EA044512B1 - DEVICE AND METHOD OF INTERACTION WITH THE BRAIN - Google Patents

Description

Field of technology to which the invention relates

The invention relates to neuromodulation devices and methods of using the aforementioned neuromodulation devices. More specifically, the present invention relates to a brain interface device and methods of using such a device, for example, the use of artificial intelligence (adaptive learning) implemented through computing devices that change the method of operation of the brain interface device in processing signals passing through it, when it works. Additionally, the present invention relates to computer program products comprising a computer-readable medium for long-term storage of information having computer-readable instructions stored therein, the computer-readable instructions being executed by a computerized device comprising processing hardware for performing the above methods.

State of the art

Recently, equipment that is designed to stimulate the human nervous system has evolved significantly. However, electrochemical signals emanating from a given brain of a given person are isolated from external influences by the skull of a given brain, and the electrochemical signals are propagated spatially by the skull. Additionally, non-invasive brain stimulation systems (NIBS) are currently used to stimulate the brain. In an example, nervous system function is altered by applying electrical stimulation to the nervous system to control the nervous system's perception of pain, or using another brain stimulation protocol to improve nervous system performance on cognitive tasks.

Typically, electrochemical signals emanating from a given person's brain are detected by connecting electrodes in contact with the person's scalp. In general, signals captured from a given brain using electrodes connected in contact with the scalp have amplitudes on the order of tens to hundreds of microvolts. These signals, or parameters derived from these signals, are associated with various brain states, cognitive performance, and specific disorders. The same or different electrodes can also be used to deliver electrical currents for non-invasive brain stimulation (NIBS). In addition, most traditional non-invasive brain stimulation systems (NIBS) rely on a single protocol, namely a common protocol used in general across different brain types. Conversely, it turns out that the human brain is highly individualistic, meaning that they differ from each other in how they respond to stimuli. Additionally, the rigid traditional electrode positioning system (also known as the 10/20 system), which takes into account skull size and is used for both electrical recording and electrical stimulation, relies on the assistance of a nurse or technician, however it still does not take into account brain characteristics and signal processing algorithms, and stimulation that does not adapt can thus lead to poor reproducibility and unexpected outcomes in extreme cases. Therefore, such a single device design according to a single protocol is a crude and ineffective approach.

Such individualistic requirements highlight a major challenge and need in the field of optimizing stimulation with respect to inter-individual structural variability, as well as with respect to individual signaling dynamics, and even with respect to rapidly changing real-time brain states. Because brain stimulation affects brain state, the stimulation potentially needs to be adapted accordingly, thus creating a feedback loop. If this adaptation occurs automatically in real time without the involvement of any third party, this type of loop is known as a closed loop. If stimulation was ineffective in changing the brain state to the desired state, then the collective stimulation parameters, which can be defined as a brain stimulation protocol, must be adjusted in this feedback loop until the desired effect is achieved. Thus, we define any adaptations for processing an input signal or for setting up a brain stimulation protocol that can adapt to interindividual and interstate variation as adaptive learning algorithms.

However, most existing dedicated devices are limited in their ability to deliver truly non-invasive brain stimulation (NTBS) of the closed-loop type. Notably, existing specialized equipment lacks real-time protocols for tuning and optimizing stimulation, resulting in poor reproducibility of the beneficial effects that noninvasive brain stimulation (NTBS) can provide. Moreover, most efforts made in this direction have so far focused primarily on triggering stimulation in response to the positive or negative phase of recorded brain waves. For example, to trigger stimulation in phase with the recorded brain waves of a given subject, a form of phase locking is currently used, as described in WIPO publication WO 2017015428 A1.

Despite the advances that have been made in the above-mentioned neuromodulation equipment, stimulation parameters need further optimization to achieve improved

- 1 044512 optimization of non-invasive brain stimulation (NIBS) in real time. Therefore, in light of the above discussion, there is a need to overcome the above-mentioned disadvantages associated with the traditional non-invasive brain stimulation (NIBS) system.

The essence of the invention

The present invention seeks to provide an improved brain interface device, such as a NIBS device, that is better able to tailor its stimulation parameters to the individual requirements and characteristics of each person to whom the device is applied.

In addition, the present invention seeks to describe an improved method of using an improved brain interface device, such as a NIBS device, that is better able to dynamically adapt its stimulation parameters to the requirements of the individual and to the characteristics of each person to whom the device is applied, depending on the response of the individual's brain to stimulation.

The object of the present invention is to provide a solution that overcomes, at least in part, the problems encountered in prior art NIBS and provide an improved brain interface device to users.

In a first aspect, embodiments of the present invention provide a brain interface device that, when operative, triggers brain activity monitoring and stimulation of the user's brain, the device comprising:

(i) a headgear device that is to be placed or positioned on the user's head, wherein the headgear device comprises an electrode device comprising an electrode array that makes electrical contact with the user's scalp, in operation, to detect electrical signals therefrom, and to supply brain stimuli to it;

(ii) an input/output device that receives electrical signals from at least one of the group of electrodes and transmits brain stimuli through at least one of the group of electrodes, in operation;

(iii) a processing device that processes the detected electrical signals received from the input/output device and creates a brain stimulation protocol that depends on the received electrical signals in operation, wherein the data processing device includes a memory module; and (iv) a power supply that supplies electrical power to the input/output device and the data processing device, characterized in that the data processing device compares the received electrical signals with a predetermined set of reference data to create an analysis of the received electrical signals, and applies at least one adaptive algorithm training or other computational algorithm to the processes of analysis and creation of a brain stimulation protocol. An advantage of the present invention is that it provides a personalized brain interface device capable of delivering user-specific stimulation in an adaptive manner and in real time, thereby resulting in a user-friendly stimulation environment to achieve desired effects.

Embodiments of the invention are advantageous for providing a brain interface device that has the potential to reduce the intensity of symptoms associated with insomnia, attention deficit hyperactivity disorder, epilepsy and tremors in Parkinson's disease through neuromodulation optimized to individual brain signal dynamics. Moreover, the device of the present invention provides a solution for achieving safe and effective transcranial stimulation, non-invasive recording of brain activity and real-time optimization of brain stimuli according to the response received from the brain.

In a second aspect, embodiments of the present invention provide a method of using a brain interface device that, when operative, provides monitoring of brain activity and stimulation of the user's brain, comprising the steps of:

(i) using a power supply to supply power to the input/output device and the data processing device;

(ii) placing or positioning the headgear device on the user's head, wherein the headgear device comprises an electrode device comprising an electrode array that makes electrical contact with the user's scalp, in operation, to detect electrical signals therefrom, and to apply to him brain stimuli;

(iii) using an input/output device to receive electrical signals from at least one of the group of electrodes, and to transmit brain stimuli to at least one of the group of electrodes;

(iv) using the processing device to process the detected electrical signals received from the input/output device and to create a brain stimulation protocol optimized with respect to the received electrical signals, the processing device

- 2 044512 data contains a memory module; and (v) comparing the received electrical signals and a predetermined set of reference data to create an analysis and applying at least one adaptive learning algorithm or other computational analysis algorithm to create a brain stimulation protocol.

In a third aspect, embodiments of the present invention provide a computer program product comprising a computer-readable medium for storing non-transitory information, having computer-readable instructions stored thereon, wherein the computer-readable instructions are executed by a computerized device comprising processing hardware for performing the above method.

Additional aspects, advantages, features and objects of the present invention will become apparent from the drawings and detailed description of the illustrative embodiments, construed in conjunction with the appended claims that follow.

It will be understood that the features of the present invention may be combined in various combinations without departing from the scope of the present invention as defined by the appended claims.

Brief description of drawings

The foregoing summary of the invention, as well as the following detailed description of illustrative embodiments, will be better understood when read in conjunction with the accompanying drawings. For purposes of illustrating the present invention, exemplary embodiments of the invention are shown in the drawings. However, the present invention is not limited to the specific methods and tools disclosed herein. Additionally, one skilled in the art will appreciate that the drawings are not to scale. Where possible, similar elements have been designated by identical positions.

