresponding light detector 130B (fig. 7a and 7B). As shown in fig. 7a and 7B, the pair of light emitters 130A and light detectors 130B are arranged or placed on the mount 125 on the same side and/or are otherwise placed on the head of the user, so the recorded measurements are due to backscattered (reflected) light following an elliptical path. The reflected light detected by the optical sensor 130 indicates the concentration changes of oxyhemoglobin (HbO) and deoxyhemoglobin (HbR), and these sensed signals (or measurement information derived from the recorded data) are provided to the control device 110 for analysis. Functional near infrared spectroscopic sensors (fNIRS) indirectly measure neuronal activity in the cerebral cortex via neuro-vascular coupling by quantifying hemoglobin concentration changes in the brain based on optical intensity measurements, such as in Noman naser and Keum-ShikHong, fNIRS-based brain-computer interface: a review (fNIRS-based brain-computer interfaces: a review) described in 2015, human neuroscience front Frontiers in Human Neuroscience, the entire contents of which are incorporated herein by reference.

The optical sensor 130 may be configured to record changes in blood oxygenation in a particular region of the brain of the user 115, depending on the placement of the sensor relative to the brain of the user. Blood oxygenation changes reflect changes in brain activity, as it represents the energy demand of that region of the brain, whether increased due to increased neuronal activity or decreased due to decreased neuronal activity, as indicated by oxygen metabolism. This may be achieved by analyzing the raw signal based on light reflection and absorption, and/or converting the raw signal into a hemodynamic response.

When device 100 is applied to the head of a subject, optical sensor 130 is configured to measure brain activity at a location or position in the middle of the subject's brain, or at a midpoint between a pair of light emitters 130A and light detectors 130B. Typically, the optimal or maximum electrical stimulation delivered to the brain of the subject via each electrode 120 is at a point directly below the respective electrode 120. Thus, the optical sensor 130 is configured to measure brain activity around and between the stimulation electrodes. By configuring the placement of the optical sensor 130, i.e., the pair of light emitters 130A and light detectors 130B, relative to the respective electrode 120, a particular site or portion of the subject's brain can be targeted with the associated (or responsive) brain activity stimulated and measured. Examples of the arrangement of the optical sensor 130 and the electrode 120 are described in more detail below with reference to fig. 7a, 7b and 12.

When the array 700 of the device 100 is placed on the head of the user, the pair of light detectors D1, D2 and the light emitter S1 are directed towards a portion of the left anterior temporal area of the brain of the user; the pair of photodetectors D3, D4 and D5, D6 and the pair of light emitters S2 and S3 are directed toward the left prefrontal cortex of the brain of the user; the pair of photodetectors D7, D8 and D9, D10, and the pair of light emitters S4 and S5 are directed toward the inner prefrontal cortex of the brain of the user; the pair of photodetectors D11, D12 and D13, D14 and the pair of light emitters S6 and S7 are directed toward the right prefrontal cortex of the brain of the user; and the photodetector pairs D15, D16 and the light emitter S8 are directed towards the right anterior temporal region of the user' S brain.

The use of the fnrs sensor as the optical sensor 130 may allow for relatively low cost, portability, safety, accuracy, and/or ease of use as compared to other sensors. In particular, the fNIRS sensor is less sensitive to movement artifacts than other blood oxygenation sensors, such as Magnetic Resonance Imaging (MRI). The fNIRS sensor is also less sensitive to electrical noise and movement artifacts than other electrical sensors, such as electroencephalograms (EEG). Thus, using an fnigs sensor as the optical sensor 130 allows the sensor 130 to be placed relatively close to the electrostimulation site of the electrode 120 and thereby provide improved readings. Furthermore, the fNIRS sensor has a relatively high spatial resolution compared to EEG and exhibits a high signal quality in the described embodiments. And furthermore, the fNIRS sensor allows for measuring and recording activity data while delivering stimulation, which is difficult for an EEG to perform accurately and effectively. This is because the neural signal is very small in amplitude and can be masked by other signals and noise. The stimulus generator delivers the current picked up by the EEG and the amplitude of the stimulus signal is many orders of magnitude greater than the nerve signal. If an attempt is made to record an EEG while the electrical stimulus is applied, a large portion of the EEG signal will be produced by the electrical stimulus and the neural response may be difficult to detect. Furthermore, the stimulus may saturate the EEG sensor, making it impossible to see any neural signals. As a result, it is difficult to record data using EEG while applying stimulus, is prone to failure due to the relatively high probability of saturation of the sensor, and involves complex signal processing in an attempt to recover the neuron signal. Thus, using an fnigs sensor as the optical sensor 130 allows for more efficient determination of improved signal quality with a higher degree of accuracy confidence.

The apparatus 100 further comprises a control device 110. As shown, the control device 110 may be housed in a housing 112. The housing 112 may further contain an optical sensor module 340 and/or an electrical stimulation source 350 (fig. 3). The housing 112 may also contain an electrode (back electrode) 123 that serves as a return electrode pad for the electrode 120. As discussed in more detail below with reference to fig. 3, the control device 110 may be configured to transmit stimulation instructions to the electrical stimulation source 350, which may include stimulation parameter values, to cause the electrical stimulation source 350 to deliver transcranial electrical stimulation to the electrode 120 and to a target region of the brain via the electrode 120. The control device 110 may be configured to receive response data, such as response signals, from the sensor 130 via the optical sensor module 340. In some embodiments, the electrodes 120 may be configured to determine a measure of electrical stimulation delivered to the subject, e.g., a measure of brain activity of the subject. For example, in such embodiments, the electrodes 120 may be configured to determine or receive an electroencephalogram (EEG) signal.

As shown, the control device 110 and/or the housing 112 may be mounted at the rear of the mount 125 relative to the sensor 130 or at the front of the mount 125 relative to the sensor 130. In some embodiments, the control device 110 may be formed as part of the mount 125. For example, when the device 100 is mounted to the head of a subject, the control apparatus 110 and/or the housing 112 may be arranged to be positioned near or at the forehead of the subject, or near or at the back of the head of the subject.

Fig. 2 depicts a block diagram of a system architecture 200 for controlling delivery of transcranial electrical stimulation to a subject and/or monitoring neural activity of the subject, in accordance with some embodiments. The control device 110 may communicate with a server 230, one or more computing devices 220, and/or a database 240 via a communication network 210.

Network 210 may comprise at least a portion of one or more networks having one or more nodes that transmit, receive, forward, generate, buffer, store, route, exchange, process, or some combination thereof, etc., one or more messages, packets, signals, some combination thereof, etc. Network 210 may include, for example, one or more of the following: wireless networks, wired networks, the internet, intranets, public networks, packet-switched networks, circuit-switched networks, self-organizing networks, infrastructure networks, public Switched Telephone Networks (PSTN), cable networks, cellular networks, satellite networks, fiber optic networks, some combination thereof, and the like.

Server 230 may include one or more processors or computing devices configured to share data or resources among a plurality of network devices. Server 230 may comprise a physical server, a virtual server, or a combination of one or more physical or virtual servers.

Database 240 may include a data store configured to store data from network devices over network 210. Database 240 may comprise a virtual data store in memory of a computing device connected to network 210 by server 230 or directly to network 210.

The electrical stimulation source 350 is configured to receive instructions from the control device 110 to provide electrical stimulation to the one or more electrodes 120 in response to the instructions. In some embodiments, electrical stimulation source 350 may provide information or live monitoring feedback of the electrical stimulation applied to control device 110 to allow control device 110 to monitor and/or control the characteristics of the electrical stimulation provided or supplied to electrodes 120. For example, the control device 110 may be configured to monitor the performance of the electrical stimulation source 350 to ensure that it is operating on command and within acceptable safety limits. The electrical stimulation source 350 may also be configured to modify stimulation parameters or characteristics of the applied electrical stimulation based on instructions received from the control device 110.

The electrical stimulation source 350 may be configured to supply electrical current, such as transcranial direct current stimulation (tDCS), transcranial alternating current stimulation (tcacs), and/or transcranial random noise stimulation (tRNS), to the user 115 through the one or more electrodes 120. In some embodiments, a combination of two or more stimulus types may be used, for example, where the current is positive, but also alternating. This may provide the beneficial effects of both tDCS and tcacs. A graphical depiction of the combined current versus time containing tDCS and tcacs is depicted in fig. 9A. In some embodiments, electrical stimulation source 350 may be configured to provide stimulation in a frequency range between 0.1 and 10 khz. Amplitude peak-to-peak amplitude may be in the range of 0.5 to 4 mA.

Fig. 9B is a graphical depiction of current versus time including transcranial direct current stimulation (tDCS), transcranial alternating current stimulation (tcacs), transcranial pulsed current stimulation (pcs), transcranial random noise stimulation (tRNS), and oscillating tDCS (otDCS). In some embodiments, the otDCS signal may be generated by a combination of tDCS, tACS, and tcss. In some embodiments, any combination of the depicted signal types may be used to provide transcranial stimulation (such as the combination of tDCS and tcacs seen in fig. 9A). In some embodiments, a combination of tDCS, tACS, and/or tRNS is used to provide transcranial stimulation, or a combination of otDCS and tRNS. The electrical stimulation source 350 may be used to provide stimulation through the electrode 120 corresponding to any of the signal types depicted in fig. 9A and 9B. The type of stimulus applied may be selected by the user using a computing device 220 that instructs the control device 110 via the network 210.

The control device 110 may be configured to receive signals or measurements from the optical sensor module 340 indicative of neuronal activity. The optical sensor module 340 is configured to be connected to the optical sensor 130. The optical sensor module 340 may include an fNIRS recording module. In response to receiving instructions from control device 110, optical sensor module 340 is configured to cause light to be emitted from light emitter 130A and to receive signals from detector 130B indicative of detected or measured reflected light. For example, the optical sensor module 340 may modulate instructions or signals received from the control device 110 and provide a composite signal (instructions or input signals imposed on a carrier wave) to the emitter 130A to cause the emitter to emit light having particular characteristics. In some embodiments, the reflected light signal detected by detector 130B is demodulated by optical sensor module 340 and the recorded data or measurements are provided to control device 110. In other embodiments, the control device 110 may demodulate the detected reflected light signal. In some embodiments, the optical sensor module 340 may provide transmitted and detected information or live monitoring feedback to the control device 110 to allow the control device 110 to monitor and/or control the operation of the optical sensor module 340 and the sensor 130.

In some embodiments, the optical sensor module 340 may be configured to generate a lock-in amplifier effect to improve the signal-to-noise ratio (SNR) of the detected reflected light signal. For example, the optical sensor module 340 may be configured to modulate instructions or signals received from the control device 110 and provide a composite signal (instructions or input signals imposed on a carrier wave) to the transmitter 130A to cause the transmitter to emit light having particular characteristics. For example, the optical sensor modules 340 may be configured to turn on and off the emitters 130A of the respective optical sensor modules 340 at a relatively high frequency-blinking frequency. In other words, the transmitter 130A modulates at a flicker frequency. For example, the flicker frequency may be greater than or equal to 100hz, such as 125hz. As a result, the data or signal detected by the corresponding detector 130B is moved to a frequency near the flicker frequency. Since in analog signal measurements there is typically more noise at lower frequencies than at higher frequencies, by moving the response signal to a relatively higher frequency, the SNR of the response signal can be increased.

The control means is arranged to determine from the response signal detected by the detector 130B that the sampling rate of the sensor data has to be at least twice the highest frequency component of the detected response signal (Nyquist's theshem). However, the higher the sampling rate, the more difficult it is to obtain reliable samples, the more samples are obtained, and/or the less time the processor 310 of the control device 110 must perform other processing tasks. Thus, in some embodiments, a sampling rate of 4 times the highest frequency component of the transmitter signal 130A is selected. For example, the flicker frequency may be 125hz, and the sampling rate may be 500hz.

In some embodiments, the control device 110 may be a wireless device configured to communicate with a computing device and/or server, for example, wirelessly. In some embodiments, the control device 110 may enable bluetooth.

As described above, sampling at a relatively high frequency results in the acquisition of a correspondingly relatively high number of data samples. For example, consider an embodiment in which the device 100 carries eight optical sensors 130 (or optical sensor components 130), each providing a short channel and a long channel): (500 samples/sec) × (3 bytes/channel/sec) × (16 detector channels (i.e. detector 130B))=24 kbytes/sec. Such data rates would be too high to use the low energy bluetooth protocol BLE 5.0 (typically up to about 5 kilobytes/second may be transmitted over BLE 5.0). Thus, downsampling and/or demodulation may be required to wirelessly transmit the acquired data to a computing device, server, or other computer at that rate using some wireless technology, such as low energy bluetooth. In some embodiments, the data rate may be reduced by demodulating the detected response signal. For example, 16 detector channels may be divided into more functional channels, such as 44 functional channels. In some embodiments, 36 long channels and 8 short channels are used. For example, the apparatus 100 of fig. 7 may be used with the center electrode removed or omitted. The arrangement of the long and short channels is discussed in more detail below with reference to fig. 12. The data may also be downsampled to a sampling rate of 10 Hz. In this example, the new data rate is almost 10% of the original rate, while still retaining useful information: (10 samples/sec) × (6 bytes/channel/sec) × (44 channels) =2.64 channels/sec/. Thus, sensor data can be acquired with relatively high accuracy (e.g., 3 bytes per sample).

The device 100 may contain a plurality of functional channels, such as 44 functional channels, each of which contains a pair of data channels. The functional channel may comprise an emitter and detector pair. The depth of measurement to the head that can be achieved by an emitter-detector pair depends on the distance between the emitter and detector pairs. In some embodiments, the functional channels may comprise long channels or short channels. The long channels may comprise pairs of functional channels, wherein the source and detector are spaced apart from each other by a relatively large distance. For example, for long channels, the emitter and detector pairs may be spaced apart from each other by about 3cm. The short channel may comprise a pair of functional channels, wherein the emitter and detector are spaced apart from each other by a relatively small distance, such as by about 1cm. The long and short functional channels may be separated to make different measurements. Due to the short distance between the source and the detector, a short channel can be used to measure blood oxygenation in the scalp. The long channel may be used to measure blood oxygenation from the scalp and brain of a subject. In some embodiments, measuring blood oxygenation from the scalp may be an undesirable effect. In such embodiments, short channels may be used to determine and allow for the subtraction of unwanted effects from measurements determined by the corresponding long channels during data analysis. In some embodiments, the 44 functional channels may include 8 short functional channels and 36 long functional channels.

In some embodiments, it may be preferable to perform any evaluation and processing of the acquired sensor data on a computing device or server other than the control device 110. For example, the processor 310 of the control device 110 may not be powerful enough to perform additional data processing while performing other tasks. The control device 110 may use low power components and may not require long use of large batteries. The recorded sensor data may be sent to an external device for additional processing and local/cloud storage. The control device may be a wireless headset for recording sensor data and the evaluation and processing of the acquired sensor data may be performed on the computing device 220 or the server 230.

In some embodiments, the control device 110 is configured to cooperate with the electrical stimulation source 350 and the optical sensor module 340 to provide closed loop stimulation and/or monitoring of brain activity of the subject. Closed loop monitoring allows specific symptoms, such as neurobehavioral disorders, to be notified of the application of stimulus to treat a neurological condition.

In some embodiments, the control device 110 is configured to command the electrical stimulation source 350 to provide electrical stimulation to the electrodes 120 in the form of short pulses. Each short pulse signal may be characterized by an amplitude, frequency, duration, and offset. In some embodiments, each short pulse is delivered one at a time. After each pulse is delivered to the electrode, the optical sensor module 340 is configured to record brain activity via the sensor 130 and provide the record or measurement to the control device 110 for evaluation. As discussed in more detail below with reference to fig. 3, the control device 110 determines an activity metric based on information received from the optical sensor module 340. For example, in some embodiments, the activity metric may be an indication that the user's brain activity is determined to be sufficiently active, underactive, overactive, overresponsive, sufficiently responsive, or not responsive. Depending on the activity of the brain and the purpose of the stimulation, the control device 110 may command the electrical stimulation source 350 and deliver more pulses with the same or different characteristics.

In some embodiments, the control device 110 is configured to command the electrical stimulation source 350 to provide electrical stimulation to the electrodes 120 in a single relatively long session. The electrical stimulation signal may be characterized by amplitude, frequency, and offset. The optical sensor module 340 is configured to record brain activity via the sensor 130 during application of stimulation to the electrodes, and provide the recording or measurement to the control device 110 for real-time assessment (i.e., simultaneously or while the brain is stimulated). The control device 110 determines an activity metric based on information received from the optical sensor module 340. For example, in some embodiments, the activity metric may be an indication that the user's brain activity is determined to be sufficiently active, underactive, overactive, overresponsive, sufficiently responsive, or not responsive. Depending on the activity of the brain and the purpose of the stimulation, the control device 110 may command the electrical stimulation source 350 and adjust the stimulation parameters (i.e., characteristics of the signals) of the electrical stimulation being delivered. For example, the control device 110 may cause the electrical stimulation source 350 to cease providing electrical stimulation to the electrode 120, or to adjust one or more of the characteristics of the signal provided to the electrode 120.

The control device 110 may be configured to transmit data received from the electrical stimulation source 350 or the optical sensor module 340 or data generated by the control device 110 itself to the server 230 for further processing or to the database 240 for storage. The control means 110 may also be arranged to receive instructions or data from the server 230. For example, the server 230 may be configured to transmit configuration instructions or updates to the control device 110 to modify the manner in which the control device 110 operates.

The control device 110 may also send information to and receive information from the computing device 220. Computing device 220 may include a computer, a smart phone device, a laptop computer, a tablet computer, or other suitable device. The computing device 220 may include one or more processors 222 and a memory 224 storing instructions (e.g., program code) that, when executed by the processor 222, cause the computing device 220 to cooperate with the control device 110 and perform processes in accordance with the described methods. The computing device 220 may be a computing device associated with a user or may be, for example, a computing device of a clinician or other clinician of the user.

Computing device 220 includes a network interface 226 to facilitate communications with components of communications network 210. The computer device 220 may also contain a user interface 228 to allow a user to interact with the cognitive performance monitoring application 225 and other applications or functions provided by the computing device 220.

Memory 224 contains a cognitive performance monitoring or cognitive assessment application 225. In some embodiments, the cognitive performance monitoring application 225, when executed by the processor 222, enables the computing device 220 to cooperate with the control system 110 to monitor the cognitive performance of the subject as the subject undergoes treatment using the device 100 or the subject is monitored using the device 100, and in some embodiments, to control the operation of the control device 110. The cognitive performance monitoring application 225 may be downloaded or otherwise deployed on the subject's computing device 220.

In some embodiments, the cognitive performance monitoring application 225 may be arranged to receive and store data regarding user progress from the control device 110, which may be displayed to the user and/or provided to the server 230 or another computing device 220. In some embodiments, the cognitive performance monitoring application 225 may be configured to receive and track behavioral data, such as sleep data, mental activities, exercise, diet, and/or other information that may have an impact on the cognitive performance of an individual. For example, a user may input such information via the user interface 228, or the cognitive performance monitoring application 225 may be configured to cooperate with other applications running on the computing device 220, such as a pedometer, or on other user devices, such as a smartwatch. The authorized clinician may be provided with access to a cognitive performance monitoring application 225 deployed on the user's computing device 220, or to files associated with the user stored in database 240 or server 230.

In some embodiments, cognitive performance monitoring application 225 may contain one or more games, tasks, activities, or applications that a user may execute to target stimulation effects to areas of the brain activated by those tasks and monitor the resulting stimulation effects using device 100 when the user undertakes treatment to activate specific areas of the brain using device 100. In some embodiments, the tasks of the cognitive performance monitoring application 225 may be performed by a user to activate specific areas of the brain (without delivering electrical stimulation to the brain) to monitor or measure the resulting neural activity using the device 100. For example, such paired activities may focus on tasks involving working memory, attention, and/or impulse control. In some embodiments, application 225 further includes a series of task-based activity and/or psychometric tests that are independent of or completed during the electrical stimulation session. This may provide the benefit of a normalized baseline active set, allowing for a more consistent analysis of the brain activity of the user 115, and may provide a consistent baseline on which the model of the control device 110 or the computing device 220 may be trained. The cognitive performance monitoring application 225 may also provide feedback to the clinician and/or user regarding the user's performance over time, which may be used to determine treatment options, plans, and/or operating parameters of the control device 110. Having users track their performance and desirably improve over time can motivate and encourage them to participate and adhere to the treatment.

The cognitive performance monitoring application 225 may receive input from a user via a user interface 228. The input may relate to instructions for performing paired tasks. The input may relate to instructions for controlling the operation of the device 110, including sending instructions to and receiving instructions from the control device 110. The computing device 220 may be further configured to display information about the apparatus 100 to a user, including data associated with the one or more electrodes 120 or the one or more optical sensors 130, data from the control device 110, or data from the server 230 and database 240.

In some embodiments, the cognitive performance monitoring application 225 may transmit instructions to activate or activate the control device 110 and begin delivering stimulation to the electrodes and/or monitoring the effect of the stimulation on the area being targeted in response to user input, e.g., via the user interface 228. Similarly, the cognitive performance monitoring application 225 may pause or deactivate the recording of the electrical stimulation session and/or sensor data by sending instructions to the control device 110. In other words, the cognitive performance monitoring application 225 may be used to control the operation of the control means 110 of the device 100. In some embodiments, the cognitive performance monitoring application 225 may be configured to transmit task data to the control device 110 or the server 230. In some embodiments, the control device 110 or the server 230 may receive the task data from the control device 110. In some embodiments, the control device 110, the server 230, and/or the computing device 220 may receive supplemental task data from elsewhere, for example, via user input using the user interface 360. The task data may indicate the type of task performed by the user during a session or while undergoing treatment. The control device 110, the computing device 220, or the server 230 may use the task data to determine one or more stimulation parameter values, and/or to determine an activity metric. For example, the desired stimulus and threshold values and/or characteristics for determining the activity metric and/or stimulus parameter value may vary from task to task. In some embodiments, the task data may include one or more scores achieved by the user in performing the task, and which may be used by the control device 110 in conjunction with the measurement data to infer behavioral progression of the subject. For example, the score may indicate an accuracy and/or reaction time associated with performing the task. In other embodiments, the recorded data and/or activity metrics and task data including scores may be sent to a server 230 (e.g., a remote server) for processing to infer behavioral progression of the subject.

Fig. 3 depicts a schematic diagram of a system 300 for controlling transcranial electrical stimulation delivery and/or monitoring brain activity, according to some embodiments. In this figure, the functional components of the control device 110 of fig. 2 are depicted in more detail. However, it should be understood that in other embodiments, one or more of the functional components of the control device 110 may be disposed on other devices or systems, such as the computing device 220 and/or the server 230.

The control device 110 may be housed within a housing 112. The housing 112 may further contain an optical sensor module 340 and/or an electrical stimulation source 350. In some embodiments, the electrical stimulation source 350 and/or the optical sensor module 340 may be external to the housing 112. The housing 112 may further include a human-machine interface (HMI) 355 configured to manually switch on or off electrical stimulation and/or optical sensing supplied to the user 115. In some embodiments, the HMI 355 may be configured to switch the system 300 between an active state, a passive state, and a powered off state.

The control device 110 includes one or more processors 310 and a memory 320 storing instructions (e.g., program code) that, when executed by the processors 310, cause the control device 110 to operate according to the described methods. Processor 310 may include one or more microprocessors, central Processing Units (CPUs), graphics/Graphics Processing Units (GPUs), application specific instruction set processors (ASIPs), application Specific Integrated Circuits (ASICs), field Programmable Gate Arrays (FPGAs), or other processors capable of reading and executing instruction code. The processor 310 may include additional processing circuitry. For example, the processor 310 may include a plurality of processing chips, a Digital Signal Processor (DSP), analog to digital or digital to analog conversion circuitry, or other circuitry or processing chips having processing capabilities to perform the functions described herein. Processor 310 may perform all of the processing functions described herein locally on control device 110, or may perform some of the processing functions locally and outsource other processing functions to another processing system, such as server 230 or computing device 220.

Memory 320 may include one or more volatile or nonvolatile memory types. For example, memory 320 may comprise one or more of Random Access Memory (RAM), read Only Memory (ROM), electrically Erasable Programmable Read Only Memory (EEPROM), or flash memory. Memory 320 is configured to store program codes that are accessible by processor 310. The program code includes executable program code modules. In other words, memory 320 is configured to store executable code modules configured to be executable by processor 310. The executable code modules, when executed by the processor 310, cause the control device 110 to perform certain functions, as described in more detail below.

Memory 320 may include a feature extraction module 322, an analysis engine 324, and/or a stimulation control module 326.

The feature extraction module 322 contains executable program code that, when executed by the processor 310, causes the control device 110 to identify or extract characteristics or features of signals or data recorded by and received from the optical sensor module 340. The signals measured or calculated by the optical sensor module 340 or the control device 110 may include Hbo and Hbr changes, hbo and Hbr curves, and combinations thereof, including total hemoglobin ThB (thb= Hbo +hbr).

The characteristic may be a characteristic or indication of a biomarker associated with cognitive function or performance or cortical activity. In some embodiments, the feature extraction module 322 is configured to determine one or more of:

peak amplitude;

peak width;

multiple peaks, e.g., a bimodal signal;

slope of a portion of the signal;

regression Moving Average (ARMA) coefficients;

rise time of amplitude or slope;

baseline activity;

baseline trend;

a plurality of zero crossings;

hemoglobin values, such as HbO and HbR correlations;

peak rise time;

ratio of HbO to HbR of amplitude, width, slope or other characteristics;

changes in total blood volume;

signal morphology; and

area under the curve.

In other embodiments, other signal features or characteristics may be extracted. The feature extraction module 322 provides the extracted features as input to the analysis engine 324. In some embodiments, feature extraction module 322 may be deployed on server 230 and/or computing device 220.

In some embodiments, the features contain or indicate functional connectivity between channel pairs of the device 100-i.e., statistical correlation and/or similarity between pairs of data from adjacent or different regions of the brain. In some embodiments, the features include or indicate statistics applied to data derived from one or more channels.

For example, the features used by the activity determination model 328 to determine the activity metric may be extracted from sensor data obtained from the left prefrontal cortex, the medial prefrontal cortex, and/or the boundary between the medial prefrontal cortex and the left prefrontal cortex of the subject. The features of the symptom severity or progression determination model 327 used to determine the overall ADHD symptom severity measure may be extracted from sensor data obtained from the subject's right prefrontal cortex. The reaction time and omission error metrics from the task data may also be used to determine the severity of symptoms or input features of the progress determination model 327 to determine an overall ADHD symptom severity metric.

The features of the symptom severity or progress determination model 327 used to determine the primary ADHD core symptom score may be extracted from sensor data obtained from the subject's right prefrontal cortex.

The features of the symptom severity or progression determination model 327 used to determine the inattention score may be extracted from sensor data obtained from the subject's medial prefrontal cortex and/or the boundary region of the medial prefrontal cortex and the left prefrontal cortex. The reaction time and omission error metrics from the task data may also be used to determine the severity of symptoms or the input features of the progress determination model 327 to determine the inattention severity metric.

The features used by symptom severity or progression determination model 327 to determine the hyperactivity score may be extracted from sensor data obtained from the subject's right prefrontal cortex, left prefrontal cortex, and/or areas overlapping the left lateral prefrontal cortex and medial prefrontal cortex.

The features of the symptom severity or progression determination model 327 used to determine the impulse severity measure may be extracted from sensor data obtained from the medial prefrontal cortex of the subject and/or the medial prefrontal cortex toward, overlapping with, or in proximity to the right prefrontal cortex of the subject. Characteristic values of the response time metrics based on task data may be provided to the symptom severity and/or progress determination model to determine an impulse severity metric.

The analysis engine 324 may include executable program code that, when executed by the control device 110, is configured to determine an activity metric indicative of measured brain activity of the user or subject at the target region based on the brain hemodynamic response measured by the optical sensor module 340. The analysis engine 324 may include executable program code that, when executed by the control device 110, is configured to determine a symptom severity metric indicative of symptoms of the neurological disorder or a progress metric indicative of the progress the subject is taking in treating the symptoms of the neurological disorder based on the brain activity measured at the target area based on the brain hemodynamic response measured by the optical sensor module 340 and task data, which may be collected by the cognitive performance monitoring application 225. In other words, analysis engine 324 infers the performance of the execution function from the recorded fnrs data, and in some embodiments also from the task data.

In some embodiments, features detected from multiple sensors 130 may be analyzed in combination or relative to one another to determine patterns of behavior or activity. For example, if successive sensors or channels show positive amplitudes, but the sensors surrounding those positive sensors show significant negative amplitudes, this may be a biomarker (e.g., described in the "blood theft hypothesis") that indicates that oxygenated blood is moving from one zone to another.

In some embodiments, the analysis engine 324 includes a univariate or multivariate activity determination model 328. The activity determination model 328 may be a machine learning model. The activity determination model 328 may be configured to receive as input features extracted from the sensor signals and provide as output activity metrics. For example, the activity metric may indicate whether a target region of the brain exhibits a sufficient level of activity, and in addition, whether sufficient stimulus has been delivered to the subject to achieve a desired level of activity at the target region. This may involve comparing the activity measure to a threshold. In other embodiments, the activity metric may be a confidence score associated with whether sufficient activity has occurred in the target region. In some embodiments, activity determination model 328 may employ techniques such as general linear model analysis, beta value or regression analysis, logistic regression, linear regression, neural networks, and comparison of activity in one channel with activity in another channel of a signal.

In some embodiments, the analysis engine 324 provides the activity metric as an input to the stimulus control module 326. The stimulus control module 326 may employ a univariate or multivariate model 329. The stimulus control module 326 may be a machine learning model. The stimulation control module 326 may be configured to receive the activity metric as an input and provide a stimulation parameter value as an output. The stimulation parameters may be merely on/off parameter values or may include values of parameters such as frequency, duration, amplitude, etc.

In some embodiments, the analytics engine 324 or the activity determination model 328 of the analytics engine 324 may be deployed on the server 230 and/or the computing device 220. In such embodiments, the server 230 and/or the computing device 220 may be configured to determine an activity metric and provide the activity metric to the control device 110, which may then determine the stimulation parameter value. In some embodiments, the stimulation control module 326 may be disposed on the server 230 and/or the computing device 220, and the server 230 and/or the computing device 220 may be configured to provide stimulation parameter values to the control device 110 to control the stimulation delivered to the subject.

In some embodiments, the control device 110 transmits stimulation instructions containing stimulation parameters to the electrical stimulation source 350 to adjust the stimulation delivered to the patient, for example, to interrupt the stimulation, or by adjusting characteristics of the stimulation signal.