Embodiments of the present invention will now be described by way of example only with reference to the following diagrams, in which:

fig. 1 is a schematic block diagram of a brain interface device for monitoring brain activity and stimulating the brain of a user, in accordance with an embodiment of the present invention;

fig. 2A and 2B are illustrations of exemplary implementations of the brain interface device of FIG. 1 applied to a user in accordance with an embodiment of the present invention;

fig. 3 is an illustration of a closed loop system for implementing at least one adaptive learning algorithm, in accordance with an embodiment of the present invention;

fig. 4 is an illustration of an exemplary implementation of a brain interface device comprising a control unit and an external stimulation device in accordance with an embodiment of the present invention;

fig. 5 is an illustration of an exemplary implementation of a brain interface device comprising an external stimulation device in accordance with an embodiment of the present invention;

fig. 6 is an illustration of an exemplary implementation of a brain interface device with another headgear device, in accordance with an embodiment of the present invention;

fig. 7 is an exemplary user interface for receiving instructions from a user or for displaying a personalized brain stimulation protocol applied to a user, in accordance with an embodiment of the present invention;

fig. 8A, 8B show spectrograms and signals detected from the O1 (channel 7 and channel 8, respectively) region of the user's brain in response to various stimulation frequencies used to determine the optimal stimulation frequency for the user, in accordance with an embodiment of the present invention;

fig. 9 is a graph illustrating the non-linear relationship between the stimulation frequency transmitted by the LEDs and the response power of the brain signal at a frequency corresponding to the stimulation frequency with the LED light, in accordance with an embodiment of the present invention; and fig. 10 is an illustration of steps of a method for monitoring brain activity and stimulating the brain of a user, in accordance with an embodiment of the present invention.

In the accompanying drawings, an underline is used to represent an element over which the underline is located, or an element to which the underline is adjacent. An un-underlined position refers to an element indicated by a line connecting the un-underlined position to the element. When an item is not underlined and accompanied by a corresponding arrow, the unemphasized item is used to indicate the general element to which the arrow is pointing.

- 3 044512

Detailed description of embodiments

The following detailed description illustrates embodiments of the present invention and the ways in which they may be implemented. Although certain modes of carrying out the present invention have been disclosed, those skilled in the art will recognize that other embodiments for making or implementing the present invention are also possible.

In one aspect, an embodiment of the present invention provides a brain interface device that, when operative, provides monitoring of brain activity and stimulation of the user's brain, the device comprising:

(i) a headgear device that is to be placed or positioned on the user's head, wherein the headgear device comprises an electrode device comprising an electrode array that makes electrical contact with the user's scalp, in operation, to detect electrical signals therefrom and to supplying brain stimuli to it;

(ii) an input/output device that receives electrical signals from at least one of the group of electrodes and transmits brain stimuli to at least one of the group of electrodes, in operation;

(iii) a processing device that processes detected electrical signals received from the input/output device and creates brain stimulation protocols that depend on the received electrical signals in operation, wherein the data processing device includes a memory module; and (iv) a power supply that supplies electrical power to the input/output device and the data processing device, characterized in that the data processing device compares the received electrical signals with a predetermined set of reference data to create an analysis of the received electrical signals, and applies at least one adaptive algorithm training or other computational algorithm to the processes of analysis and creation of a brain stimulation protocol.

In another aspect, an embodiment of the present invention provides a method of using a brain interface device that, when operative, provides monitoring of brain activity and stimulation of the user's brain, characterized in that the method comprises the steps of:

(i) using a power supply to supply power to the input/output device and the data processing device;

(ii) placing or positioning the headgear device on the user's head, wherein the headgear device comprises an electrode device comprising an electrode array that makes electrical contact with the user's scalp, in operation, to detect electrical signals therefrom, and to apply to him brain stimuli;

(iii) using an input/output device to receive electrical signals from at least one of the group of electrodes, and to transmit brain stimuli to at least one of the group of electrodes;

(iv) using a processing device to process detected electrical signals received from the input/output device and to create a brain stimulation protocol depending on the received electrical signals, wherein the data processing device contains a memory module; and (v) comparing the received electrical signals and a predetermined set of reference data to create an analysis and applying at least one adaptive learning algorithm or other computational analysis algorithm to create a brain stimulation protocol.

The present invention provides the above-mentioned apparatus and the above-mentioned method for providing monitoring of brain activity and stimulation during operation. The device described herein is simple, rugged, inexpensive, and can deliver electrical stimuli in an efficient manner. The device effectively senses brain activity and provides brain stimuli as feedback from it in a robust, efficient, and adaptive manner.

Throughout the present invention, the term user as used herein refers to any person (ie, human being) using the above-mentioned device. Optionally, the user may be a person having a certain physical or mental disorder such as epilepsy, head injury, encephalitis, brain tumor, encephalopathy, memory related problems, sleep disorders, stroke, dementia, etc. Alternatively, the user may be a person wishing to achieve a specific state of mind, such as improved concentration, relaxation, mental performance, or generally improved performance for a task.

Throughout the present invention, the term brain activity monitoring as used herein refers to monitoring electrical signals received from the brain by electroencephalography (EEG). Optionally, monitoring brain activity may include detecting signals that include, but are not limited to, signals or a combination of signals obtained through electric field encephalography (EFEG), near-infrared spectroscopy (NIRS), magnetoencephalography (MEG), electromyography (EMG) , containing signals coming from electrodes located spatially distant from a given user's scalp, electrocardiography (ECG), vision tracking and/or functional magnetic resonance imaging (fMRI). More possibly, brain activity monitoring refers to monitoring changes in the electrical activity of a user's brain when external electrical stimuli are provided to the user's brain. More possibly, the electrical activity of the user's brain may indicate biological parameters related to the mental and physical health of the user, including, but not limited to, heart rate, breathing rate, and skin conductance.

Throughout the present invention, the term brain stimulus or brain stimuli (plural of stimulus) as used herein refers to an external electrical current or a specific sequence or multiple sequences of electrical current amplitudes between a pair, multiple pairs, or any collection of electrodes applied to the user's scalp, or to locations spatially distant from the user's scalp, to alter and/or improve electrical activity in the user's brain or nerve tissue that the current is capable of reaching. Additionally, in the example, the brain stimuli applied to the user's scalp are analog external electrical signals having a voltage in the range of 1 millivolt to 50 volts, and having a current in the range of 0.1 milliampere to 20 milliampere.

Throughout the present invention, the term stimulation as used herein refers to a change (referring to an increase, decrease, or other modulation) in levels of physiological or neural activity in the brain or in tissues spatially distant from the brain of a given user. Notably, stimulation of the user's brain is performed using electrical signals applied to the user's scalp using one or more electrodes. Additionally, brain stimulation is achieved using any one of minimally invasive brain stimulation or non-invasive brain stimulation techniques, or possibly both.

Throughout the present invention, the term electrodes as used herein refers to one or more electrical conductors, the materials of these conductors including, but not limited to, stainless steel, platinum, silver coated with silver chloride, carbon rubber, graphene and other materials, as well as hydrogels, silicone, sponges, foam or any absorbent with a conductive medium, where necessary to be placed between the conductors and the scalp or skin, including, but not limited to, electrically conductive gels and pastes (such as Ten20 paste), and liquids (such as physiological saline) with such an ionic composition to form an electrical path to detect electrical signals created by neurons within the brain and provide brain stimuli to neurons and/or other cells present within the user's brain. In addition, the electrodes are configured to convert ionic potential into electrical potential and induce electromagnetic fields on the scalp and inside the skull. In addition, the electrodes may be of a minimally invasive type (such as needle electrodes or microelectrodes) or a non-invasive type (such as surface electrodes), or possibly both. In the example, the electrodes comprise an assembly of brine-impregnated foam, conductive carbon, and a metal contact. In such an example, the metal contact is operatively coupled to one or more components of the brain interface device (such as an input/output device and/or a data processing device, later described in detail herein).

A brain interface device in accordance with the present invention comprises a headgear device comprising an array of electrodes. In use, an array of electrodes is placed or positioned on the user's scalp to form electrical contact with neurons in the user's brain. Such electrical contact forms an electrical path for detecting electrical signals generated by neurons to provide brain stimuli to neurons and/or other cells present within the user's brain. The electrode array detects electrical signals created within the user's brain by neuronal activity, and the detected electrical signals are provided to an input/output device. In general, the amplitude of the detected electrical signals ranges from 1 microvolt to 100 microvolts. The electrode array may optionally be configured as any suitable EEG electrode device known in the art. The electrode array is a hybrid electrode that can function for both EEG recording and/or electrical stimulation, e.g., transcranial current stimulation (tCS), transcranial direct current stimulation (tDCS), transcranial alternating current stimulation (tACS), transcranial random noise stimulation (tRNS), transcranial temporal interference stimulation (TI), transcranial temporal summation (TS), and/or any other arbitrary transcranial stimulation protocol

- 5 044512 electric current created by adaptive algorithms (tES). An array of magnetic coils can be used in place of electrodes to deliver transcranial static magnetic field stimulation (tSMS), weak field magnetic stimulation (LFMS), repetitive transcranial magnetic stimulation (rTMS), and/or any other arbitrary transcranial magnetic stimulation (TMS) protocol generated by adaptive algorithms . Alternatively, a bank of ultrasound generators can be used to transmit focused ultrasound stimulation (FUS) protocols, also generated by adaptive algorithms. Throughout the present invention, the terms headgear or headgear as used herein refer to an item of clothing worn by users on their heads. Optionally, the headgear device may include, but is not limited to, any of: a cap, hat, helmet, headphones, headband, glasses, or hat. More conceivably, the headgear device may be manufactured to include a layer of electrically insulating material. In an example, the headgear device may be made from one of materials including, but not limited to, wood, cotton, polyester, rubber, lycra, nylon, or laminated canvas.

Throughout the present invention, the term input/output device as used herein refers to programmable and/or non-programmable components that, when operated, receive, modify, convert, process, or create one or more types of signals. Optionally, the input/output device is implemented in the form of hardware or software, or a combination thereof.

Throughout the present invention, the term data processing apparatus as used herein refers to programmable and/or non-programmable components that, when operative, execute one or more software applications to store, process, and/or share data and/or set of instructions. Optionally, the data processing unit may comprise, for example, a component contained in an electronic message network. In addition, the data processing apparatus may comprise hardware, software, firmware, or combinations thereof, suitable for storing and processing various information and services available to one or more users through one or more pieces of user equipment. Optionally, the data processing apparatus may include functional components such as a processor, memory, network adapter, etc. For example, the data processing apparatus may be implemented by a computer, a telephone (such as a smartphone), a local server, a server arrangement (such as an arrangement of two or more servers communicatively connected to each other), a cloud server, a quantum computer, etc. . Throughout the present invention, the term memory module as used herein refers to a non-storing or storage medium, such as an electrical circuit, magnetic disk, virtual memory or optical disk, in which a computer and/or data processing apparatus can store data in for any duration. Optionally, the memory module may be a non-storage storage device, such as a physical storage medium.

Throughout the present invention, the term power supply as used herein refers to a power source configured to supply electrical power to one or more components of a brain interface device. Optionally, the power supply may contain one or more batteries or batteries capable of providing electrical power. In an example, the power supply may provide 12 Volt electrical power to the stimulus generator in the input/output fixture, and 5 Volt electrical power to the data processing fixture. Optionally, the power supply could also include a fixed voltage generator and a regulator circuit to convert 3.7V power from the battery to 5V power for the brain interface device, and 12-40V power for the stimulus generator. Optionally, the power supply may also include a circuit that contains a voltage splitter to provide ±12-40V to the stimulus generator.

Throughout the present invention, the term given reference data set as used herein refers to data obtained from EEG recordings from a group of people. Additionally, the group of people may be a group of different ages, genders, mental and physical health conditions, and geographic locations.

Throughout the present invention, the term adaptive learning algorithm as used herein refers to algorithm-based software that runs on computing hardware and is configured to adapt and tune its operating parameters depending on the information that is presented while attempting to minimize a given error/loss metric, or processed by software-based algorithms when executed on computing hardware.

Throughout the present invention, the term real time refers to any process or set of processes that executes in concert or interleave in time with a small time delay between these interleavements. Also, if a set of processes are to be executed sequentially, then the term consistent will refer to processes running in parallel with

- 6 044512 minimum delay/time shift relative to each other.

Throughout the present invention, the term brain stimulation protocol as used herein refers to an electrical signal containing information about brain stimuli to be generated. It should be noted that in an embodiment of the present invention where the electrode array comprises electrodes placed at positions remote from the scalp of a given user, the brain stimulation protocol may also contain information about the stimuli to be generated at these electrodes. It should also be noted that the brain stimulation protocol also refers to information that may change over the duration of stimulation as a result of the optimization process described in the present invention. For example, the information contains one or more electrical properties for each electrode, such as amplitude, time period, phase, one or more frequencies, and the power of those frequencies, contributing to the particular sequence of brain stimuli to be created. The generated brain stimuli will take the form of a specific sequence or multiple sequences of electrical current amplitudes between a pair, multiple pairs, or any collection of electrodes. Optionally, the brain stimulation protocol contains a length of time during which brain stimuli are to be delivered to the user's scalp. Optionally, the brain stimulation protocol refers to information about at least one of visual stimulation, audio stimulation, and/or virtual reality stimulation to be created and provided to the user.

Optionally, the electrode array may comprise individual electrodes configured to record EEG and perform electrical stimulation, respectively. Alternatively, the electrode device may comprise an individual electrode for each position at which it may be desired to detect EEG signals and/or provide electrical stimulation.

In an embodiment, the array of electrodes is in electrical contact with a substantial area of the scalp of a given user; for example, the electrodes may be user-replaceable electrodes and may be lightly spring-loaded to provide positive contact to the user's scalp when the headgear device is worn by the user. More optionally, the end of the electrodes may contain a 2-D group of small pointed sub-electrodes modified by a conductive medium to safely transmit sufficient current, where the end may have an area of any size containing, but not limited to, 4 mm x 4 mm, but other dedicated ones may be used areas, wherein the subelectrodes are pointed and can find their way between the hairs of the scalp to make contact on the skin of the scalp. Specifically, a group of electrodes is spatially arranged such that voltage applied to the electrodes creates an electromagnetic field in specific parts of the brain.

In addition, a group of electrodes, when actively transmitting current and in contact with the user's scalp, delivers electromagnetic fields to the user's brain, acting as brain stimuli. Such brain stimuli provide the assistance of created brain stimulation protocols received by the input/output device from the data processing device. The generated brain stimulation protocols received from the processing device are processed by the input/output device, namely converted into analog form and adjusted to the desired current amplitude before being delivered as brain stimuli to the user's scalp.

In an embodiment, the array of electrodes used to provide brain stimuli to the user's brain may be located in one pair, more than one pair, or any combination of stimulating electrodes as determined by the brain stimulation protocol.

The input/output device contains an input signal processing device containing a preprocessor and an input converter. The input signal processing device, when in operation, processes and/or modifies electrical signals received from the user's brain. Optionally, the preprocessor includes an amplifier, more specifically, it may contain a programmable voltage amplifier that stabilizes electrical signals received from the brain and amplifies the signals with a gain ranging from 2x to 100x to produce an amplified signal, wherein a gain of 2x is used for very High dynamic range A/D conversion to enable digital pre-processing and artifact subtraction. Optionally, the preprocessor may contain one or more analog filters (such as an electrical noise filter or a stimulation artifact filter) to reduce specific artifacts and/or noise. Electrical signals received from the brain vary over time and are analog in nature. However, the data processing device only understands (namely, processes) digital bits, hence, it is necessary to convert the received electrical signal (analog in nature) from the brain into digital bits so that the data processing device is able to understand (namely, process) the received electrical signals. signals from the brain after analog-to-digital conversion. The input converter receives the amplified signal and converts

- 7 044512 it in a form suitable for analysis and processing. In addition, the input converter includes an analog-to-digital converter. In an example, the input signal processing apparatus receives analog electrical signals having amplitudes in the range of 1 microvolt to 12 volts from the user's scalp, and the preprocessor removes some artifacts and noise, and amplifies the signals to create corresponding amplified signals having amplitudes in the range up to 12 B. Subsequently, the amplified signals are converted into corresponding digital signals having a sequence of discrete values representing the corresponding range.

The input/output device further comprises an output converter and a stimulus generator. In operation, the brain stimulation protocol is received from a data processing device that is communicatively connected to the input/output device. The accepted brain stimulation protocol takes the form of a digital or discrete signal. In addition, the received brain stimulation protocol is sent to an output converter, wherein the output converter converts the received brain stimulation protocol into an analog signal having an alternating voltage amplitude with respect to time. The stimulus generator receives the converted analog signals from the output converter and can optionally convert the specified voltage signals into specific current signals. The output of the stimulus generator acts as brain stimuli, and the generated brain stimuli are applied to the user's scalp via one pair, more than one pair, or any combination of stimulating electrodes, as determined by the brain stimulation protocol. Perhaps the stimulus generator is an isolated stimulus generator powered by a separate power supply, a DC stimulator, or a voltage-to-current converter. Alternatively, the I/O device may be connected to a DC voltage source.

The data processing device contains a processing unit and a memory module. The memory module contains a given set of reference data or a set of parameters derived from them. Optionally, a given set of reference data may comprise EEG recordings or data derived from EEG recordings from a group of individuals, wherein the EEG recording is in the form of digital electrical signals or data that represent them.