In some embodiments, analysis engine 324 includes a univariate or multivariate symptom severity or progression determination model 327. The symptom severity determination model 327 or the progress determination model 327 may be a machine learning model. The symptom severity or progress determination model 327 may be configured to receive as input one or more characteristics or features extracted from the recorded data or the measured data, and one or more scores associated with respective tasks performed by the subject at the time of recording the data, and provide as output a symptom severity measure or progress measure. For example, the score may indicate an accuracy and/or reaction time associated with performing the task. Multiple sets of data, each containing task-specific sensor data and associated scores, may be used to determine a symptom severity measure or progress measure. For example, the data set may span a particular period of time. In some embodiments, a baseline or initial dataset is determined and progress is assessed against the baseline. In some embodiments, the determined measure of progression is determined relative to a most recently determined measure of progression or symptom severity. In some embodiments, symptom severity or progress determination model 327 may include one or more sub-models configured to infer behavioral progress with respect to a particular task. In other embodiments, symptom severity or progress determination model 327 may be configured to receive as input scores associated with a plurality of respective tests. The symptom severity or progress determination model 327 may be configured to provide symptom severity indications or values associated with one or more symptoms. For example, where the symptom severity or progress determination model 327 is configured to determine the symptom severity or progress of a characteristic or behavior associated with ADHD, the symptom severity or progress determination model 327 may provide as output values for one or more of: overall ADHD rating scale score, ADHD core symptom score, inattention score, hyperactivity score, and impulsivity score.

In some embodiments, symptom severity or determination model 327 may be deployed on server 230, such as a remote server, or computing device 220, such as a smartphone, and server 230 and/or computing device 220 may be configured to determine a symptom severity or progression metric. For example, the server 230 and/or the computing device 220 may be configured to receive or determine task data, including, for example, scoring and logging data or measurement data, from the control device 110 or the feature extraction module 322 and/or the cognitive performance monitoring application 225.

The system 300 includes a network interface or communication module 330 to communicate with components of the system 300, such as the computing device 220, the database 240, and/or other systems or servers 230, over the network 210. The communication module 330 may comprise a combination of network interface hardware and network interface software adapted to establish, maintain, and facilitate communications over the associated communication channels. The communication module 330 may include a wireless ethernet interface, a SIM card module, a bluetooth connection, or other suitable wireless adapter that allows wireless communication over the network 210. For example, in some embodiments, the control device 110 and the computing device 220 are arranged to communicate with each other via bluetooth. In some embodiments, a wired communication means is used.

For example, in some embodiments, the system and/or control device 110 may be a wireless system or device, such as a wireless headset. In such embodiments, components of the system 300 and/or the control device 110 may be specifically selected or configured for low power operation to allow for extended use without requiring, for example, a relatively large battery. This may allow the overall device or system to be reduced in size, making it cheaper to manufacture.

The activity determination model 328 and/or symptom severity or progress determination model 327 may be based on models such as logistic regression, linear regression, and neural networks, for example, that have been trained to infer an activity metric from fNIRS data. In some embodiments, the activity determination model 328 and/or the symptom severity or progress determination model 327 are trained using supervised machine learning approaches using a training dataset that is divided into a training data subset and a test data subset. The training dataset contains data for a plurality of individuals who completed a clinically relevant neurobehavioral disorder rating scale questionnaire and were subjected to a psychological test that measures performance of the function while the optical sensor module 340 recorded the fnrs data. Thus, the training data contains exemplary data for each of a plurality of individuals. Exemplary data includes the results of the psychometric tests (and in some embodiments the types of tests) and associated fnrs data recorded when the respective tests were performed.

In some embodiments, the activity determination model 328 and/or the symptom severity or progress determination model 327 may be trained using unsupervised machine learning to help mask any additional relationships between optical signal data, task data (e.g., psychometric test results), and/or stimulus parameters. The activity determination model 328 and/or the symptom severity or progress determination model 327 may also form the basis of feedback provided to the medical professional in the treatment of the user 115. In this way, consistent feedback may be provided to the clinician to help assess the progress of the user and/or make therapeutic decisions, and in some embodiments, reconfigure the control 110. In some embodiments, activity determination model 328 may include one or more sub-models configured to infer brain activity for a particular task. In other embodiments, the activity determination model 328 may be configured to receive as input scores associated with a plurality of respective tests.

In some embodiments, the stimulation control model 329 may be determined or trained based on experimental assessment or evaluation of the efficacy of different stimulation parameters. For example, stimulation control model 329 may contain models such as logistic regression, linear regression, and/or neural networks trained using supervised machine learning approaches. In some embodiments, similar psychometric tests may be performed on participants with and without the application of stimulus, and statistical changes in performance observed and recorded. By stimulating many different stimulation configurations and experimentally evaluating the most promising stimulation configuration, decisions will be made on the most appropriate clip and stimulation parameters (e.g., amplitude and type/frequency of stimulation).

In some embodiments, one or more of stimulus control module 326, model 329, activity determination model 328, symptom severity (or) progress determination model 327, and feature extraction module may be located within memory 224 of computing device 220 and executed by processor 222 of computing device 220.

Fig. 4 is an example of an apparatus 100 according to some embodiments. The device 100 includes a front strap 405 configured to be placed in front of the head of the user 115 and a rear strap 415 having a back electrode mount 410 configured to be placed on the back of the head of the user 115. The front strap 405 may pivot about point 420, as depicted in fig. 5, and the rear strap may be adjustable to accommodate different head sizes of the user 115. The back sheet 415 may be configured to house or support the control device 110, thereby acting as the housing 112 in fig. 2. The return electrode 123 may also be disposed on the back sheet 415. In some embodiments, the back sheet 410 may be omitted. As depicted in fig. 7, the front strap 405 may further include an elongated array 700 arranged to carry or mount the electrodes 120, which in this embodiment are disposed in a spaced apart manner along the length of the array 700. The array 700 may carry or mount the optical sensor 130 (or optical sensor component), and more particularly, the light emitter 130A and the detector 130B. In this example, each optical sensor 130 includes a light emitter 130A and two corresponding light detectors 130B. The electrodes 120, light emitters 130A, and light detectors 103B are arranged or placed on the same side on the inner surface of the array 700 such that they are in contact with the user's head when the device is worn by the user. In such embodiments, the front strap 405 holds the array 700 in position against the front of the head of the user 115 and is configured to more easily provide consistent electrical stimulation to the user 115 and receive more consistent optical responses from the user 115. Thus, in this embodiment, when the electrodes 120 and optical sensor 130 are placed on the head of the user 115, the fixed locations on the array allow the same area to be stimulated over multiple stimulation sessions. This has the benefit of achieving a high degree of consistency in the application of the electrical stimulus to the desired region of the head and ensuring the accuracy of the measured optical signal-which in turn has a benefit in the accuracy of the determination of the benefit by the analysis engine 324. In some embodiments, control device 110 may instruct electrical stimulation source 350 to provide electrical stimulation to select a particular electrode 120 or a particular combination of electrodes 120. In other embodiments, control device 110 is configured to cause electrical stimulation source 350 to provide electrical stimulation to all electrodes in array 700.

In some embodiments, a different number of electrodes 120 and/or optical sensors 130 may be provided, such as a high number or a lower number, which may depend on the size of the array 700 on the front strap 405.

Fig. 12 depicts a section 1200 of the array 700. The segment 1200 includes an electrode 1202 having a first optical sensor 1204 disposed on a first side 1206 of the electrode 1202 and a second optical sensor 1208 disposed on a second side 1210 of the electrode 1202. The first optical sensor 1204 includes first and second light detectors 1204A ₁ And 1204A ₂ And a light emitter 1204B. The second optical sensor 1208 includes first and second photodetectors 1208A ₁ 、1208A ₂ And a light emitter 1208B.

As shown, in some embodiments, the light emitter 1204B of the first optical sensor 1204 is disposed on the first side 1206 of the electrode 1202 toward the first end 1212 of the electrode 1202 or at the first end 1212 of the electrode 1202. First light detector 1204A ₁ Disposed on the first side 1206 of the electrode 1202 toward or near the first end 1212 of the electrode 1202, and for example near the light emitter 1204B, toward the first end 1212 of the electrode 1202. A second light detector 1204A of the first optical sensor 1204 ₂ Disposed toward the second end 1214 of the electrode 1202 (opposite the first end 1212) on the first side 1206 of the electrode 1202, disposed at the second end 1214 of the electrode 1202, or disposed immediately adjacent the second end 1214 of the electrode 1202. In some embodiments, the second light detector 1204A ₂ Is positioned closer to the first light detector 1204A than the light emitter 1204B ₁ . In other words, the first light detector 1204A ₁ Positioned innermost of the light emitters 1204B.

As shown, in some embodiments, the light emitter 1208B of the second optical sensor 1208 is disposed on the second side 1210 of the electrode 1202 toward the second end 1214 of the electrode 1202, at the second end 1214 of the electrode 1202, or near the second end 1214 of the electrode 1202. First light detector 1208A of second optical sensor 1208 ₁ Disposed toward or near the second end 1214 on the second side 1210 of the electrode 1202. A second light detector 1208A of the second optical sensor 1208 ₂ Toward the first end 1212, at the first end 1212, or immediately adjacent to the first end 1212. In some embodiments, the second light detector 1208A ₂ Is positioned closer to the first light detector 1208A than the light emitter 1208B ₁ . In other words, the first light detector 1208A ₁ Positioned innermost of the light emitters 1204B.

As explained above, when the device 100 containing the array 700 is applied to the head of a subject, the optical sensor 130 is configured to measure brain activity at a location or position in the middle of the subject's brain, or at a midpoint between the light emitter 130A and the light detector 130B.

Referring again to fig. 12, when stimulus is applied to the subject's head via electrodes 1202, a pair of light emitter detectors 1204A ₁ And 1204B are configured to be disposed at the light emitter 1204A ₁ And the location of the light detector 1204B, a systemic (skin, skull, etc.) blood oxygenation change of the subject is determined or measured at an intermediate location.

Referring again to fig. 12, when stimulus is applied to the subject's head via electrodes 1202, a pair of light emitter detectors 1204A ₂ And 1204B are configured to detect light at the light detector 1204A ₂ And the light emitter 1204B, at a location intermediate to the location of the light emitter 1204B, at location 1230A in fig. 12, the brain activity of the subject is determined or measured. Similarly, a light emitter detector pair 1204A ₂ And 1208B are configured to detect light at the light detector 1204A ₂ An intermediate location between the location of light emitter 1208B, location 1230B in fig. 12, where brain activity of the subject is determined or measured; photo-emitter detector pair 1208A ₂ And 1204B are configured to detect light at light detector 1208A ₂ An intermediate location between the location of the light emitter 1204B, location 1230D in fig. 12, where brain activity of the subject is determined or measured; photo-emitter detector pair 1208A ₂ And 1208B are configured to detect light at light detector 1208A ₂ An intermediate location between the location of light emitter 1208B, location 1230C in fig. 12, where brain activity of the subject is determined or measured; the light emitter detector pair 1208A ₁ And 1208B are configured to emit light at light emitter 1208A ₁ A general (skin, skull, etc.) blood oxygenation change of the subject is determined or measured at an intermediate location between the location of the light detector 1208B; photo-emitter detector pair 1208A ₁ And 1204B are configured to detect light at light detector 1208A ₁ An intermediate location between the location of the light emitter 1204B, location 1230E in fig. 12, where brain activity of the subject is determined or measured; photo-emitter detector pair 1204A ₁ And 1204B are configured to detect light at the light detector 1204A ₁ Determining or measuring brain activity of the subject at an intermediate location between the location of the light emitter 1204B; and a photo-emitter detector pair 1204A ₁ And 1208B are configured to detect light at the light detector 1204A ₁ Intermediate portion between the position of light emitter 1208BPosition 1230E in fig. 12, where brain activity of the subject is determined or measured.

Emitter-detector pair 1204A ₂ And 1204B, 1204A ₂ And 1208B, 1208A ₂ And 1204B, 1204A ₁ And 1208B, 1208A ₁ And 1204B, and 1208A ₂ And 1208B each form a relatively long channel. These long channels are arranged to measure cerebral blood oxygenation at an intermediate point between the emitter-detector pairs. Emitter-detector pair 1204A ₁ And 1204B and 1208A ₁ And 1208B each form a relatively short channel. These short channels are arranged to measure cerebral blood oxygenation in the vicinity of the scalp or scalp region of the subject at an intermediate point between the emitter-detector pairs. This information (i.e., cerebral blood oxygenation in the nearby scalp) may be used by the analysis engine 324 to remove scalp information from long channel measurements during data processing. By configuring the placement of the optical sensor 130, i.e., the pair of light emitters 130A and light detectors 130B, relative to the respective electrode 120, a particular site or portion of the subject's brain can be targeted with the associated (or responsive) brain activity stimulated and measured. In some embodiments, the control device 110 is configured to determine brain activity at a particular site of the subject's brain based on one or more sensor signals received from one or more light emitter and light detector pairs 130A, 130B of the respective sensor module 130. For example, where the control device 110 includes an array 700 including the section 1200 of fig. 12, a location near and around the site of application of stimulation to the subject (which may be a portion or site of the subject's brain directly under the electrode 1202, indicated at location 1230E) and brain activity at the stimulation site 1230E may be determined. In some embodiments, emitter-detector pairs 1204B and 1208A may be used ₁ And/or 1208B and 1204A ₁ To determine brain activity at location 1230E.

This arrangement provides an integrated mechanism for fnrs recording and electrical stimulation of the brain at the same site. The device 100 containing the array 700 or the like may allow for more accurate measurement and analysis of the effects of electrical stimulation of the brain using fNIRS.

In some embodiments, the location of the electrode 120 may be based on the EEG 10-5 system and include locations F3-F4, FP2-F3, P3-FP2, F6-F5, AF7-AF8. However, it should be appreciated that the locations of the electrodes 120 may include any combination of locations that span a line between P7-P8, FT9-FT10, F9-F10, AF7-AF8, FP1-FP2, PO3-PO4, and O1-O2. This includes the 10-5 locations between these landmarks on the same plane. The optical sensor 130 may be placed around the selected stimulation channel to ensure close proximity to the site of electrical stimulation. In such embodiments, different strap shapes and locations may be used to ensure an accurate fit with the head of the user 115. In some embodiments, adjusting the front strap 405 by the pivot point 420 allows the electrode 120 to target a desired head region.

The assembly of the array 700 may be particularly configured to allow the optical sensor 130 to be placed at a desired distance from the head of the user 115 to ensure that a given user 115 obtains accurate measurements.

Placement of electrodes for delivering neural stimulation is important because it determines the brain site to which the stimulation is targeted. The device 100 may be configured to fit or accommodate a variety of head sizes, noting that head sizes vary from person to person, and particularly between sexes. Due to variations in head size, the location of the electrodes on the static or fixed arrangement array 700 of the device 100 may mean that the placement of the electrodes 120 (e.g., where the electrodes contact the forehead) may vary from individual to individual, which may result in variations in stimulation results and/or effectiveness of the application of stimulation.

Thus, in some embodiments, the front strap 405 and/or the array 700 of the device 100 may be adjustable to allow for selective placement of the electrodes 120 at desired locations on the subject's head.

In some embodiments, the control device 110 may allow for software distribution of electrode sites relative to the subject's head (e.g., selection of a particular subset of the electrodes 120 of the array 700) such that, depending on the user's head size, an appropriate subset of the electrodes 120 may be selected for delivering stimulation to the subject.