The data processing device processes the detected electrical signals received from the input/output device and creates a brain stimulation protocol corresponding to the received electrical signals in operation. Optionally, the data processing unit applies adaptive learning algorithms to process and analyze detected electrical signals received from the input/output device. Optionally, the processed electrical signals received from the input/output device are compared with one or more EEG records of a given reference data set present in the memory module.

In an embodiment, comparison of the processed electrical signals or a set of parameters extracted from the signals with a given set of reference data is performed, for example, using a comparator or one or more artificial intelligence or other data processing algorithms implemented in a processing unit of the data processing apparatus. The data processing device then creates an analysis of the compared electrical signals. In addition, the analysis optionally comprises a measure of at least one of: the deviation of a parameter derived from an ideal reference signal stored in a given set of reference data; the reasons for such deviation from the ideal reference signal; and/or a parameter obtained after decomposing the waveforms by individual component analysis, principal component analysis or Fourier transform, periodogram, wavelet decomposition, wavelet transform, adaptive filters such as Weiner/Kalman filters, and other methods commonly used by those skilled in the art.

In addition, the data processing device creates a brain stimulation protocol by implementing one or more adaptive learning algorithms or other computational algorithms after analyzing the electrical signals received from the input/output device. Specifically, a brain stimulation protocol may comprise, but is not limited to, at least one of the following stimulation parameters: amplitude, phase, one or more frequencies with appropriate power for the brain stimuli to be generated, which parameters are obtained through one or more adaptive algorithms. learning or other computational algorithms. It is possible that a brain stimulation protocol could provide brain stimuli in the form of a discrete waveform or an arbitrary continuous waveform. In addition, the generated brain stimuli or brain stimulation protocol are optionally transmitted to an input signal processing apparatus for comparison and subtraction of generated stimulus artifacts, wherein the input signal processing apparatus is communicatively coupled to the stimulus generator or data processing apparatus.

The brain interaction device further comprises one or more power supplies. The power supplies are electrically connected to the input/output device and the data processing device, and supply electrical power to the input/output device and the data processing device in operation. Optionally, the power supply may contain at least one of the following sources

- 8 044512 nickels, containing, but not limited to: nickel-cadmium (NiCd), nickel-zinc (NiZn), nickel metal hydride (NiMH), solid-state battery (for example, a ceramic-based battery, a glass-based battery, or a sulfide-based battery) and a lithium-ion (Li-ion) or lithium polymer (Lipo) battery, as well as a power generator from sources such as motion or solar power, a receiver for one of the wireless power transfer technologies, or an overload-protected input from the grid side.

In an embodiment, the brain interface device comprises at least two power supplies for providing isolated electrical power to an input portion (containing blocks/devices responsible for recording or monitoring and processing the electrical signal received from the user's brain) and an output portion (containing blocks/devices responsible for recording or monitoring and processing the electrical signal received from the user's brain) and an output portion (containing blocks/devices responsible for generating brain stimuli) input/output devices, respectively.

In an embodiment, one or more power supplies are configured to supply electrical power to the brain interface device upon receiving an instruction from a user via the control unit. In addition, the user may provide the brain interface device with instructions to turn on the power supply to the brain interface device after wearing the headgear device to initialize the operation of the brain interface device. Optionally, the one or more power supplies are configured to automatically turn on electrical power to the brain interface device in a situation where the user is wearing the headgear accessory of the brain interface device.

Preferably, the brain interface device provides a user-friendly stimulation environment for the user to achieve the desired effects of the NIBS systems on the user's brain. Desired effects may include, but are not limited to, one or more of: cognitive improvement of the user, improvement of the user's motor control of the muscles, improvement of the user's mood, improvement of the user's learning, improvement of the user's relaxation, improvement of the user's concentration, relief of tremors affecting the user, relief of depression affecting the user. the user, and relief from epilepsy afflicting the user.

In an embodiment, a given set of reference data is stored in a memory module, and in some examples it can be updated iteratively in real time while the brain interface device is operating.

In one embodiment, operation of the memory module may comprise updating a predetermined set of reference data based on electrical signals or parameters received from those electrical signals received from the user's brain, storing the received electrical signals or parameters in the memory module during operation. In an example, electrical signals received from the user's brain are processed and/or modified by an input/output device and then sent to a data processing device. In addition, the data processing device stores the received electrical signals in a memory module. Thereafter, the data processing apparatus compares the received electrical signals or parameters obtained from the received electrical signals with a predetermined set of reference data to create an analysis of the received electrical signals. Optionally, this may contain a machine learning algorithm or other computational algorithms to update the processing used to create a measure of the deviation of the detected electrical signal from an ideal reference signal or a set of parameters derived from a reference signal stored in a given reference data set, or the reason for such deviation from the ideal reference signal.

In an embodiment, the data processing apparatus may analyze the received electrical signals in real time such that the electrical signals are detected on the user's scalp in concert with brain stimuli provided to the user's brain.

In an example, the processed and/or measured electrical signals received from the input signal processing apparatus may be sent to the data processing apparatus for comparison with a predetermined set of reference data to create an analysis of the received electrical signals, wherein at least one adaptive learning algorithm is used to create an analysis of the received electrical signals. electrical signals, and at least one adaptive learning algorithm is used to create a brain stimulation protocol. Optionally, the brain stimulation protocol may contain at least one of the following stimulation parameters: amplitude, a waveform perceived as displayed on an oscilloscope screen, one or more frequencies with appropriate power and phase difference for brain stimuli to be delivered to the user's brain. Thereafter, the brain stimulation protocol is transmitted to the signal generator of the input/output device, where the signal generator creates brain stimuli corresponding to the received brain stimulation protocol from the data processing device. Subsequently, the generated brain stimuli are applied to the user's scalp using at least one electrode or group of electrodes. Specifically, detecting, processing and analyzing electrical signals received from the brain and delivering brain stimuli to the user's scalp are performed in concert or simultaneously so that there is minimal delay in the above operation.

In another embodiment, the data processing device may process electrically

- 9 044512 signals received from the input signal processing device, temporarily alternating with brain stimuli supplied to the user; This approach results in potentially less cross-talk between stimuli and detected electrode signals compared to matching the delivery of stimuli and the reception of detected electrode signals. In an example, electrical signals received from the input signal processing apparatus are analyzed by the data processing apparatus through at least one adaptive learning algorithm or other computational algorithms. In addition, based on the analysis, a brain stimulation protocol is created, and brain stimuli are created in accordance with the brain stimulation protocol. Such recordings of the received electrical signal by the input/output device and the delivery of the generated brain stimuli are performed in an alternating manner with a small time gap between them. Moreover, such analysis of the received electrical signal by the data processing device and delivery of the generated brain stimuli to the user is performed in a temporally alternating manner.

In yet another embodiment, brain stimuli are applied to the user's scalp through the electrode array of the electrode device, and to other parts of the user spatially distant from the user's scalp, including, for example, one or more of the limbs, the spinal cord, or the vagus nerve. In addition, brain stimuli or stimuli to other parts of the user are generated by the stimulus generator of the input/output device in accordance with the brain stimulation protocol received from the data processing device. The generated brain stimuli are then applied to the scalp and other parts of the user by one or more of a group of electrodes.

It is possible that the generated brain stimuli can be delivered to other parts of the body, such as parts containing, but not limited to, the neck, spine, heart, chest, abdomen, hands, feet, arms and legs, which are spatially distant or located away from the scalp of a given user, and herein the term electrode device includes the location of the electrode on any of the above-mentioned body parts. In an example, one or more of a group of electrodes are in electrical contact with the user's neck to stimulate the vagus nerve to reduce heart rate, and an electrical signal is applied thereto in concert with brain stimuli applied to the user's scalp.

The data processing apparatus uses at least one adaptive learning algorithm or other computational algorithms implemented in at least one of executable software and digital hardware (eg, FPGA, ASIC, custom silicon design hardware). In addition, the at least one adaptive learning algorithm may comprise at least one of hardware executed by software or digital hardware (e.g., FPGA, ASIC, custom silicon chip design hardware) configured to use the adaptation technology in real-time timing of brain stimuli in a manner that minimizes response time between signal processing and creation of the brain stimulation protocol. Additionally, a processing device containing adaptive learning algorithms monitors the effects that different brain stimulation protocols have on the user's brain. In addition, such a processing device is sufficiently general to analyze its own actions and therefore uses at least one of the adaptive learning algorithms or other computational algorithms to optimize the brain stimulation protocol based on more meaningful training data sets.

In addition, training datasets may contain, but are not limited to, records of previous activities, data from a group of other similar systems, specified benchmark data, and historical data. In an embodiment, the brain interface device implementing the adaptive learning algorithm is configured to record and retrieve one or more potential target markers for neuromodulation. Optionally, one or more of the potential target markers are changes or actions induced in the user's brain in the form of altered brain waves or decreased response to painful stimuli, wherein the changes or actions are induced in response to the use of one or more drugs administered to the user. In an embodiment, one or more potential target markers are stored in databases for implementing artificial intelligence algorithms. The brain interface device is capable of transmitting and optimizing a brain stimulation protocol to induce effects similar to those induced by drugs that affect specific neuronal receptors. The benefit is that such optimal stimulation helps in inhibiting or enhancing effects similar to drugs without their side effects. In another embodiment, a brain interface device implementing an adaptive learning algorithm is configured to stimulate or simulate changes or actions induced in the user's brain in the form of brain wave changes based on recorded target markers. Therefore, the use of device and algorithms (i) to record and extract potential target markers for neuromodulation; (ii) to modulate brain waves, event-related potentials or other signals to simulate changes achieved by drugs; (iii) to improve the effects of drugs; (iv) to reduce unwanted side effects of drugs on brain activity, has implications for replacing conventional drugs such as opiates, or other benefits in medical settings.

The benefit is that the adaptive learning algorithm contributes greatly to achieving more personalized and thus more effective brain stimulation for the user. Additionally, an adaptive learning algorithm or other computational algorithm continuously, in a closed loop, learns the user's brain response patterns to past stimulation to better tune future brain stimuli to achieve optimized results. In addition, the implementation of adaptive learning algorithms helps in improving the therapeutic contribution of a neuromodulation device, such as the brain interface device of the present invention.

In an embodiment, the adaptive learning algorithm may comprise, but is not limited to, at least one of machine learning algorithms, which in turn includes, but is not limited to: k-nearest neighbors, regression analysis, ensemble tree algorithms, maximum power point tracking , hidden Makarov model, artificial neural network, recurrent neural network, long short-term memory algorithm, generative adversarial or adaptive adversarial neural networks, convolutional neural network or deep convolutional neural network, reinforcement learning algorithm, random forest algorithm, adaptive annealing algorithm, support vector machines , a recommender system, a genetic algorithm, a Q learning algorithm, and a deep Q learning algorithm, wherein at least one adaptive learning algorithm or other suitable computational algorithm is implemented in a closed loop system. In addition, a machine learning algorithm refers to complex source code implemented on at least one of executable software and digital hardware (e.g., FPGA, ASIC, custom silicon chip), wherein such implementation of the machine learning algorithm is pre-trained to extract information from the input signal data, or from a set of parameters derived from the input signal data, in real time with minimal latency, or trained at runtime by a training algorithm, comparing the desired outcome and tuning brain stimulation protocols, respectively. In addition, the algorithm uses various rules to configure a set of parameters, with parameters being built into the algorithm to form patterns to carry out the decision-making process. It is possible that when new or additional data becomes available, the algorithm, when running, automatically adjusts parameters to create a change in the pattern by comparing the existing pattern with the previous pattern.

In another embodiment, a reinforcement learning algorithm is a category of algorithms based on goal-oriented algorithms that learn how to achieve a complex problem (goal) or maximize on a particular parameter over many stages, using the concept of cumulative benefit; for example, maximizing the power and duration of high alpha activity over a long period of stimulation, which acts as a cumulative benefit. In addition, a reinforcement learning algorithm learns from the benefits it receives in response to an action performed by a system implementing reinforcement learning algorithms and adapts accordingly to maximize the cumulative benefits in response to subsequent actions.

Additionally, it is possible that a deep Q learning algorithm falls into the category of algorithms that contain both a reinforcement learning algorithm and a neural network algorithm with multiple hidden layers to achieve optimized output in real time while implementing a closed loop system. Additionally, a neural network algorithm refers to a set of algorithms that attempt to recognize the underlying relationships in a set of data through a process that mimics the way the human brain works. In addition, the neural network algorithm provides deep learning through the hierarchical arrangement of a set of parameters, where the parameters are built into the algorithm to form patterns to carry out the decision-making process. In addition, when new or additional data becomes available, the algorithm, when running, automatically adjusts parameters to create changes in patterns.

In an embodiment, at least one of the aforementioned adaptive learning algorithms is implemented in a closed loop system. In addition, the closed loop system includes an electrode device, an input/output device, a data processing device, and a power supply. In addition, the input/output device contains a preprocessor, an input transformer, a stimulus generator and an output transformer. In addition, the data processing device includes a processing unit and a memory module, wherein the processing unit and the memory module are communicatedly connected. In addition, the electrode device, the preprocessor, the input transducer, the stimulus generator, the output transducer, and the data processing device are communicatedly connected to each other directly or indirectly. In addition, electrical signals generated in the user's brain are detected by the electrode device and then transmitted to the processing unit through a preprocessor and an input transducer. In addition, the processing unit applies at least one of the adaptive learning algorithms to create and transmit a stimulation protocol

- 11 044512 brain output converter. In addition, the output transducer processes and transmits the processed brain stimulation protocol to the stimulus generator, wherein the stimulus generator generates brain stimuli and transmits the generated brain stimuli to the electrode device for stimulating the user's brain. Possibly for artifact subtraction, a copy of the generated brain stimuli is also sent to the preprocessing fixture. In addition, different types of signals are processed in a closed loop such that the brain stimulation protocol is updated iteratively to achieve the target electrical activity, and thus achieve personalized and optimized brain stimulation in real time.

The benefit is that closed-loop systems that implement machine learning algorithms in non-invasive brain stimulation (NIBS) systems provide real-time protocol tuning and optimal stimulation, resulting in more personalized and effective stimulation of the user's brain.

Throughout the present invention, the term target electrical activity as used herein refers to the desired general or specific pattern of electrical activity in the user's brain or parameters derived from the analysis of such activity that should be obtained to reduce the intensity of symptoms associated with a particular state of mental imbalance health in humans, or other clinically significant condition that can be alleviated by the above method. In addition, the target electrical activity may also be the desired electrical activity to provide or induce a specific mood, emotion in the user's brain, or other specific state of mind, which can be achieved in the above manner.

In an embodiment, the data processing apparatus uses at least one adaptive learning algorithm to adjust brain stimuli iteratively such that a given user's brain electrical activity is adjusted to an approximate target brain electrical activity, as desired. After delivering the generated brain stimuli to the user's scalp, the electrical signals from the user's brain are detected again and analyzed by the data processing device in a closed loop. Optionally, the analysis of the detected electrical signals comprises determining changes in the detected electrical activity or in parameters derived from the detected electrical brain activity following the delivery of brain stimuli in the previous iteration. More possibly, the analysis of the detected electrical signals further comprises determining a positive or negative value or set of values required to adjust any of the parameters of the brain stimulation protocol to achieve the desired target electrical brain activity. Such analysis by the processing device may be performed by at least one adaptive learning algorithm such that the brain stimulation protocol to be applied can be adjusted iteratively after each brain stimulus is delivered and the effect of the brain stimuli delivered is detected. Specifically, iterative brain stimulus tuning work is performed in real time to deliver tuned brain stimuli to the user's scalp to ultimately achieve the desired target brain electrical activity. In this context, real time means that: recording of a received electrical signal by an input/output device; processing by the data processing device, comprising executing an adaptive learning algorithm; setting up brain stimulation protocol parameters; the delivery of the created brain stimuli is carried out either in a coordinated, sequential or alternating manner, with the cycle being completed within a small area. In addition, it is possible that the time to complete a loop can be reduced to a few milliseconds by implementing digital processing hardware and executing adaptive learning algorithms.

Alternatively, real-time means with cycle intervals less than 5 min, more possible with cycle intervals less than 1 min, more possible with cycle intervals less than 1 s, more possible with cycle intervals less than 1 ms, and even more possible with the above digital hardware implementations means with cycle intervals less than 1 ms.

Throughout the present invention, the term feedback loop refers to adaptations of brain stimulation used to influence the user's brain state with respect to: inter-individual structural variability; individual signal dynamics and rapidly changing brain states in real time.

In an embodiment, the adaptation of brain stimulation automatically in real time without the involvement of any third party is called a closed loop. Additionally, if stimulation is not effective in changing the brain state to the desired state, then the brain stimulation protocol needs to be adjusted in this feedback loop until the desired effect is achieved. In addition, perhaps any algorithm that processes an input signal or configures a brain stimulation protocol that can adapt to interindividual and interstate differences is defined as adaptive learning algorithms.