Fig. 13 depicts a head top arrangement 1300 of an array 700 positioned on a subject's head 1310. Array 700 includes a plurality of electrodes 120, a set of which may be selectively used to deliver stimulation to the head of a subject. In this example, the array 700 includes four electrodes, of which the outer two are selected electrodes 1320 and the inner two are unselected electrodes 1330. This selection of electrodes 120 may be adapted or suited for subjects having a relatively large head size. Similarly, fig. 14 depicts a head top arrangement of an array 700 positioned on a subject's head 1310. Array 700 includes a plurality of electrodes 120, a set of which may be selectively used to deliver stimulation to the head of a subject. In this example, the array 700 includes four electrodes, of which the outer two are unselected electrodes 1330 and the inner two are selected electrodes 1320. This selection of electrodes 120 may be adapted or suited for subjects having relatively small head sizes.

The number of electrodes depicted in fig. 13 and 14 is merely representative. Thus, any number of electrodes, such as 4, 6, or 8 electrodes, etc., may be arranged on the array 700.

In some embodiments, the control device 110 may be configured to receive input from a subject or other user, e.g., via the user interface 360, to indicate which size configuration should be accommodated, and which in turn may dictate which combination of electrodes 120 the control device 110 uses in applying the stimulus. For example, the user interface 360 may allow the subject to select a small, medium, or large head size.

In some embodiments, the control device 110 may be configured to assist in determining the proper selection of electrodes for a given subject by applying test stimulus delivered to the subject's head via one or more sets of electrodes 120, and analyzing the responses received via the respective sensor modules 130 to determine the exact placement of the electrodes relative to the subject's head. In some embodiments, the control device 110 may be configured to analyze the response and determine whether the one or more sets of electrodes are appropriate, and in some embodiments, to assist in determining or selecting one of the one or more sets of electrodes as a selected electrode for applying stimulation to the subject. In some embodiments, the control device 110 may transmit a response to the cognitive performance monitoring application 225 or the server 230 of the computing device 220 over the network 210 for analyzing the accurate placement of the electrodes relative to the subject's head. For example, the control device 110, the computing device 220, and/or the server 230 may be configured to analyze the response to determine whether the response satisfies a condition associated with a strong or valid electrode placement of the array 700 that indicates proper placement. In some embodiments, if the response does not meet the condition, the control device may select another set of electrodes and reapply the test stimulus and measure the associated response to analyze the response to determine proper placement of the electrodes. For example, where the analysis is performed by the computing device 220 or the server 230, this may involve transmitting appropriate instructions to the control device 110. In some embodiments, the cognitive performance monitoring application 225 may cause instructions to be output to a subject or user, for example, using the user interface 228 to instruct the user to alter the placement of the array 700 on their head.

By allowing the electrode sets of the array 700 to be selectively selected to accommodate different users, the control apparatus 110 and/or device 100 may be customized to the individual stimulus of the subject, which may be particularly beneficial in a home environment.

In some embodiments, a facial recognition filter may be used to help the subject place array 700 reliably or accurately, and thus place electrodes 120 on their head. In such embodiments, computing device 220 includes a forward facing camera that may be configured to allow users to capture their own images. The memory 224 of the computing device 220 may contain a positioning feedback module 227 that, when executed by the processor 222, causes the computing device 220 to assist a subject or user in correctly placing the array 700, and thus the electrodes 120, relative to their head based on the captured image or image stream. The positioning feedback module 227 may include a facial recognition algorithm that allows facial landmarks such as the nose, eyebrows, hairline, and/or other facial features to be determined from the captured image or image stream. Such features may allow for determining the pose and/or structure of the user's face. The cognitive performance monitoring application 225 may also be configured to determine the location of the array 700 of control devices 110 relative to the determined facial features, or the pose of the array 700 itself. The cognitive performance monitoring application 225 may also be configured to compare the determined location of the array 700 to an ideal or target location (or range of locations), and based on the comparison, may provide feedback to the subject to direct them to reposition the array 700 to achieve the target location. For example, the cognitive performance monitoring application 225 may display an indication to a user via the user interface 228 to indicate the current location of the array and the target location of the array to help the subject achieve the desired placement. One benefit of such a positioning feedback module 227 is that it allows for more accurate and reliable placement of the device 100 when used by a user, which may result in improved stimulation and data capture reliability, particularly in a home environment.

Fig. 8 depicts a process flow of a method 800 for controlling transcranial electrical stimulation delivery according to some embodiments. For example, the method may be performed by the processor 310 of the control device 110 executing the feature extraction module 322, the analysis engine 324, and the stimulation control module 326 of the memory 320. In some embodiments, the method 800 may be performed by the processor 222 and/or the server 230 of the control device 110 and the computing device 220. Fig. 17 depicts an overview of a method 800 according to some embodiments. While performing the method 800, the user may be at rest.

At 805, the control device 110 sends or transmits instructions from the stimulation control module 326 to the electrical stimulation source 350 to cause the electrical stimulation source 350 to deliver electrical stimulation to the one or more electrodes 120. One or more electrodes are arranged or configured to be placed on the head of the user 115 to deliver transcranial electrical stimulation to a target area of the brain of the user 115. The instructions sent by the control device 110 may include instructions defining one or more of a voltage, a current, a frequency, a duration, and/or an offset value of an electrical signal to be applied or delivered to the electrode. The electrical stimulation may be applied for a preselected length of time or until the stimulation is otherwise altered. The instructions may include instructions to supply tDCS, tACS, tPCS, tRNS, otDCS or random noise stimulus, or a combination thereof.

The instructions may further comprise instructions to supply relatively short pulses, wherein each pulse is characterized by an amplitude, a frequency, a duration, and an offset. Each pulse may be delivered one at a time. In other embodiments, the instruction may comprise a single long pulse. In some embodiments, the initial and updated stimulation parameter values for delivering electrical stimulation to the user may depend on the particular region of the brain to be targeted and/or the type of task or activity to be performed by the user during the session.

At 810, the control device 110 receives recorded data from one or more corresponding optical sensors positioned near a target area of the brain. The recorded data may contain one or more signals from the corresponding one or more optical sensors. These signals may be indicative of the intensity of the reflected light detected by the detector 130B of the optical sensor. In some embodiments, and as discussed above, the optical sensor module 340 may be configured to produce a lock-in amplifier effect to improve the signal-to-noise ratio (SNR) of the detected reflected light signal.

In some embodiments, after delivering electrical stimulation to one or more electrodes 120, optical sensor module 340 records the optical response detected by the corresponding optical sensor 130 positioned on the head of user 115 and transmits or provides the recorded data to control device 110. In some embodiments, the optical sensor module 340 records data before, during, or simultaneously with the application of the stimulus, and after the application of the stimulus, e.g., in real-time or continuously, and transmits the recorded data to the control device 110 for real-time processing or transmission. In some embodiments, the control device 110 may be configured to transmit or stream the recorded data to the server 230 or the computing device 220 for processing. In some embodiments, and as discussed above, the control device 110 may be configured to sample data at a relatively high frequency and to downsample and/or demodulate the data prior to transmitting the data to, for example, a computing device or server.

At 815, the control device 110 (or in some embodiments, the computing device 220 and/or the server 230) analyzes the recorded data to determine an activity or effectiveness measure. The recorded data may be indicative of a change in oxygenation of the blood.

In some embodiments, analyzing the recorded data includes removing or mitigating scalp effects from the recorded data. This may be accomplished, for example, by determining one or more short channels associated with or in the vicinity of the candidate long channel, and subtracting the signal from the short channel from the long channel. In some embodiments, all available short channel signals are subtracted from the candidate long channel. In some embodiments, the signal from only the short channel that is physically closest to the candidate long channel is subtracted from the long channel signal.

In some embodiments, other signal processing techniques may be used to isolate the effects of stimulus in the signal from the measured channel. Examples include bandpass filtering the measurement data, regression of accelerometer data from the signal, and/or regression of baseline drift from the signal.

In some embodiments, the feature extraction module 322 extracts features or characteristics from the recorded data or the optical response signals or processed recorded data. The extracted features may correspond to characteristics or biomarkers associated with cognitive function or performance or cortical activity of the user. The extracted features may be provided as inputs to an activity determination model 328, which may provide activity metrics as outputs. In some embodiments, the activity metric comprises a transient increase in HbO.

In some embodiments, and as depicted in fig. 17, the control device 110 may be determined from recorded or measured sensor data, pre-stimulus sensor data (i.e., data acquired prior to the application of a stimulus), stimulus sensor data (i.e., data acquired during the application of a stimulus), and post-stimulus data (i.e., data acquired after the application of a stimulus). Feature extraction module 322 may determine a set of pre-stimulus features, a set of stimulus features, and a set of post-stimulus features from each of the respective sets of pre-stimulus sensor data, and post-stimulus data. In some embodiments, the features include functional connectivity between a pair of channels (two regions of the brain), and/or statistical measures of data acquired from the data channels. In some embodiments, features are extracted from sensor data acquired from pairs of optical channels around and between stimulation electrodes.

The control device 110 may determine a first set of inputs to the activity determination model 328 based on the pre-stimulus feature set and the post-stimulus feature set. The first set of inputs may comprise a relative change in the characteristic value from pre-stimulus to post-stimulus. The control device 110 may determine a second set of inputs to the activity determination model 328 based on the pre-stimulus feature set and the during-stimulus feature set. The second set of inputs may comprise a relative change in the characteristic value from pre-stimulus to during-stimulus. The control device 110 may determine a third set of inputs for the activity determination model 328 based on the during-stimulus feature set and the post-stimulus feature set. The third set of inputs may comprise a relative change from during stimulation to post-stimulation eigenvalues.

The control device 110 may be configured to provide the first, second, and third sets of inputs and the applied stimulation parameters (e.g., amplitude) to the activity determination model 328 to determine an activity metric, such as a probability of sufficient stimulation being applied.

In some embodiments, only two of the three sets of inputs include values for a particular feature. For example, the first and second sets of inputs may include the value of the first feature, but the third set of inputs does not include the value of the feature.

In some embodiments, the control device 110 determines only two of the first, second, and third sets of inputs and provides only the first and second sets of inputs, the first and third sets of inputs, or the first and third sets of inputs, and the applied stimulation parameters to the activity determination model 328 to determine an activity metric, such as a probability of sufficient stimulation being applied.

The activity metric may indicate whether the user's brain activity is determined to be sufficiently active, underactive, overactive, overresponsive, sufficiently responsive, or insufficient responsive. In some embodiments, the activity metric may be compared to one or more activity metric thresholds to determine whether the subject is sufficiently active, underactive, overactive, overresponsive, sufficiently responsive, or insufficiently responsive.

In some embodiments, the activity metric is compared to a threshold level to determine whether the subject's brain is non-responsive to the stimulus or responsive. At 820, the control device 110 may modify the stimulation instructions based on the determined activity metric. In some embodiments, the stimulation control module 326 determines one or more stimulation parameter values based on the determined activity metrics. The stimulation instructions may include stimulation parameter values.

At 825, control device 110 may transmit updated stimulation instructions containing the determined one or more stimulation parameter values to the electrical stimulation generator to cause the electrical stimulation generator to modify one or more characteristics of the stimulation.

In some embodiments, in response to determining that the activity metric is less than the threshold, the control device 110 increases the stimulation parameter value and reappears stimulation at the increased stimulation parameter value. In some embodiments, in response to determining that the activity metric has reached the threshold, the control device 110 determines the stimulation parameter value as a user-specific calibrated stimulation parameter.

The updated stimulation instructions may include instructions to modify the frequency, amplitude, voltage, and/or current of the electrical stimulation delivered to the electrodes 120. In embodiments where the activity metric indicates that sufficient stimulation has been applied, and for example, a change in brain activity of the user 115 corresponding to a desired effect on a symptom of a neurological disorder, e.g., a symptom of ADHD, the stimulation control module 326 may modify the electrical stimulation by ceasing to deliver stimulation, i.e., the stimulation parameter value may include zero or other indicators for ceasing stimulation. In some embodiments, the stimulation parameter values generated by stimulation control module 326 may instruct or command electrical stimulation generator or source 350 to continue to stimulate for a period of time in an existing setting, or modify characteristics of the electrical stimulation signal to target a brain region, and/or to elicit a different response.

If the desired activity level is not met, the stimulation parameter values generated by the stimulation control module 326 may instruct or command the electrical stimulation generator or source 350 to continue delivering the current stimulation level, increase the stimulation level, decrease the stimulation level, or modify the characteristics of the applied stimulation. The desired activity level or activity level threshold may depend on task information, such as the type and duration of tasks or activities being or to be performed by the user during the session.

For example, prior to applying the stimulus, the control device 110 may determine an average oxygenation concentration variation of 2 micromolar in amplitude from measurement data associated with optical signals from the respective one or more sensors 130. Once the stimulus is applied, the control device 110 can determine (based on the measurement data) that the subject's brain activity has increased to an oxygenation concentration variation of 3 micromolar amplitude. In this example, the effect of the stimulus on the subject may be a 1 micromolar change in blood oxygenation concentration in the measurement zone. In some embodiments, such an increase may also be accompanied by an increase in performance of the particular task, such as an increase in accuracy and/or reaction time. The activity determination model 328 may determine an activity metric using the magnitude of blood oxygenation concentration and optionally a task score achieved while undertaking tasks while recording sensor data as input. If the resulting activity metric meets the activity threshold, the control device 110 may determine that sufficient stimulation has been delivered and instruct the stimulation generator to continue delivering the appropriate level of stimulation for a period of time and stop delivering stimulation at the predetermined point. On the other hand, if the resulting activity metric does not meet the activity threshold, the control device 110 may determine that stimulation needs to be continued (with the existing parameter value or with the changing parameter value), and may instruct the stimulation generator to maintain or modify the stimulation accordingly.

In some embodiments, optical sensor 130 may continue to measure or monitor the optical response from user 115 at all times during the session, regardless of whether electrical stimulation is being applied. In such embodiments, the optical sensor 130 may detect a change in brain activity of the user 115 that indicates that an electrical signal may need to be applied again, e.g., the brain activity of the user in the target area drops below a threshold activity level. Thus, the user 115 using the device for a period of time may activate the device to initiate a stimulation session, wherein electrical stimulation is applied via the electrode 120 for a period of time until a threshold activity level is met, and wherein after the beneficial effects of the initial electrical stimulation on symptoms of a neurological disorder, such as symptoms of ADHD, are no longer detected, further electrical stimulation is applied via the electrode 120.

In some embodiments, the method 800 may be used to calibrate the device 100 for a particular subject. For example, the determined activity metric may be compared (at 815) to a calibrated threshold level to determine whether the subject's brain is non-responsive to the stimulus. Responsive to the brain being deemed unresponsive, the control device 110 may be configured to modify the stimulation instructions based on the determined activity metrics (at 820). For example, the control device 110 may be configured to transmit (at 825) an updated stimulation instruction containing the determined stimulation parameter value to the electrical stimulation generator to cause the electrical stimulation generator to modify one or more characteristics of the stimulation. In some embodiments, the stimulation parameter value may be a fixed increase in the stimulation to be applied. The activity metric may again be determined (at 815) and the stimulation instructions (e.g., increased stimulation) updated until sufficient stimulation is deemed to have been applied or the electrical stimulation generator has reached a maximum safety limit. In some embodiments, once sufficient stimulus is deemed to have been applied, or the electrical stimulation generator has reached a maximum safety limit, the control device may instruct the electrical stimulation generator to continue delivering sufficient stimulus deemed to be for the session, deliver sufficient stimulus deemed to be for a particular time window during the session, or stop delivering stimulus to the subject.

Such a calibration procedure may be performed at the beginning of a session with a subject such that the device is calibrated for a particular subject and any factors that may affect or alter the extent to which current is received by the subject are considered, such as, for example, skin oiliness, electrode conductivity, hair growth/thickness/style, etc., which may vary from session to session.

In some embodiments, the control device 110 may be activated or started by activating an HMI 355 switch. In some embodiments, the control device 110 may be activated or launched by a cognitive performance monitoring application 225 being executed by the processor 222 of the computing device 220. In such embodiments, the user 115 may initiate instructions using the user interface 228 of the computing device 220 while using the application 225. In some embodiments, the control device includes a user interface 360 configured to allow a user to provide input to activate/deactivate and/or control operation of the control device and system as a whole. The user interface 360 may also contain a display or audio output to convey information to the user regarding the operation of the control device 110.

In some embodiments, the control device 110 may be configured to transmit data associated with the detected brain activity 110 to a computing device 220 associated with the user, or to the server 230 for processing or storage, via the communication module 330. The data may include metrics associated with the applied electrical stimulation, the optical response of the detected brain activity, measurement data derived from the optical response, detection of one or more biomarkers, or other data related to the stimulation session. In some embodiments, the application 225 may store the data in the memory 224 of the computing device 220, for example, accessed by the user 115 and displayed on the user interface 228 of the computing device 220. The application 225 may also display stimulation history, past psychometric test performance, or other information related to the stimulation session. In such embodiments, the data may be received from the control device 110 or retrieved from the database 240 via the network 210.

In some embodiments, the treatment session may be initiated by a user activating HMI switch 355 or other activation mechanism provided on control 110 or apparatus 100. In some embodiments, the cognitive performance monitoring application 225 may cooperate with the control device 110 to initiate session initiation. For example, a user or clinician may interact with the cognitive performance monitoring application 225 to begin a session. In some embodiments, the cognitive performance monitoring application 225 transmits the task data to the control device 110, a computing device 220 associated with, for example, a clinician, or a server 230. The task data may contain information about the type of task the user is or will be performing. The task data may contain one or more scores that the user achieves when performing a particular task.

In some embodiments, the task data may include one or more scores achieved by the user in performing the task, and which may be used by the control device 110 in conjunction with the measurement data to infer behavioral progression of the subject. In other embodiments, the record data and/or activity metrics and task data including scores may be transmitted to a server, such as a remote server, for processing to infer behavioral progression of the subject.

Fig. 10 depicts a process flow diagram of a method 1000 of inferring symptom severity and/or behavioral progression in a subject undergoing treatment for one or more symptoms of a neurological disorder, in accordance with some embodiments. In some embodiments, the method 1000 may be performed by the control device 110. In other embodiments, the method 1000 may be performed by the control device 110 in conjunction with the server 230 and/or the computing device 220. Fig. 18 depicts an overview of a method 1000 according to some embodiments.

At 1005, control device 110 may send or transmit instructions to stimulation control module 322 to cause electrical stimulation source 350 to deliver electrical stimulation to one or more electrodes 120. The one or more electrodes 120 are arranged or configured to be placed on the head of the user 115 to deliver transcranial electrical stimulation to a target region of the brain of the user 115. The instructions sent by the control device 110 may include instructions defining one or more of a voltage, a current, a frequency, a duration, and/or an offset value of an electrical signal to be applied or delivered to the electrode. The electrical stimulation may be applied for a preselected length of time or until the stimulation is otherwise altered. The instructions may include instructions to supply tDCS stimulation, tcacs stimulation, or random noise stimulation, or a combination thereof. As discussed above with reference to process 800, the instructions may include instructions to supply short pulses or long pulses. However, in some embodiments of method 1000, electrical stimulation need not be delivered to the subject, and symptom severity and/or progression metrics may be determined based solely on task data and sensor data.

At 1010, the control device 110 receives or determines recorded data from one or more corresponding optical sensors positioned near a target area of the brain. The recorded data may contain one or more signals from the corresponding one or more optical sensors. These signals may be indicative of the intensity of the reflected light detected by the detector 130B of the optical sensor. The recorded data may be recorded without applying a stimulus (i.e., without applying any stimulus), after applying a stimulus, or while applying a stimulus. In some embodiments, and as discussed above, the optical sensor module 340 may be configured to produce a lock-in amplifier effect to improve the signal-to-noise ratio (SNR) of the detected reflected light signal.

At 1015, the control device 110, the computing device 220, and/or the server 230 determine task data. For example, the task data may be determined by the cognitive performance monitoring application 225. The task data may include one or more scores associated with the user's performance in undertaking or participating in one or more respective tasks. For example, suitable types of tasks include psychometric tasks or tests that measure performance of functions such as working memory, impulse control, cognitive flexibility, and tasks such as a stroop task, a wisconsin card class (wisconsin card sorting) task, a Ke Erxi block test (corsi blocking test), a go-no-go task, a continuous performance task, and an n back task. The psychometric task or test may be a modified or gamified version of the standard test. In some embodiments, the task data is determined substantially simultaneously with the recorded data from the one or more respective optical sensors (1005).

In some embodiments, the task data may include scores or metrics for one or more behavioral or characteristic symptoms, such as accuracy, reaction time, omission errors, and/or misclassification errors. The system may determine a task feature value based on a metric of the task data. The task feature values may be statistical measures of the metric, such as accuracy, reaction time, mean and/or standard deviation values of missing errors and/or misclassification errors.

In some embodiments, the symptom severity and/or progress determination model 328 may use eigenvalues of task data metrics derived from reaction time to determine a primary ADHD core symptom score or measure, a inattention severity score, an impulsive severity score, and/or a hyperactive severity score.

At 1020, the control device 110, the computing device 220, or the server 230 determines a symptom severity or progress metric based on the recorded data and the task data. In embodiments where the server 230 or computing device 220 determines a symptom severity or progression metric, the respective server 230 or computing device 220 may be configured to determine or receive recorded data from the control device 110. In some embodiments, the control device 110 may be configured to transmit or stream the recorded data to the server 230 or the computing device 220. In some embodiments, and as discussed above, the control device 110 may be configured to sample the data at a relatively high frequency and thus downsample and/or demodulate the data prior to transmitting the data to, for example, a computing device or server.

The symptom severity and/or progression measure may comprise one or more scores for a corresponding one or more behaviors or characteristics of a neurobehavioral disorder, such as ADHD. For example, symptom severity or progress determination model 238 may be configured to provide a score for one or more of: (i) an overall ADHD rating scale score, (ii) an ADHD core symptom score, (iii) a inattention score, (iv) a hyperactivity score, and (v) an impulsivity score. In such instances, the symptom severity or progression determination model 238 may be trained using tagged data tasks and sensor data, tagged with scores determined from the ADHD rating scale questionnaire. For example, a clinical population may be required to fill a standard ADHD rating scale questionnaire that may be used to determine the scores for a variety of ADHD symptoms, such as i) overall ADHD rating scale score, (ii) ADHD core symptom score, (iii) inattention score, (iv) hyperactivity score, and (v) impulsivity score. Task data and associated sensor data for the clinical population are determined and labeled with a determined score for the associated participant. The symptom severity or progress determination model 238 is then trained using the task data, sensor data, and labels. Thus, the symptom severity or progress determination model 238 can be used as an automated symptom severity or progress measurement or monitoring tool for measuring or monitoring neurobehavioral disorders.

In some embodiments, the control device 110, the server 230, or the computing device 220 analyzes the recorded data including the intensity signal to determine measurement data. For example, the measurement data may include recorded data, and/or data derived from the recorded data, including one or more of: (i) an oxygenated hemoglobin (HbO) concentration; (ii) deoxyhemoglobin (HbR) concentration; and total hemoglobin (HbR) concentration. An exemplary plot of HbO, hbR, and HbR concentrations is shown in FIG. 11.

In some embodiments, the control device 110 is configured to receive sensor data from a plurality of channels, each channel corresponding to an emitter-detector pair of the optical sensor 130. As discussed above, one or more of the channels are relatively short channels, with the transmitters disposed near the respective detectors, and one or more of the channels are relatively long channels, with the transmitters disposed at relatively large distances from the respective detectors.

The long channel is configured to measure cerebral blood oxygenation at an intermediate point between the respective emitter-detector pairs. The short channel is configured to measure cerebral blood oxygenation in the vicinity of the scalp or scalp region of the subject at a point intermediate between the emitter-detector pairs. In some embodiments, only signals from long channels are used.

In some embodiments, the control device 110, the computing device 220, or the server 230 is configured to screen useful biological information provided by the optical sensor 130. For example, the control device 110, the computing device 220, or the server 230 may be configured to determine whether the signal from the corresponding channel is of sufficient quality. Channels determined to be ineffective and/or of insufficient quality in obtaining useful information (i.e. "bad" channels) may be excluded from further analysis. In some embodiments, a quality metric indicative of the quality of each detector channel of the optical sensor is determined, and in response to the quality metric falling below a quality threshold, sensor data from the respective detector channel is excluded or ignored in determining the symptom severity or progress metric.

In some embodiments, a Scalp Coupling Index (SCI) is determined for each of the channels. SCI is a measure of the signal quality of the channel for a particular measurement duration. In response to the SCI falling below the threshold SCI value, the control device 110, the computing device 220, or the server 230 may be configured to determine that the corresponding channel is "bad" and exclude measurements from the channel for further analysis. In some embodiments of the present invention, in some embodiments, The control device 110, the computing device 220, or the server 230 may be configured to determine detector saturation. This may be determined, for example, by detecting whether the voltage measurement is outside an acceptable range, such as a value of-1.2V, for example. In response to the determined detector saturation, the control device 110, the computing device 220, or the server 230 may be configured to determine that the corresponding channel is "bad" and exclude measurements from the channel for further analysis. In some embodiments, the control device 110, the computing device 220, or the server 230 may perform motion artifact correction on signals received from the channel to reduce motion artifacts. For example, such motion artifact correction may be configured to model motions using spline interpolation and subtract them from the corresponding signals. In some embodiments, a wavelet-based approach is used. Further details on suitable spline interpolation and wavelet-based methods can be found in the following papers: scholkamm et al, "how to detect and reduce motion artifacts in near infrared imaging using motion standard deviation and spline interpolation" (How to detect and reduce movement artifacts in near-infrared imaging using moving standard deviation and spline interpolation) https://pubmed.ncbi.nlm.nih.gov/ 20308772/) And Molavi et al, "Wavelet-based functional near infrared spectral motion artifact removal (Wavelet-based motion artifact removal for functional near-infrared spectroscopy)", respectivelyhttps:// iopscience.iop.org/article/10.1088/0967-3334/33/2/259/metacasa_token＝ s1IqbEC3gYQAAAAA:gbJBtl-KCd_xpeG2oKUflnjnh5BHdGFR7UqQWmmjNjghCDwWTpWLx7j9NI9 9HboeTw5zosE80A) Both of which are incorporated herein by reference in their entirety.

In some embodiments, control device 110, computing device 220, or server 230 may perform band pass filtering on multiple channels to remove irrelevant information.

In some embodiments, the control device 110, the computing device 220, or the server 230 may further process the information from the channel to remove other artifacts such as known artifacts from the recorded data, for example, to increase or maximize the significance of the hemodynamic response in the observed data. This can be achieved by performing regression with polynomial drift, short channel data and accelerometer data. For example, the apparatus 100 and/or the control device 110 may contain an accelerometer (not shown) for capturing accelerometer data. Polynomial drift may be calculated by fitting a polynomial to the data, and regression may involve quantifying how much each time series (e.g., short channel, accelerometer, polynomial drift) contributes to the measured long channel data, and then subtracting it.

The control device 110, the computing device 220, or the server 230 may be configured to separate the sensor data from each channel into sensor data associated with the subject being at rest or not performing a task (data in a resting state), and sensor data associated with the subject performing a task (data in a task state).

This may be accomplished by considering a timestamp associated with the data. For example, in some embodiments, the task data may include one or more timestamps associated with a subject performing respective one or more actions associated with the task (e.g., task-related timestamps). The sensor data may comprise time series data or time stamp data. The control device 110 (or the server 230 or the computing device 220) may be configured to determine a symptom severity or progress metric by associating one or more subsets of sensor data with corresponding task data based on timing. In some embodiments, the task data may be time stamped according to interactions or events, such as recorded button presses or tasks being displayed to the subject. The task related timestamp may be an additional timestamp of the time-series timestamp that may be associated with the determined sensor data.

In some embodiments, the control device 110, server 230, or computing device 220 (e.g., cognitive performance monitoring application 225) may be configured to timestamp a segment or subset of sensor data with a time stamp associated with the task. For example, the control device 110 or the server 230 may be configured to timestamp sensor data in response to receiving a timestamp instruction from the cognitive performance monitoring application 225.

In some embodiments, the control device 110 provides or streams the sensor data to the computing device 220. When a task or task related event occurs, the cognitive performance monitoring application 225 time stamps the sensor data with a corresponding task or event related time stamp. This may allow the sensor data to be associated with task data that occurs at a particular time, such as when a stimulus event or task event occurs, for example, the subject is shown as an image on a user interface of the computing device, or the subject performs a particular task or action. This may allow for increased ease of data collection and/or improved accuracy of the result analysis, as the biological data that is time stamped and related to human behavior tends to be inherently more informative than pure biological data.

The control device 110, the computing device 220, or the server 230 may be configured to determine a representative response for each channel. For example, the representative response may indicate activation during a task as a function of measurement data or sensor data associated with performing the task. For example, the response of a channel (e.g., hemodynamic response) may be an average of measurement data or sensor data (e.g., a block of data in a task state) associated with performing a task recorded or measured by the channel. Thus, a representative response may be determined for each of the multiple channels under consideration, which may be long channels.

In some embodiments, the feature extraction module 322 extracts features or characteristics from the recorded data or the measured data. For example, the feature extraction module 322 may extract features or characteristics from the representative responses of one or more channels, as described above, such as HbO amplitude or area under the curve, for example. The extracted features may correspond to characteristics or biomarkers associated with cognitive function or performance or cortical activity of the user. The extracted features may be provided as input to a symptom severity (or progress) determination model 327 along with a score, and the symptom severity or progress determination model 327 may provide a symptom severity or progress measure as output. The measure of progression may be indicative of the progression a subject has in treating a symptom of a neurological disorder.

In some embodiments, feature extraction module 322 may be configured to determine feature values that indicate or contain functional connectivity between pairs of optical sensor channels and/or statistical measures of data acquired from the optical sensor channels.

In some embodiments, the symptom severity and/or progression determination model 328 uses eigenvalues derived from sensor data acquired from an optical channel configured to measure activity at the right prefrontal cortex to determine an overall ADHD symptom severity metric.

In some embodiments, the symptom severity and/or progression determination model 328 uses eigenvalues derived from sensor data acquired from an optical channel configured to measure activity at the right prefrontal cortex to determine a primary ADHD core symptom score or metric.

In some embodiments, symptom severity and/or progression determination model 328 uses eigenvalues derived from sensor data acquired from an optical channel configured to measure activity at the medial prefrontal cortex of the subject, and in some cases, at the medial prefrontal cortex toward, overlapping with, or near the left prefrontal cortex of the subject.

In some embodiments, symptom severity and/or progression determination model 328 uses eigenvalues derived from sensor data acquired from an optical channel configured to measure activity at the subject's right prefrontal cortex, left prefrontal cortex, and/or areas overlapping the left prefrontal cortex and medial prefrontal cortex.

In some embodiments, symptom severity and/or progression determination model 328 uses eigenvalues derived from sensor data acquired from an optical channel configured to measure activity at the medial prefrontal cortex of the subject and/or toward, overlapping with, or near the medial prefrontal cortex of the subject.