In an embodiment, the brain interface device further comprises a control unit that, in operation, receives input from at least one user or device

- 12 044512 of a third party, wherein the control unit is communicatedly connected to the data processing device and contains a message module for generating a message between the device and the third party device.

Throughout the present invention, the term control unit as used herein refers to a device configured to receive an instruction from a user or third party device via a user interface, wherein the user interface is configured to record the instruction via at least one of: a pushbutton interface , wireless interface, touch screen interface, gesture interface, microphone interface (voice detection), or brain interface device operating in this context to control stimulation. Optionally, the control unit, in operation, provides a processing device with operating parameters to personalize brain stimulation based on input from a user or a third party device. In addition, the operating parameters include at least one of: on/off state, stimulation mode, stimulation time, age of the user, gender of the user, any significant medical history and medical condition of the user undergoing brain stimulation, or the desired mental state of the user. Optionally, the control unit device loads into the data processing device a program comprising at least one of adaptive learning algorithms for optimizing detection of brain signals specified by the program or for optimizing stimulation to achieve a target electrical activity specified by the program. Alternatively, the control unit may include a communication module for establishing a wired or wireless connection including, but not limited to, an Internet connection between the brain interface device and a third party device. Optionally, this may allow a third party device to download a program to the processing device via the control unit. Optionally, it may provide input and output signals to a third party device, such that the third party device is implemented as a computer, a telephone (such as a smartphone), a local server, a server arrangement (such as an arrangement of two or more servers communicatingly connected each other), a cloud server or a quantum computer to allow a third party device to act as a data processing device. Additionally, the control unit is configured to control the external stimulation device based on input from at least one user and a third party device. In addition, the control unit is configured to receive electricity from the power supply.

Throughout the present invention, the term third party device as used herein refers to an external device communicatively connected to the control unit via a communication module, the communication being via a wired or wireless connection, including, but not limited to, Internet connection, Bluetooth ® etc. Optionally, the third party device comprises at least one of: a smartphone, a computer (can be a personal, cloud, distributed or tablet computer), a smart watch, a remote control, a medical device, a local server, a server arrangement (such as a two-part arrangement). or several servers communicatedly connected to each other), a cloud server and a quantum computer. More possibly, the third party device is configured to receive monitoring information associated with electrical signals detected from the user's brain, wherein the monitoring information comprises at least one of: electroencephalogram (EEG), electric field encephalography (EFEG), near infrared spectroscopy (NIRS) ), magnetoencephalography (MEG), electromyography (EMG), electrocardiography (ECG), heart and/or respiratory rate monitor, vision tracking and/or functional magnetic resonance imaging (fMRI). Additionally, the third party device is configured to control the external stimulation device via the control unit. Optionally, the brain interface device, in operation, uses a third party device communicatively coupled to the control unit to transmit operating parameters to the control unit, which includes, but is not limited to, at least one of an on/off state, a stimulation mode , the time of stimulation, the age of the user, the gender of the user, any significant medical history and medical condition of the user undergoing brain stimulation, or the desired mental state of the user. Additionally, it is possible that the brain interface device, in operation, uses a third party device to load into the processing device, via a control unit, a program comprising at least one of adaptive learning algorithms for optimizing the detection of brain signals specified by the program, or for optimizing stimulation to achieve the target electrical activity defined by the program.

The benefit is that the control unit and third party devices provide a better user experience through a user-friendly interface. It is possible that the control unit allows the third party device to execute a custom adaptive learning algorithm instead of the data processing unit, which may be advantageous where the processing power required to execute the adaptive learning algorithm exceeds the power of the data processing unit.

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In addition, the use of third party devices allows the user to select the operating parameters of the device at will to create a custom brain stimulation protocol. Preferably, the brain interface device also provides an open platform for scientists and clinicians to explore functional aspects of the human brain in more detail and in real time (as mentioned above) using monitoring information such as electroencephalogram (EEG), electric field encephalography (EFEG), near infrared spectroscopy (NIRS), magnetoencephalography (MEG), electromyography (EMG), electrocardiography (ECG), vision tracking and/or functional magnetic resonance imaging (fMRI).

In an embodiment, the device further comprises an external stimulation device for providing at least one of: visual stimulation, audio stimulation and/or virtual reality stimulation to the user's brain, wherein the external stimulation device is in communication with the control unit. It is possible that the external stimulation device communicates with the data processing device directly or via a control unit. More possibly, at least one of the visual stimulation, audio stimulation, and/or virtual reality stimulation of the user's brain provided by the external stimulation device is in synchronization with the brain stimuli provided to the user's brain.

In an embodiment, the parameters of at least one of: visual stimulation, audio stimulation and/or virtual reality stimulation become part of the brain stimulation protocol optimized by the control unit.

Throughout the present invention, the term external stimulation device as used herein refers to a releasably connected external device used for audio-visual or virtual reality stimulation through at least one of: a virtual reality device, a display device, glasses, headphones, in-ear headphones, a speaker, a therapeutic massager, electrodes placed elsewhere on the body, and/or a smart lens (such as Google Lens (RTM)). In addition, the external stimulation device is configured to receive electrical energy from one or more power supplies.

In an example, an external stimulation device provides audiovisual stimulation to relax the user and reduce stress while working in sync with brain stimuli. Preferably, the external stimulation device provides isolation to the user by reducing unwanted light entering the user's eyes and noise entering the user's ears. This isolation helps the user further reduce unwanted brain activity, resulting in improved effectiveness of brain stimulation protocols.

In an exemplary operation, the control unit is implemented as a microcontroller coupled to a stimulus generator and/or an external stimulation device. In addition, the third party device is implemented in the form of a laptop computer (eg, a MacBook™ laptop computer) such that the microcontroller is communicatively connected to the laptop computer via a cloud platform. The laptop computer processes the operating parameters associated with brain stimulation to be provided to the user, and subsequently, transmits the operating parameters to the microcontroller associated with the external stimulation device. In addition, the laptop transmits operating parameters to the microcontroller in real time. In such an example, the external stimulation device comprises a light emitting diode (referred to as an LED herein) or alternatively an LED assembly, and the communication module comprises a WiFi chip such that the LED is coupled to the microcontroller and the microcontroller is communicatively coupled to the laptop computer via a cloud platform. In addition, the laptop computer controls the brain stimuli transmitted through the LED, such as adjusting the frequency, pulse width, and/or brightness of the light emitted by the LED. In addition, the electrode group contains a pair of electrodes located on the user's scalp corresponding to the location of the occipital lobe (such as in locations O1 and O2, according to the 10-20 EEG location system), and the reference electrode and bias electrodes are located on the temples user (such as at locations T3 and T4, respectively). The electrode array records activity in the user's visual cortex such that the activity reflects the user's perceptions associated with the visual stimuli conveyed by the LED. The electrode array is communicatedly connected to an input/output device, which may be implemented by an OpenBCI Cyton PCB. The I/O fixture has a programmable analog-to-digital gain converter to amplify and convert the analog signals detected at each of the electrode arrays into digital data. In addition, the input/output device is communicatively connected to a third party device implemented in the form of a laptop computer, and wirelessly transmits digital data to the third party device. Thus, the third party device receives information from the brain through the input/output device and acts as a data processing device to create and optimize the brain stimulation protocol transmitted through the above-mentioned LEDs.

- 14 044512

In another exemplary work, the control unit is implemented as a microcontroller coupled to a stimulus generator and/or external stimulation device. In addition, the third party device is implemented in the form of a smartphone. In such an example, application software (or an application) is installed on a smartphone such that the smartphone (or a user associated with the smartphone) transmits and receives operating parameters associated with brain stimulation to be provided to the user, the headgear device, and/or the external device. stimulation via the app. In one example, such operating parameters correspond to one or more operating modes of the brain interface device. In another example, a smartphone (or a user associated with a smartphone) may measure at least one of: a current transmitted to a group of electrodes to provide brain stimulation, a voltage of current transmitted to provide brain stimulation (such as a voltage required to provide a direct current an electrode group associated with the electrode device) and an impedance on the electrode group of the electrode device (so as to determine that the electrode group is properly located on the user's scalp). In yet another example, an array of electrodes may be positioned over the mastoid process of the user's temporal bone to target cranial nerves and deeper regions of the user's brain. For example, to create an input for an input/output device, an array of electrodes (configured to record the user's brain activity) is placed over the frontal portions of the brain. In addition, the electrode group is connected to an input/output device with an amplifier and a digital-to-analog converter, which can be implemented by modifying the OpenBCI Cyton PCB, so that the modified OpenBCI Cyton PCB can be communicatively connected via the Internet to the data processing device through a cloud platform, and also an application installed on a third party device.

In an embodiment, at least one of the stimulus generators and the other portion of the hardware configuration includes a safety device, wherein the safety device prohibits the transmission of brain stimuli to the electrode device in the event of an electrical failure of the device or a request from the user to stop stimulating the brain. In addition, the safety device includes at least one of: a protective relay, an overcurrent sensor, an overvoltage sensor, a frequency sensor, an excessive muscle/motor activity sensor (discomfort sensor), and an emergency kill switch. In addition, the safety device is communicatedly connected to the control unit through a data processing device, which in turn is also connected to a third party device with a user-friendly interface for interrupting stimulation/recording. In addition, the safety device, in operation, receives data associated with at least one of the current and voltage across the electrode group from at least one of the overcurrent sensor and the overvoltage sensor. Further, in one implementation of the safety device, when in operation, it determines an electrical fault event by comparing data associated with at least one of: current and voltage across the electrode array with specified reference data comprising reference data associated with at least one from: current and voltage on a group of electrodes. In addition, the safety device may also be implemented in the data processing device, the electrode device, one or more power supplies, and the external stimulation device.

Throughout the present invention, the term electrical fault as used herein refers to an unwanted amount of electrical current and/or electrical voltage occurring in a brain interface device, wherein such unwanted amount of electrical current and/or electrical voltage may be harmful to the user and/or device. Furthermore, in the event of an electrical fault, the safety device is configured to interrupt the supply of electrical power to the device from one or more power supplies via a protective relay.

The benefit is that the safety device provides improved protection against any damage to the user in real time, resulting in risk-free use of the brain interface device without any expert assistance. Moreover, the brain interface device is designed in its external and internal component parts, as well as its operating method, to avoid any harm occurring to the user.

The present invention also relates to the method as described above. The various embodiments and variations disclosed above apply mutatis mutandis to the method.

Optionally, the method comprises using a data processing apparatus to update a given set of reference data iteratively in real time and storing the updated defined set of reference data in a memory module. Real time should be understood as described in the present invention, and should not simply be temporarily continuous.

Optionally, the method comprises using a data processing apparatus to analyze the received electrical signals in real time such that the electrical signals are detected on the user's scalp in concert with brain stimuli provided to the user.

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Optionally, the method comprises using the data processing apparatus to analyze electrical signals received from the signal processing apparatus temporarily interleaved with brain stimuli provided to the user.

Optionally, the method comprises using a group of electrodes of the electrode device to deliver brain stimuli to the user's scalp and to other parts of the user spatially distant from the user's scalp.

Optionally, the method comprises using at least one adaptive learning algorithm or other computational algorithm implemented with data processing apparatus in the form of at least one of executable software and digital hardware (eg, FPGA, ASIC, custom chip design).

The adaptive learning algorithm may include, but is not limited to, at least one of a machine learning algorithm, which in turn contains, but is not limited to: k-nearest neighbors, regression analysis, ensemble tree algorithms, maximum power point tracking, hidden Makarov model, artificial neural network, recurrent neural network, long short-term memory algorithm, generative adversarial or adaptive adversarial neural networks, convolutional neural network or deep convolutional neural network, reinforcement learning algorithm, random forest algorithm, adaptive annealing algorithm, support vector machines , a recommender system, a genetic algorithm, a Q learning algorithm, and a deep Q learning algorithm, wherein at least one adaptive learning algorithm or other suitable computational algorithm is implemented in a closed loop system.

Optionally, the method comprises programming the data processing apparatus to use, but is not limited to, at least one adaptive learning algorithm for iteratively adjusting the brain stimulation protocol such that the user's brain electrical activity is tuned to an approximate target brain electrical activity, as desired.

Optionally, the method comprises a control unit for receiving input from at least one user or third party device, the control unit being communicatively coupled to the data processing device and comprising a message module for generating a message between the device and the third party device.

Optionally, the method comprises using an external stimulation device to provide at least one of: visual stimulation, audio stimulation and/or virtual reality stimulation to the user's brain, wherein the external stimulation device is in communication with the control unit.

In one example, an external stimulation device is used to provide visual stimulation as a temporary response to the user's eyes.

Optionally, the method comprises using a safety device to prohibit delivery of brain stimuli to the electrode group in the event of a malfunction of the device, wherein the safety device is in communication with the input/output device.

In an embodiment, the present invention provides a computer program product comprising a computer-readable storage medium for long-term storage of information, having computer-readable instructions stored therein, wherein the computer-readable instructions are executed by a computerized device comprising processing hardware for executing a method of using a brain interface device that provides, during operation, monitoring of brain activity and stimulation of the user's brain.

Detailed description of drawings

Referring to FIG. 1, there is shown a block diagram of a brain interface device 100 for monitoring brain activity and stimulating the brain of a user, in accordance with an embodiment of the present invention. As shown, the brain interface device 100 for monitoring brain activity and stimulating the user's brain includes a headgear device 120, a data processing device 140, an input/output device 130, and one or more power supplies 150. In addition, the headgear device 120 includes an electrode device 110 comprising a group of electrodes 112-118, the group of electrodes positioned to contact the wearer's scalp to detect brain activity. In addition, the electrode device 110 is communicatedly connected to the input/output device 130, wherein the input/output device 130, in operation, receives detected signals and transmits brain stimuli to at least one of the group of electrodes 112-118. In addition, the input/output device 130 includes a possible input signal pre-processing device (not shown), which may include a possible amplifier (not shown); artifact filter (not shown); input converter (not shown); an output transducer (not shown) and a stimulus generator (not shown). In addition, the input/output device 130 is communicatedly connected to the data processing device 140. In addition, the data processing device 140 includes a memory module 142 and a processing unit 144. One or more power supplies 150, in operation, provide electrical power to the input/output fixture 130 and the data processing fixture 140.

Referring to FIG. 2A, shows an exemplary implementation of a brain interface device 200 (such as the brain interface device 100 of FIG. 1) located on the head of a user 201, in accordance with an embodiment of the present invention. Specifically, the exemplary implementation is a side view of a user 201 wearing a brain interface device 200 . The brain interface device 200 includes a headgear device 220 (such as the headgear device 120 of FIG. 1) and an assembly block 270 in which the headgear device 220 is implemented by a sports cap in this example. Moreover, the headgear device 220 includes an electrode device 210 (such as the electrode device 110 of FIG. 1), wherein the electrode device 210 includes an electrode group 212-218 (such as the electrode group 112-118 of FIG. 1). In addition, the electrode group 212-218 is connected to the assembly block 270 through a group of connecting wires 272-278, respectively. Specifically, one of the electrodes 218 of the group of electrodes 212-218 is a reference electrode connected to a non-hairy portion of the user's scalp 201.

Referring to FIG. 2B, shows the same exemplary implementation of a brain interface device 200 placed on the head of a user 201, in accordance with an embodiment of the present invention. Specifically, the exemplary implementation is a rear view of a user 201 wearing a brain interface device 200 including a headgear device 220 and an assembly block 270. Additionally, the assembly block 270 includes an input/output device 230 (such as the I/O device 130 of FIG. 1), a data processing apparatus 240 (such as the data processing apparatus 140 of FIG. 1), and one or more power supplies 250 (such as one or more power supplies 150 of FIG. 1). In addition, the data processing apparatus 240 includes a memory module 242 (such as the memory module 142 of FIG. 1) and a processing unit 244 (such as the processing unit 144 of FIG. 1).

Referring to FIG. 3, illustrates a brain interface device (such as the brain interface device 100 of FIG. 1) operating as a closed loop system 300. The closed loop system 300, when operated, implements at least one adaptive learning algorithm or other computational algorithm, in accordance with an embodiment of the present invention. The closed loop system 300 includes an electrode fixture 310 (such as the electrode fixture 110 of FIG. 1), an input/output fixture 330 (such as an I/O fixture 130 of FIG. 1), a data processing fixture 340 (such as a data processing fixture 140 of FIG. 1) and one or more power supplies 350 (such as one or more power supplies 150 of FIG. 1). In addition, the input/output fixture 330 includes a preprocessor 332, an input transformer 334, a stimulus generator 336, and an output transformer 338. The data processing fixture 340 includes a processing unit 344 (such as the processing unit 144 of FIG. 1) and a memory module 342 (such as memory module 142 of Fig. 1), wherein the processing unit 344 and the memory module 342 are communicatedly connected. The electrode fixture 310, the preprocessor 332, the input transducer 334, the stimulus generator 336, the output transducer 338, and the data processing fixture 340 are communicatedly connected in the manner shown. Electrical signals generated in the user's brain are detected by electrode device 310 and then transmitted to processing unit 344 via preprocessor 332 and input transducer 334. Processing unit 344 applies at least one adaptive learning algorithm or other computational algorithm to create and transmit a brain stimulation protocol. output transducer 338. Additionally, output transducer 338 processes and transmits the processed brain stimulation protocol to stimulus generator 336, wherein stimulus generator 336 generates brain stimuli and transmits the generated brain stimuli to electrode device 310 for stimulating the user's brain. Additionally, one or more power supplies 350, in operation, supplies electrical power to the input/output device 330 and the data processing device 340.