At 1025, the control device 110, server 230, or computing device 220 outputs a symptom severity or progress metric. For example, the control device 110 or the computing device 220 may output the symptom severity or progress metric by providing the symptom severity or progress metric to the user via the user interface 360, or the control device 110 or the server 230 may transmit the symptom severity or progress metric to, for example, the cognitive performance monitoring application 225 of the computing device 220 of the user, or the computing device of the clinician, the server 230, or the database 240.

In some embodiments, steps 1015, 1020, and/or 1025 may be performed by server 230. In some embodiments, steps 1015, 1020, and/or 1025 may be performed by computing device 220.

A first study is conducted to determine one or more characteristics that may be extracted from the sensor data, which correspond to, or are relatively strong indicators of, a characteristic or biomarker associated with a cognitive function or performance or cortical activity of a user in response to an applied stimulus.

The study involved 16 participants or subjects. A headset or device 100 (or an array 700 of devices 100) is placed on the forehead of each subject. The device 100 extends between the eyebrows and the hairline and from temple to temple. The device 100 is equipped with a conductive sponge (electrode 120) to deliver electrical current to the head of the subject, and in particular the prefrontal cortex. The subject is instructed to close their eyes and lie down (referred to as resting state measurement). The control device 110 is used to deliver stimulation from the electrical stimulation generator 350 to the electrically conductive sponge, and thus to the prefrontal cortex of the subject. In particular, the control device 110 is configured to deliver stimuli of different intensities without current flow between each of the interrupted stimulation sessions. For example, for many subjects, 8 stimulations are performed and sessions are recorded. The first session involves a period of no stimulation (e.g., between 2 and 8 minutes) followed by applying stimulation at a current of 0.25mA for an application period (e.g., between 2 and 8 minutes). The second session involved a period of no stimulation followed by the stimulation applied at a current of 0.5mA for the applied period. The third session involved applying the stimulus at a current of 0.75mA for an application period. The fourth session involved a period of no stimulation followed by applying stimulation at a current of 1.0mA for an application time. The fifth session involved a period of no stimulation followed by applying stimulation at a current of 1.25mA for the applied period. The sixth session involved a period of no stimulation followed by applying stimulation at a current of 1.5mA for the applied period. The seventh session involved a period of no stimulation followed by applying stimulation at a current of 1.75mA for the applied period. The eighth session involved a period of no stimulation followed by applying stimulation at a current of 2.0mA for the applied period. Sensor data is recorded from the sensors of device 100 during each session, thereby generating a sensor data set containing a set of sensor data set for each stimulation current from 0.25mA to 2.0mA in steps of 0.25mA, and corresponding application and rest periods.

A training dataset is generated from the recorded sensor dataset to train a logistic regression model (sigmoid model) -activity determination model 328. The training dataset contains a first set of instances, each of which corresponds to sensor data (i.e., stimulation current of 0.25 mA) recorded during a first stimulation session. The first set of instances is labeled "insufficient," that is, insufficient to elicit adequate response from the brain or generate an insufficient measure of activity. The training dataset contains a second set of instances, each of which corresponds to sensor data (i.e., stimulation current of 2.0 mA) recorded during the eighth stimulation session. The second set of instances is labeled "sufficient," that is, sufficient to elicit a sufficient response from the brain or to generate a sufficient measure of activity. The training data set is used to train the activity determination model 328.

The coefficients of the features are determined by training and testing the activity determination model 328 over 1000 different random samples of the training set and then averaging these values.

Once the model is trained, relevant eigenvalues are extracted from the instances of the remaining sensor data acquired during the second through seventh stimulation sessions to predict the probability that each of the stimulation currents delivers sufficient stimulation to the respective subject. The average results for all subjects are depicted in fig. 15, which is a plot of probability of adequate stimulation versus stimulation current (mA).

The acquired sensor data is processed and signal enhanced by using standard techniques including short channel removal and accelerometer removal as discussed above. Dividing the sensor data into three time blocks; pre-stimulus, during stimulus, and post stimulus. For each time block, the following statistics were calculated for Hbr and Hbo data:

for each fNIRS long channel: (i) average (raw_mean), (ii) standard deviation (raw_std), (iii) average of derivatives (diff_mean), and (iv) standard deviation of derivatives (diff_std). For each pair of fnigs long channels: (i) Correlation coefficient (raw_corrcoef) (note: this is used to determine functional connectivity).

Variations in these statistics between different time blocks are used to generate features. Three changes used are (i) before stimulation to during stimulation (before_to_stimulation_change); (ii) Before stimulation to after stimulation (before_to_after_change); and (ii) from the stimulation period to the stimulation later (stimulation_to_after_change).

All features of each of the long channels were collected and tested for a significant correlation with the stimulus current used. Only features associated with p-values <0.005 are used in the activity determination model 328.

Activity predictors extracted from the sensor data of the examples and used as model inputs that were found to be good are features, shown in table I, along with averages of weights or coefficients determined by model training. Table I further includes values for standard deviation, t-score, and coefficient of variance (CoV) for the feature coefficient values. The features of table I are arranged from the most predictive feature or features that are most indicative of the activity level of the subject to the least predictive features in the set of features. All features with positive coefficient values are positively correlated with the activity measure, and features with negative coefficient values ("S2D 6 hbr S3D8 hbr raw corr coeff during to after change") are negatively correlated with the activity measure. According to the results of table I, the feature "S3D4 HbR raw std before to after change" is the most indicative of whether sufficient stimulus is applied, and is a suitable and reliable feature for predicting the probability of whether sufficient stimulus is applied. The feature "S3D hbo S4D8 hbo raw corr coef before to during change" is a high performance feature that indicates whether sufficient stimulus has been applied to the subject, and a suitable and reliable feature for predicting the probability of whether sufficient stimulus has been applied.

Any one or any combination of the features of table I may be used to predict the probability of whether sufficient stimulus is applied. Such features may be used to train the activity determination model 328. Once trained, the activity determination model 328 will be an activity measure of the subject by providing values of those features extracted from the sensor data as input to the activity determination model.

	coefficient_mean	coefficient_std	tscore	CoV
					S3_D4 hbr_raw_std_before_to_after_change	0.784002	0.200759	123.493097	0.256069
S3_D8 hbo_S4_D8 hbo_raw_corrcoef_before_to_during_change	0.721617	0.116793	195.383673	0.161850
					S4_D8 hbo_S5_D8 hbo_raw_corrcoef_before_to_during_change	0.560612	0.113208	156.597854	0.201936
S4_D8 hbr_diff_std_during_to_after_change	0.511265	0.112889	143.217219	0.220803
					S4_D6 hbo_diff_std_before_to_after_change	0.480229	0.090511	167.783092	0.188474
S2_D4 hbo_S3_D4 hbo_diff_corrcoef_before_to_during_change	0.460770	0.136550	106.706664	0.296352
					S2_D6 hbr_S3-D6 hbr_diff_corrcoer_beofre_to_during_change	0.444764	0.114132	120.075151	0.263358
S4_D6 hbr_diff_std_before_to_after_change	0.388282	0.095078	129.141378	0.244869
					S4_D8 hbo_S5-D8 hbr_raw_corrcoef_before_to_during_change	0.366713	0.105675	109.736839	0.288169
S2_D6 hbr_S3-D8 hbr_raw_corrcoef_during_to_after_change	-0.342503	0.163320	-66.317003	0.476843
					S2_D4 hbo_S4_D6 hbo_diff_corrcoef_before_to_during_change	0.327312	0.101647	101.828010	0.310551
S3_D6 hbo_diff_std_before_to_after_change	0.289173	0.123267	74.183886	0.426276
					S3_D8 hbo_S5_D8 hbo_diff_corrcoef_before_to_during_change	0.284035	0.064078	140.173335	0.225598
S4_D6 hbr_S4_D8 hbr_diff_corrcoef_during_to_after_change	0.280692	0.084771	104.708465	0.302008
					S4_D6 hbo_S4_D8 hbo_diff_corrcoef_before_to_during_change	0.229936	0.067740	107.339713	0.294605
S4_D8 hbr_diff_std_before_to_after_change	0.221947	0.073822	95.074540	0.332610
					S2_D4 hbo_S3_D8 hbo_diff_corrcoef_before_to_during_change	0.206023	0.091213	71.426433	0.442732
S3_D6 hbr_diff_std_before_to_after_change	0.179699	0.147208	38.602602	0.819188
					S2_D4 hbo_diff_std_before_to_after_change	0.165402	0.095532	54.750828	0.577576

TABLE I

Table II below provides a description of some terms used for the features of table 1. Although all features in table I are not included in table II, it should be understood that the description provided as an explanation of terms used to define the features may apply equally to other features of table I.

Table II

With respect to the feature "S3D4 HbR raw std before to after", which is a measure of the change in standard deviation of deoxygenated blood over the channel around the stimulation cathode (negative stimulation electrode) from before to after the stimulation was applied, it was found to have a positive correlation with the activity measure. This may be due to the increased stimulus, resulting in an increased negative current at the cathode. Negative current at the cathode may result in reduced activity around the cathode and thus increased deoxygenated blood activity.

Regarding the features "S4D8 HbO S5D8 HbO raw corr coef before to during" and "S3D8 HbO S4D8 HbO raw corr coef before to during", both of these features indicate functional connectivity of oxygenated blood in the brain region at the midpoint of the anode (pos) and cathode (neg) of the stimulation electrode, and were found to have a positive correlation with the activity measure. The increase in functional connectivity expresses an increased synchronous activity occurring at a point between the two electrodes when the current moves from the anode to the cathode. This is evident when comparing the functional connectivity before stimulation with the functional connectivity during stimulation and is positively correlated with the stimulation intensity, showing that the increase in connectivity may be due to an increase in activation caused by stimulation.

Table II referring to fig. 7a and 7b, the location of the sensor detector pairs mentioned in tables I and II can be easily understood.

In some embodiments, the features for the activity determination model 328 are extracted from sensor data obtained from the left prefrontal cortex of the subject. For example, the device 100 may be positioned on the head of a subject such that a first sensor module (comprising sensor S2 and partitions D3 and D4) and an adjacent second sensor module (comprising sensor S3 and detectors D5 and D6) are positioned to measure or record data from the left prefrontal cortex of the subject. For example, sensor data acquired from the left prefrontal cortex of a subject may include sensor data determined from one or more of the following: a long channel between a first sensor (S2) and a first detector (D4) of a first sensor module, a long channel between a second sensor (S3) and a second detector (D6) of a second adjacent sensor module, a long channel between a first sensor (S2) of a first sensor module and a second detector (D6) of a second sensor module, and a long channel between a second sensor (S3) of a second sensor module and a first detector (D4) of a first sensor module.

In some embodiments, the features for the activity determination model 328 are extracted from sensor data obtained from the medial prefrontal cortex of the subject. For example, the device 100 may be positioned on the head of a subject such that a third sensor module (comprising sensor S4 and partitions D7 and D8) and an adjacent fourth sensor module (comprising sensor S5 and detectors D9 and D10) are positioned to measure or record data from the medial prefrontal cortex of the subject. For example, sensor data acquired from the medial prefrontal cortex of a subject may include sensor data determined from one or more of: a long channel between the first sensor (S4) and the first detector (D8) of the third sensor module and a long channel between the second sensor (S5) of the fourth adjacent sensor module and the first detector (D8) of the third sensor module.

In some embodiments, the features for the activity determination model 328 are extracted from sensor data obtained from the left prefrontal cortex, the medial prefrontal cortex, and/or the boundary between the medial prefrontal lobe and the left prefrontal lobe of the subject. In some embodiments, the sensor data may be obtained from one or more of the following: a long channel between the second sensor (S3) of the second sensor module and the first detector (D8) of the third sensor module, and a long channel between the first sensor (S4) of the third sensor module and the second detector (D6) of the second sensor module. In some embodiments, sensor data may be acquired from one or more of channels S2D4, S2D6, S3D4, S3D6 and one or more of channels S4D8 and S5D8, and optionally from one or more of S3D8 and S4D 6.

As shown in fig. 7b, the different regions have some overlap. For example, S4D6 and S3D8 are boundary channels between the medial and left forehead lobes, and thus may be considered part of either/both regions. This is true for all places where the circles in fig. 7b intersect.

A second study is conducted to determine one or more characteristics that may be extracted from the sensor data and/or task data, the one or more characteristics corresponding to, or being a relatively strong indicator of, a characteristic or biomarker of a subject's cognitive function or performance or cortical activity when the subject is experiencing or undertaking a particular task.

The study involved 10 participants or subjects with ADHD. The headphones or devices 100 (or the array 700 of devices 100) are placed between the eyebrows and hairline of each subject's forehead and from temple to temple. The headset or device 100 is free of conductive sponge and no stimulus is applied. The device 100 is used only to record brain responses or cortical activity while the subject is performing a task or test using the optical sensor 130, which contains the fNIRS sensor in the present study.

All subjects completed the ADHD rating scale questionnaire. The ADHD rating scale questionnaire comprises a set of 18 questions that are typically given by a psychiatrist to aid in diagnosing ADHD. The specific questions are designed to evaluate different symptoms of ADHD and to help identify the type and/or extent of ADHD that a person has, be it inattentive, multifactorial or combination. The scale is a self-reporting scale in which patients can dose out their frequency of common symptoms and their severity of symptoms very frequently from time to time. A set of determined symptom severity or progress scores is assigned to the subject using answers to the questionnaire provided by the subject. For example, each set of determined progress scores includes a total or overall ADHD rating scale score, an ADHD core symptom score, a inattention score, an hyperactivity score, and an impulsivity score.

The subject is asked to perform a first cognitive performance (executive function) task called "Go-No/Go task", which is a well known test designed to test the impulse control and attention of the subject. The subject is also required to perform a second performance (executive function) task called an "N-back task," which is a well-known test designed to test the subject's working memory and attention.

In this study, for each condition of the Go-No/Go and N-back tasks, the following metrics were calculated using the recorded button press information:

a. accuracy-average (accuracy_mean)

b. Accuracy-standard deviation (accuracy_std)

c. Reaction time-average (reaction_time_mean)

d. Reaction time-standard deviation (reaction_time_std)

e. Missing error-average (omision_error_mean)

f. Missing error-standard deviation (omision_error_std)

g. Misclassification error-average (commitment_error_mean)

h. Misclassification error-standard deviation (commitment_error_std)

A task score group for each task performed is generated for each subject. For example, the task scores for the first and second performance tasks include values for reaction time, accuracy, omission errors, and misclassification errors.

For each subject, a first set of sensor data is recorded when the subject is engaged in a first performance task and a second set of sensor data is recorded when the subject is engaged in a second performance task.

As discussed above, the sensor data is processed and signal enhanced, including short channel removal and accelerometer removal.

All duplicate sensor data were averaged for each experimental condition. For Go No/Go, the conditions are: (go, gooogo), and for N-Back, the conditions are: (0-back, 1-back, 2-back). The sensor data for each region of interest is averaged. In other words, data from a particular sensor channel is grouped into regions, as discussed in more detail below. For each region of interest, the following statistics of Hbr and Hbo data were calculated:

a. Average value (raw_mean)

b. Standard deviation (raw_std)

c. Maximum value (raw_max)

d. Minimum value (raw_min)

e. Fitting coefficient of general linear model (theta)

A training dataset of instances is generated from the sensor data, the task scoring group, and the determined progress scores (tags). The training dataset is used to train a linear model-symptom severity (or progress) determination model 327. The symptom severity determination model 327 is configured to receive as input features from sensor data acquired while performing the first performance task, features from sensor data acquired while performing the second performance task, task scores from the first performance task, and task scores from the second performance task, and to provide as output progress or symptom severity metrics, or symptom severity metrics for each type of symptom (e.g., overall ADHD score, ADHD core symptom score, inattention score, hyperactivity score, and impulsive score).

Symptom severity (or progression) determination model 327 is trained on 75% of the data of the training dataset and tested on the remaining 25%. This was done 1000 times for different samples of training and test data. All features of each of the long channel and task features were collected and tested for significant correlation with symptom severity metrics. Only the features of the first 10 of all available features are used in the symptom severity (or progression) determination model 327.

Some high performance features extracted from the sensor data sets and/or task score sets of the examples and used as model inputs are shown in tables III-VII below, along with averages of weights or coefficients determined by model training. The table further includes standard deviation, t-score, coefficient of variance (CoV) of coefficient values, and values of p-values of features.

Referring again to fig. 7a and 7b, the regions "left_3", "left_2", "left_1", "mid", "right_1", "right_2", "right_3" mentioned in the following table refer to:

·left_3＝S1D2、S2D2、S1D4

·left_2＝S2D4、S3D4、S2D6

·left_1＝S3D6、S4D6、S3D8

·mid＝S4D8、S4D10、S5D8、S5D10

·right_1＝S6D10、S5D12、S6D12

·right_2＝S7D12、S6D14、S7D14

·right_3＝S8D14、S7D16、S8D16

	coefficient_mean	coefficient_std	tscore	CoV	pvalue
						1-back_reaction_time_std	2.244548	1.194139	59.439336	0.532018	0.000000
1_back_right_2_hbo_raw_mean	-2.101908	1.252190	-53.081523	0.595740	0.000000
						2-back_omission_errors_std	1.384634	1.028096	42.589403	0.742503	0.000000
gonogo_left_3_hbr_raw_mean	1.093182	0.871831	39.651570	0.797516	0.000000
						1_back_right_2_hbo_raw_min	-0.755032	0.499386	-47.811100	0.661411	0.000000
gonogo_lett_1_hbo_theta	-0.575179	0.627704	-28.976638	1.091320	0.000000
						2_back_right_3_hbr_raw_mean	0.508484	1.280569	12.556668	2.518405	0.000000
1_back_left_1hbo_theta	-0.320053	0.607091	-16.671248	1.896845	0.000000
						gonogo_left_3hbr_raw_max	0.255609	0.820641	9.849689	3.210536	0.000000
gonogo_right_1_hbo_raw_max	0.094094	0.773660	3.846036	8.222173	0.000128

table III

Table III lists the first ten features determined to be good predictors of overall ADHD symptom severity measures, with the first ("1-back reaction time std") and third features extracted from the task score data positively correlated with the symptom severity measure, the fourth, seventh, ninth, and tenth features (all extracted from the sensor data) positively correlated with the symptom severity measure, and the four other features extracted from the sensor data negatively correlated with the overall ADHD symptom severity measure. Thus, features from the sensor data and task data are used as inputs to the trained symptom severity determination model 327 to determine the overall symptom severity measure of ADHD for the subject. Among the available task data, the response time and omission measures have the strongest (positive) correlation with the overall ADHD symptom severity measure. Notably, among the available sensor data, the channel configured to measure activity at the right prefrontal cortex (S7D 12, S6D14, S7D 14) has the strongest (negative) correlation with the overall ADHD symptom severity measure.

	coefficient_mean	coefficient_std	tscore	CoV	pvalue
						1_back_right_2_hbo_raw_mean	-0.956945	0.533121	-56.762457	0.557107	0.000000
1_back_mid_hbr_theta	0.871165	0.606536	45.419661	0.696235	0.000000
						gonogo_left_2_hbr_raw_std	-0.834898	0.592958	-44.525558	0.710216	0.000000
1_back_left_1_hbo_raw_mean	-0.653260	0.836472	-24.696471	1.280457	0.000000
						1_back_mid_hbo_theta	0.631417	0.926957	21.540536	1.468059	0.000000
gonogo_mid_hbo_theta	0.613029	0.750131	25.843078	1.223646	0.000000
						gonogo_left_2_hbr_raw_mean	-0.486064	0.442470	-34.738397	0.910312	0.000000
gonogo_left_2_hbr_raw_max	-0.455532	0.295332	-48.776214	0.648324	0.000000
						gonogo_right_1_hbo_raw_max	0.338631	0.688645	15.550049	2.033613	0.000000
1_back_right_2_hbo_raw_min	0.150772	0.494621	9.639376	3.280583	0.000000

Table IV

Table IV lists the first ten features determined to be good predictors of primary ADHD core symptom scores, with the first, third, fourth, seventh, and eighth features extracted from the sensor data being inversely related to the primary ADHD core symptom scores and the other features extracted from the sensor data being positively related to the primary ADHD core symptom scores. Thus, only features from the sensor data are used as input to the trained symptom severity determination model 327 to determine the subject's primary ADHD core symptom score. Notably, among the available sensor data, the channel configured to measure activity at the right prefrontal cortex (S7D 12, S6D14, S7D 14) has the strongest (negative) correlation with the primary ADHD core symptom score.

	coefficient_mean	coefficient_std	tscore	CoV	pvalue
						2_back_right_1_hbo_theta	0.768204	0.371564	65.379610	0.483679	0.000000
1-back_reaction_time_std	0.551849	0.353224	49.404889	0.640074	0.000000
						gonogo_lefi_3hbr_raw_max	0.541322	0.309392	55.328190	0.571549	0.000000
gonogo_left_1-hbr_raw_max	-0.348583	0.302418	-36.450096	0.867564	0.000000
						1-back_omission_errors_mean	0.253324	0.170113	47.091058	0.671524	0.000000
2_back_mid_hbr_theta	-0.149754	0.235921	-20.072959	1.575392	0.000000
						2_back_left_1_hbo_theta	0.100341	0.231974	13.678608	2.311842	0.000000
gonogo_left_3_hbr_raw_mean	0.081239	0.293446	8.754623	3.612123	0.000000
						2_back_right_1_hbo_raw_mean	-0.035858	0.208646	-5.434784	5.818590	0.000000
1-back_omission_errors_std	0.035267	0.273057	4.084234	7.742646	0.000048

Table V

Table V lists the first ten features of a good predictor of symptoms determined to be inattentive, with the first, third, seventh, and eighth features (all extracted from the sensor data), the second, fifth, and tenth features (extracted from the task scoring data) being positively correlated with the symptoms of inattention, and the other features all extracted from the sensor data being negatively correlated with the symptoms of inattention. Thus, features from both sensor data and task data are used as inputs to a trained symptom severity determination model 327 to determine a subject's inattention severity measure. Among the available task data, the response time and omission error metrics have the strongest (positive) correlation with the symptoms of inattention. Notably, among the available sensor data, the channel configured to measure activity at the medial prefrontal cortex of the subject, and in some cases, the channel of activity at the medial prefrontal cortex that overlaps or is proximate to the right prefrontal cortex of the subject (S6D 10, S5D12, S6D 12) has the strongest correlation with the inattention symptom.

	coefficient_mean	coefficient_std	tscore	CoV	pvalue
						1_back_right_2hbo_raw_mean	-1.188398	0.309523	-121.413931	0.260454	0.000000
gonogo_left_1_hbo_theta	-0.937263	0.501494	-59.101111	0.535062	0.000000
						1_back_left_1_hbo_raw_min	0.561317	0.450860	39.370139	0.803217	0.000000
1_back_left_2_hbo_theta	-0.448590	0.294416	-48.182383	0.656314	0.000000
						1_back_right_2_hbo_raw_min	-0.277226	0.218123	-40.191331	0.786806	0.000000
gonogo_mid_hbo_theta	-0.185172	0.519825	-11.264635	2.807261	0.000000
						gonogo_right_1_hbo_raw_max	0.175440	0.420896	13.181156	2.399090	0.000000
2_back_left_2_hbr_raw_max	-0.145495	0.177432	-25.930891	1.219502	0.000000
						2_back_left_2_hbr_raw_mean	0.102297	0.320478	10.093974	3.132837	0.000000
1_back_left_1_hbo_theta	-0.059325	0.289072	-6.489749	4.872727	0.000000

Table VI

Table VI lists the first ten features determined to be good predictors of hyperactivity, with the first, second, fourth, fifth, sixth, eighth, and tenth features (all extracted from the sensor data) being negatively correlated with hyperactivity, and the other features also all extracted from the sensor data being positively correlated with hyperactivity. Thus, features from the sensor data are used only as input to the trained symptom severity determination model 327 to determine the subject's hyperactivity severity measure. Notably, among the available sensor data, the channel configured to measure activity at the right (S7D 12, S6D14, S7D 14) and left (S2D 4, S3D4, S2D 6) frontal cortex and/or the region overlapping the left and medial frontal cortex has the strongest (negative) correlation with the hyperactivity severity measure.

	coefficient_mean	coefficient_std	tscore	CoV	pvalue
						2-back_reaction_time_std	1.475761	0.457687	101.964176	0.310136	0.000000
1_back_right_1_hbo_raw_mean	1.071202	0.457760	74.000324	0.427333	0.000000
						2_back_right_3_hbo_raw_max	-0.841258	0.370674	-71.769004	0.440619	0.000000
2_back_left_3_hbr_raw_mean	0.654955	0.227334	91.105948	0.347099	0.000000
						2_back_right_3hbr_raw_mean	0.592720	0.296899	63.130766	0.500909	0.000000
gonogo_left_3_hbr_raw_mean	0.474359	0.338177	44.357130	0.712913	0.000000
						2_back_left_3_hbr_raw_max	0.254784	0.195714	41.167227	0.768154	0.000000
1_back_left_1_hbo_theta	0.153493	0.352429	13.772648	2.296056	0.000000
						2_back_right_3_hbr_theta	0.100848	0.345465	9.231256	3.425620	0.000000
1_back_right_2_hbo_raw_mean	-0.088005	0.332657	-8.365900	3.779961	0.000000

Table VII

Table VII lists the first ten features determined to be good predictors of impulse symptoms, with the first feature (extracted from the task scoring data), the second, fourth, fifth, sixth, eighth, and ninth features (all extracted from the sensor data) being positively correlated with impulse symptoms, and the other features also all extracted from the sensor data being positively correlated with impulses. Thus, features from both sensor data and task data are used as inputs to a trained symptom severity determination model 327 to determine a measure of impulse severity of a subject. Among the available task data, the response time has the strongest (positive) correlation with impulse symptoms. Notably, among the available sensor data, the channel configured to measure activity at the medial prefrontal cortex of the subject, and in some cases, the channel of activity at the medial prefrontal cortex that overlaps or is proximate to the right prefrontal cortex of the subject (S6D 10, S5D12, S6D 12) has the strongest correlation with impulse symptoms.

Table VIII below provides a description of some terms used for the features of tables III to VII. Although all of the features of tables III through VII are not included in table VIII, it should be understood that the description provided as an explanation of the terms used to define the features may apply equally to other features of those tables.

Table VIII

Referring to fig. 16 a-16 e, a plot of actual symptom severity metrics/scores (as per ADHD rating scale questionnaire score) versus predicted symptom severity metrics/scores (as predicted by symptom severity determination model 327) for each of the categories of overall ADHD score (fig. 16 a), primary ADHD core symptom score (fig. 16 b), inattention (fig. 16 c), hyperactivity (fig. 16 a), impulsivity (fig. 16 e) is shown.

It will be appreciated by persons skilled in the art that numerous variations and/or modifications may be made to the above-described embodiments without departing from the broad scope of the disclosure. The present embodiments are, therefore, to be considered in all respects as illustrative and not restrictive.

Claims

1. A system for controlling delivery of electrical stimulation to a subject, the system comprising:

a control device configured to:

transmitting stimulation instructions to an electrical stimulation generator to cause the electrical stimulation generator to deliver transcranial electrical stimulation to one or more electrodes arranged to be positioned in proximity to a target area of the subject's brain, wherein the stimulation instructions comprise at least one stimulation parameter value;

Receiving sensor data from one or more optical sensors arranged to be positioned in proximity to the target area; and

transmitting updated stimulation instructions containing one or more updated stimulation parameter values to the electrical stimulation generator to cause the electrical stimulation generator to modify one or more characteristics of the stimulation;

wherein the system is further configured to:

analyzing the sensor data to determine an activity metric; and is also provided with

The one or more updated stimulation parameter values are determined based on the determined activity metrics.

2. The system of claim 1, wherein the sensor data comprises pre-stimulus sensor data acquired prior to the delivering of stimulation to the one or more electrodes, during-stimulus sensor data acquired while the stimulation is delivered to the one or more electrodes, and post-stimulus sensor data acquired after the stimulation is delivered to the one or more electrodes, and wherein the system is configured to:

determining pre-stimulus, during-stimulus, and post-stimulus values of one or more features from the pre-stimulus sensor data, the during-stimulus sensor data, and the post-stimulus sensor data, respectively;

For each of the one or more features, determining a relative change in the value of the feature from (i) the pre-stimulus value to the post-stimulus value, (ii) the pre-stimulus value to the during-stimulus value, and (iii) the during-stimulus value to the post-stimulus value;

providing the relative change in the values of the one or more features and the stimulation parameter values to an activity determination model; and is also provided with

The activity metric is determined by the activity determination model.

3. The system of claim 1 or 2, wherein the activity metric indicates that sufficient stimulus has been delivered to the subject.

4. The system of any of the preceding claims, wherein the one or more features comprise: functional connectivity between pairs of optical sensor channels and/or statistical measures of data acquired from the optical sensor channels.

5. The system of any one of the preceding claims, wherein the one or more features are extracted from sensor data obtained from stimulation electrodes surrounding the one or more electrodes and/or optical channel pairs between stimulation electrodes of the one or more electrodes.

6. The system of any one of the preceding claims, wherein the sensor data comprises data acquired from left prefrontal cortex of the subject, medial prefrontal cortex of the subject, and/or a boundary region between medial and left prefrontal lobes of the subject.

7. The system of any one of the preceding claims, wherein the sensor data comprises data acquired from the subject's right prefrontal cortex, the subject's medial prefrontal cortex, and/or a boundary region between the subject's medial and right prefrontal lobes.

8. The system of any of the preceding claims, wherein the system configured to determine the one or more updated stimulation parameter values based on the determined activity metric comprises:

in response to determining that the activity metric is less than a threshold value, increasing the stimulation parameter value and reapplying the stimulation at the increased stimulation parameter value; and

in response to determining that the activity metric has reached the threshold value, the stimulation parameter value is determined as a user-specific calibrated stimulation parameter.

9. The system of any one of the preceding claims, further comprising a head-mountable array carrying the optical sensor, and wherein the optical sensor is a functional near infrared spectroscopy sensor fNIRS.

10. The system of claim 9, wherein the head-mountable array further carries the one or more electrodes.

11. The system of any one of the preceding claims, further comprising:

an optical sensor module coupled to the one or more optical sensors, the optical sensor module configured to cause light to be emitted from respective emitters of the one or more optical sensors and to receive signals indicative of reflected light from respective detectors of the one or more optical sensors, wherein the signals are indicative of a cerebral hemodynamic response associated with neural activity in the target area;

wherein the optical sensor module is configured to provide the sensor data to the control device, the sensor data being based on the signals received from the respective one or more sensors; and is also provided with

Wherein the optical sensor module is configured to operate in response to instructions received from the control device.

12. The system of claim 11, wherein the control device is configured to cause the optical sensor module to turn on and off the emitters of the one or more optical sensors at a relatively high frequency to produce a lock-in amplifier effect to improve a signal-to-noise ratio, SNR, of the respective detected reflected light signals.

13. The system of claim 11 or 12, wherein each of the one or more optical sensors comprises an emitter and first and second detectors and forms two detector channels, and wherein the control device is configured to demodulate the signals of the detector channels of each of the optical sensors.

14. The system of claim 13, wherein the sensor data from a plurality of functional channels is downsampled.

15. The system of any one of the preceding claims, wherein the sensor data comprises measurement data including one or more of: (i) Reflected light intensities at two different wavelengths detected by the one or more sensors; (ii) an oxygenated hemoglobin HbO concentration; (iii) deoxyhemoglobin HbR concentration; (iv) total hemoglobin ThB concentration; and (v) measuring the relative change in any of (i) to (iv).

16. The system according to any one of the preceding claims, wherein the control device is configured to cause the stimulus generator to supply one or more of: (i) transcranial direct current stimulation tDCS; (ii) transcranial alternating current stimulation of tcacs; (iii) transcranial random noise stimulated tRNS; (iv) transcranial pulsed current stimulation tcs; (v) transcranial random noise stimulated tRNS; and (vi) and oscillating transcranial direct current to stimulate otDCS.

17. The system of any one of the preceding claims, wherein the stimulation instructions comprise one or more of: (i) a voltage; (ii) an electrical current; (iii) frequency; (iv) duration; and (v) offset.

18. The system according to any one of the preceding claims, wherein the control device is configured to cause the electrical stimulation generator to deliver relatively short electrical stimulation pulses to the one or more electrodes and to cause the optical sensor module to record reflected signals from the respective sensors after the relatively short electrical stimulation pulses have been delivered to the one or more electrodes.

19. The system of any one of claims 1 to 17, wherein the control device is configured to cause the electrical stimulation generator to deliver a relatively long electrical stimulation session to the one or more electrodes and to cause the optical sensor module to record reflected signals from the respective sensors while the electrical stimulation is delivered to the one or more electrodes.

20. The system according to any one of the preceding claims, wherein the control device is configured to receive recorded data from the one or more optical sensors before, during and/or after the delivery of the electrical stimulation to the one or more electrodes.

21. The system of any one of the preceding claims, wherein the control device is configured to continuously monitor brain activity of the subject.

22. The system of any of the preceding claims, wherein the control device is configured to initiate a session in response to instructions received from a cognitive performance monitoring application deployed on a computing device in communication with the control device.

23. The system of any of the preceding claims, further comprising a computing device or server in communication with the control device over a communication network, and wherein the computing device or server is configured to:

receiving the sensor data from the control device;

analyzing the sensor data to determine the activity metric;

determining the one or more updated stimulation parameter values based on the determined activity metrics; and

transmitting the updated stimulation parameter value to the control device.

24. The system of claim 23, wherein the control device is configured to transmit the sensor data to a computing device or server for processing and to receive the updated stimulation parameter values from the respective computing device or server.

25. A system for determining stimulation parameters to control delivery of electrical stimulation to a subject, the system comprising:

one or more processors; and

a memory containing executable instructions that, when executed by the one or more processors, cause the system to:

receiving sensor data from a control device, the sensor data originating from one or more optical sensors positioned near a target area of a subject's head;

analyzing the sensor data to determine an activity metric;

determining one or more updated stimulation parameter values based on the determined activity metrics, wherein the updated stimulation parameter values are indicative of characteristics of transcranial electrical stimulation to be delivered to the subject by an electrical stimulation generator under control of the control device; and is also provided with

Transmitting the updated stimulation parameter value to the control device.

26. The system of any of the preceding claims, further comprising an activity determination model configured to receive as input features extracted from the sensor data and to provide as output the activity metric.

27. The system of any one of the preceding claims, wherein the control device is configured to allow selection of a subset of the electrodes to be used in applying stimulation, thereby customizing the control device to fit a head size of a particular subject.

28. The system of claim 27, wherein the control device is configured to receive a head size indication from the subject via a user interface and determine the subset of electrodes to use based on the head size indication.

29. The system of claim 27, wherein the control device is configured to:

delivering at least a first test signal to each of one or more subsets of electrodes of the array;

analyzing the test responses detected by the respective sensor modules;

determining a suitable subset of electrodes for the subject based on the detected test response; and

the appropriate subset of electrodes is selected for applying the stimulus to the head of the subject.

30. The system of any one of the preceding claims when directly or indirectly dependent on claim 9, further comprising a positioning feedback module configured to assist the subject in correctly placing the array relative to the subject's head.

31. The system of claim 30, wherein the positioning feedback module is configured to:

determining one or more images of the subject wearing the array of control devices;

Detecting a position of one or more facial features of the subject within the one or more images;

detecting a position of the array within the one or more images relative to the determined facial features;

comparing the determined position of the array to a target position; and is also provided with

In response to determining that the location of the array falls within an acceptable range, determining that the array is properly positioned; and is also provided with

In response to determining that the position of the array falls within an acceptable range, determining that the array is incorrectly placed, feedback is provided to the subject via a user interface to assist them in repositioning the array to achieve the target position.

32. A method for controlling delivery of electrical stimulation to a subject, the method comprising:

receiving sensor data from one or more optical sensors arranged to be positioned in proximity to the target area;

Analyzing the sensor data to determine an activity metric;

determining one or more updated stimulation parameter values based on the determined activity metrics; and

transmitting updated stimulation instructions containing the one or more updated stimulation parameter values to the electrical stimulation generator to cause the electrical stimulation generator to modify one or more characteristics of the stimulation.

33. A method for determining stimulation parameters to control delivery of electrical stimulation to a subject, the method comprising:

analyzing the sensor data to determine an activity metric;

determining one or more updated stimulation parameter values based on the determined activity metrics, wherein the updated stimulation parameter values are indicative of characteristics of transcranial electrical stimulation to be delivered to the subject by an electrical stimulation generator under control of the control device; and

transmitting the updated stimulation parameter value to the control device.

34. A non-transitory machine-readable medium storing instructions which, when executed by one or more processors, cause a computing device to perform the method of claim 32 or claim 33.

35. A system for inferring symptom severity and/or behavioral progression in a subject undergoing treatment for one or more symptoms of a neurological disorder, the system comprising:

a control device configured to receive sensor data from one or more optical sensors arranged to be positioned near a target area of the subject's brain;

wherein the system is further configured to:

determining task data, the task data comprising one or more scores associated with performance of the subject in undertaking one or more respective tasks;

determining a symptom severity and/or progression measure based on the sensor data and the task data; and is also provided with

Outputting the symptom severity and/or progression measure.

36. The system of claim 35, wherein the symptom severity and/or progression metric comprises a plurality of scores, each score indicating a severity of a behavior or experience associated with the neurological disorder.

37. The system of claim 35 or 36, wherein the neurological disorder is ADHD and the symptom severity and/or progression measure comprises a score of one or more of: (i) an overall ADHD rating scale score, (ii) an ADHD core symptom score, (iii) a inattention score, (iv) a hyperactivity score, and (v) an impulsivity score.

38. The system of any one of claims 35 to 37, wherein the system comprises a symptom severity and/or progress determination model configured to determine the symptom severity and/or progress metric based on the task data and the sensor data, wherein the symptom severity and/or progress determination model has been trained using data derived from a clinical population.

39. The system of claim 38, wherein the system comprises a feature extraction module configured to determine one or more feature values from the sensor data and provide the feature values to the symptom severity and/or progress determination model.

40. The system of claim 39, wherein the one or more characteristic values are indicative of functional connectivity between pairs of optical sensor channels and/or statistical measures of data acquired from the optical sensor channels.

41. The system of claim 39 or 40, wherein one or more eigenvalues derived from sensor data acquired from an optical channel configured to measure activity at the prefrontal cortex of the right side of the subject are provided to the symptom severity and/or progression determination model to determine an overall ADHD symptom severity measure and/or ADHD core symptom severity measure.

42. The system according to any one of claims 39 to 41, wherein one or more eigenvalues derived from sensor data acquired from an optical channel configured to measure activity at the subject's right prefrontal cortex, left prefrontal cortex and/or region overlapping with the left prefrontal cortex and medial prefrontal cortex are provided to the symptom severity and/or progression determination model to determine an ADHD core symptom severity measure.

43. The system of any one of claims 39 to 42, wherein one or more eigenvalues derived from sensor data acquired from an optical channel configured to measure activity at and/or towards the left prefrontal cortex of the subject, at an medial prefrontal cortex overlapping or close to the left prefrontal cortex of the subject are provided to the symptom severity and/or progression determination model to determine a concentration less severity measure and/or an impulse severity measure.

44. A system according to any one of claims 39 to 43 wherein one or more characteristic values derived from the reaction time metrics and/or error omission metrics of the task data can be provided to the symptom severity and/or progress determination model to determine an overall ADHD symptom severity metric, impulse severity metric, and/or inattention severity metric.

45. The system of any one of claims 35 to 44, further comprising a head-mountable array carrying the optical sensor, and wherein the optical sensor is a functional near infrared spectroscopy sensor fNIRS.

46. The system of any one of claims 35 to 45, further comprising:

47. The system of claim 46, wherein the control device is configured to cause the optical sensor module to turn on and off the emitters of the one or more optical sensors at a relatively high frequency to produce a lock-in amplifier effect to improve a signal-to-noise ratio, SNR, of the respective detected reflected light signals.

48. The system of claim 46 or 47, wherein each of the one or more optical sensors includes an emitter and a first detector and a second detector, and forms two detector channels, and wherein the control device is configured to demodulate the signals of the detector channels of each of the optical sensors.

49. The system of claim 48, wherein the sensor data from a plurality of functional channels is downsampled.

50. The system of any one of claims 35 to 49, wherein the sensor data comprises measurement data including one or more of: (i) Reflected light intensities at two different wavelengths detected by the one or more sensors; (ii) an oxygenated hemoglobin HbO concentration; (iii) deoxyhemoglobin HbR concentration; (iv) total hemoglobin ThB concentration; and (v) measuring the relative change in any of (i) to (iv).

51. The system of any one of claims 35 to 50, wherein the system is configured to determine a quality metric indicative of a quality of each detector channel of the one or more optical sensors, and in response to the quality metric falling below a quality threshold, to exclude sensor data from the respective detector channel when determining the symptom severity and/or progress metric.

52. The system of any of claims 35 to 51, wherein the system is configured to determine a subset of the sensor data based on time-stamped sensor data and time-stamped task data, the subset of sensor data comprising sensor data associated with a task acquired when the subject performs the task.

53. The system of claim 52, wherein the system comprises a feature extraction module configured to extract one or more features from sensor data associated with the task, and wherein determining the symptom severity and/or progress metric based on the sensor data and the task data comprises determining the symptom severity and/or progress metric based on the one or more features and task data.

54. The system in accordance with any one of claims 35 to 53, wherein the system comprises a cognitive performance monitoring application configured to:

evaluating the subject performing one or more specific tasks;

assigning one or more task scores to the subject based on the performance of the subject while undertaking the task, wherein the task data includes the one or more task scores.

55. The system of any one of claims 35 to 54, wherein the control device is configured to send stimulation instructions to an electrical stimulation generator to cause the electrical stimulation generator to deliver transcranial electrical stimulation to one or more electrodes arranged to be positioned in proximity to the target area of the brain of the subject.

56. The system of any one of claims 35 to 55, comprising a head-mountable array carrying the one or more electrodes.

57. The system of any one of claims 35 to 56, further comprising a computing device or server in communication with the control device over a communication network, and wherein the computing device or server is configured to:

receiving the sensor data from the control device;

determining the task data;

determining the symptom severity and/or progression measure based on the sensor data and the task data; and is also provided with

Outputting the symptom severity and/or progression measure.

58. The system of claim 57, wherein the control device is configured to transmit the sensor data to the computing device or server.

59. A system according to claim 55 or any one of claims 56 to 58 when dependent directly or indirectly on claim 55, wherein the control means is configured to allow selection of a subset of the electrodes of the array to be used in applying stimulation, thereby customizing the control means to suit the head size of a particular subject.

60. The system of claim 59, wherein the control device is configured to receive a head size indication from the subject via a user interface and determine the subset of electrodes to use based on the head size indication.

61. The system of claim 60, wherein the control device is configured to:

analyzing the test responses detected by the respective sensor modules;

62. A system according to claim 56 or any one of claims 57 to 61 when dependent directly or indirectly on claim 56, further comprising a positioning feedback module configured to assist the subject in correctly placing the array relative to the subject's head.

63. The system of claim 62, wherein the positioning feedback module is configured to:

determining one or more images of the subject wearing the array of the system;

64. A system for inferring symptom severity and/or behavioral progression in a subject undergoing treatment for one or more symptoms of a neurological disorder, the system configured to:

Outputting the symptom severity and/or progression measure.

65. A method for inferring symptom severity and/or behavioral progression in a subject undergoing treatment for one or more symptoms of a neurological disorder, the method comprising:

receive sensor data from one or more optical sensors arranged to be positioned near a target area of the subject's brain;

determining a symptom severity and/or progression measure based on the sensor data and the task data; and

outputting the symptom severity and/or progression measure.

66. A method for inferring symptom severity and/or behavioral progression in a subject undergoing treatment for one or more symptoms of a neurological disorder, the method comprising:

outputting the symptom severity and/or progression measure.

67. A server arranged to communicate with a control device for detecting brain activity of a subject over a communication network, the server being configured to:

receiving sensor data from the control device, the sensor data indicating reflected light intensities at two different wavelengths detected by one or more sensors coupled to the control device when the sensors are positioned near a target region of a subject's brain and when the subject is assuming a particular task;

receive one or more task scores from a cognitive assessment application deployed on a computing device associated with the subject, wherein the cognitive assessment application is configured to assess the subject performing the particular task and assign the one or more task scores to the subject based on the subject's performance;

Providing the sensor data and the one or more task scores as inputs to a symptom severity and/or progress determination model; and is also provided with

As an output of the symptom severity and/or progress determination model, a symptom severity and/or progress metric is determined, the symptom severity and/or progress metric being indicative of progress the subject has in treating the symptom of the neurological disorder.

68. A computer-implemented method for inferring symptom severity and/or behavioral progression in a subject undergoing treatment for one or more symptoms of a neurological disorder, the method comprising:

receiving sensor data from a control device, the sensor data indicating reflected light intensities at two different wavelengths detected by one or more sensors coupled to the control device when the sensors are positioned near a target region of a subject's brain and when the subject is assuming a particular task;

Providing the sensor data and the one or more task scores as inputs to a symptom severity and/or progress determination model; and

69. A non-transitory machine-readable medium storing instructions which, when executed by one or more processors, cause a computing device to perform the method of claim 65, claim 66, or claim 68.

70. A head-mountable apparatus, comprising:

an array comprising a plurality of optical sensor components disposed along a length of the array, each optical sensor component comprising an emitter and first and second detectors,

wherein the transmitter is disposed proximate the first detector to form a first relatively short channel and the transmitter is disposed at a relatively greater distance from the second detector to form a first relatively long channel;

an optical sensor module configured to emit light from a selected emitter of the optical sensor component and to receive signals indicative of reflected light from the first detector and the second detector of the selected optical sensor component, wherein the signals are indicative of cerebral hemodynamic responses related to neural activity in the targeted area by the emitter detector; and is also provided with

Wherein the array further comprises a plurality of electrodes, each electrode disposed between a pair of adjacent optical sensor components.

71. The apparatus of claim 70, wherein the emitter and the first detector are both disposed toward a first end of the array and the second detector is disposed at a second end of the array.

72. The device of claim 70 or 71, wherein the one or more electrodes are configured to deliver electrical stimulation to the subject.

73. The device of claim 70 or 71, wherein the one or more electrodes are configured to determine an electroencephalogram EEG signal from the subject.