Referring to FIG. 4, a block diagram of an exemplary implementation of a brain interface device 400 (such as the brain interface device 100 of FIG. 1) is shown, including a data processing device 440 (such as a data processing device 140 of FIG. 1), a headgear device 420 ( such as headgear fixture 120 of FIG. 1), I/O fixture 430 (such as I/O fixture 130 of FIG. 1), data processing fixture 440 (such as data processing fixture 140 of FIG. 1), one or multiple power units 450 (such as one or more power units 150 of FIG. 1), a control unit 460, and an external stimulation device 480, in accordance with an embodiment of the present invention. Additionally, the data processing device 440 of the brain interface device 400 is communicatedly connected to the control unit 460. The control unit 460 further includes a message module 462. In addition, the control unit 460 is in communication with an external stimulation device 480, comprising in this example an audio stimulation device 482 and a virtual reality stimulation device 484. Additionally, one or more power units 450 of the brain interface device 400, in operation, supplies electrical power to the data processing apparatus 440 (such as the data processing apparatus 140 of FIG. 1), and may also optionally provide electrical power to the processing unit 460. control and adaptation of 480 external stimulation.

Referring to FIG. 5, shows an exemplary implementation of a brain interface device 500 (such as the device 400 of FIG. 4) comprising an assembly block 570 (such as an assembly block 270 of FIGS. 2A and 2B), a headgear attachment 520 (such as a headgear attachment 120 from Fig. 1) and external stimulation device 580 (such as external stimulation device 480 from Fig. 4), in accordance with an embodiment of the present invention.

The external stimulation device 580 includes this example audio stimulation device 582 (such as the audio stimulation device 482 of FIG. 4), and the virtual reality stimulation device 584 (such as the virtual reality stimulation device 484 of FIG. 4). Additionally, external stimulation device 580 is communicatively coupled to a control unit (not shown). In addition, the assembly block 570 (such as the assembly block 270 of FIGS. 2A and 2B) includes a control unit (not shown) and one or more power supplies (not shown). In addition, one or more power supplies, in operation, may also optionally supply electrical power to the external stimulation device 580.

Referring to FIG. 6, shows an exemplary implementation of a brain interface device 600 (such as the device 100 of FIG. 1) with a headband-type headgear device 620, in accordance with an embodiment of the present invention. The brain interface device 600 further includes an assembly block 670 (such as the assembly block 270 of FIGS. 2A and 2B). Additionally, headgear fixture 620 (such as headgear fixture 120 of FIG. 1) includes an electrode fixture (such as electrode fixture 110 of FIG. 1) containing an electrode array 612-616 (such as an electrode array 112-118 of FIG. 1), wherein the group of electrodes 612-616 is connected to the assembly block 670 through a group of connecting wires 672-676 (such as the group of connecting wires 272-278 from FIGS. 2A and 2B).

Referring to FIG. 7, an exemplary user interface 700 is shown for receiving instructions from a user or for displaying personalized brain stimulation applied to a user, in accordance with an embodiment of the present invention. As shown, the user interface 700 may be used by the user to provide instructions such as those related to an on/off state via a button 702, a stimulation mode of tDCS, tACS, pulse or edge via a corresponding button 704, etc. The user interface 700 also allows the user to adjust the test current sent to the electrode group, tACS frequency, pulses, or light emitted by the LED associated with the external stimulation device, pulse/edge width, and/or offset using appropriate sliders 706A-D. Alternatively, the user may check the current delivered to the electrode array, tACS frequency, pulses, or light emitted by an LED associated with the external stimulation device, pulse/edge width, and/or offset, which are displayed by the respective sliders 706A-D such that the respective sliders The 706A-D automatically change their location on the user interface 700 based on updated values determined by the stimulation optimization algorithm. In addition, the user interface 700 displays various stimulation parameters, such as voltage, current, and impedance, applied to the electrode array to provide brain stimulation through the output region 708 of the user interface 700.

Referring to FIG. 8A, 8B show spectrograms 810 and 820 of signals detected from area O1 (channel 7810 and channel 8820, respectively) of the user's brain in response to various user stimulation frequencies 810820 used to determine the optimal stimulation frequency for the user, in accordance with an embodiment carrying out the present invention; Stimulation frequencies 810-820 are optimized to maximize changes in brain signal power at a frequency that matches the LED light stimulation frequency by an adaptive maximum power point tracking algorithm. Stimulation frequencies of 810–820 are applied to the LEDs for 25 s, each after an inactive baseline period of 25 s. The adaptive maximum power point tracking algorithm determines the next change in pacing rate based on the location of the local maximum. In addition, the amplitude of this change in stimulation frequency is varied to allow precise determination of the optimal stimulation frequency. Accordingly, the amplitude of the applied stimulation frequency is varied until the optimal stimulation frequency is determined with an accuracy of less than ±0.1 Hz.

As shown, the adaptive maximum power point tracking algorithm determines the first stimulation frequency band around 10 Hz to become prominent (depicted by the white line in spectrogram 810 of FIG. 8A along the right side of the 10 Hz column), and frequencies around 10 Hz are examined to narrow to the optimal stimulation frequency. In addition, when the adaptive maximum power point tracking algorithm applies different stimulation frequencies around 9.5 Hz, no additional increase is detected by the power point tracking algorithm. Therefore, the adaptive maximum power point tracking algorithm determines the optimal stimulation frequency of 9.5 Hz for a given user.

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Referring to FIG. 9, a graph 910 is shown illustrating the non-linear relationship between the stimulation frequency transmitted by the LEDs and the response power of a brain signal at a frequency corresponding to the stimulation frequency of the LED light, in accordance with an embodiment of the present invention. The nonlinear relationship between the stimulation frequency applied to the LEDs and the response power is determined through an adaptive maximum power point tracking algorithm. The adaptive peak power point tracking algorithm determines the local maximum by applying different stimulation frequencies and tapering to the optimal stimulation frequency. Subsequently, the adaptive maximum power point tracking algorithm determines a local maximum of about 10 Hz (labeled 920 in plot 910). In addition, the adaptive maximum power point tracking algorithm attempts to determine the optimal stimulation frequency around a local maximum by applying different stimulation frequencies around 10 Hz. It will be understood that such technology for determining the optimal frequency of flashing LED stimulation through an adaptive maximum power point tracking algorithm may be used, for example, in brain computer interface (or BCI) applications that rely on steady state visual evoked potentials. Optimal stimulation frequency in such BCI-related applications can be used to create a reliable response to flashing visual stimulation to more accurately and quickly guide the equipment that the user is attempting to control with the brain.

Referring to FIG. 10 illustrate steps of a method 1000 for monitoring brain activity and stimulating a user's brain using a brain interface device (such as device 100 of FIG. 1), in accordance with an embodiment of the present invention. The method is initiated at step 1002, and at step 1002, one or more power supplies (such as one or more power supplies 140 of FIG. 1) are used to supply power to the input/output device and the data processing device. At step 1004, a headgear device (such as the headgear device 120 of FIG. 1) is placed on the user's head to detect electrical signals and provide brain stimuli thereto. At step 1006, an input/output device (such as input/output device 130 of FIG. 1) is used to receive an electrical signal from an electrode array (such as an electrode array 112-118 of FIG. 1) and transmit brain stimuli to at least one a pair or any combination of a group of electrodes. At step 1008, a processing apparatus (such as the processing apparatus 140 of FIG. 1) is used to process the received electrical signal and create a brain stimulation protocol corresponding to the received electrical signal. Optionally, the received electrical signal is processed using at least one of the adaptive learning algorithms or another computational algorithm to create a brain stimulation protocol corresponding to the received electrical signal. At step 1010, the data processing device compares the received electrical signal with a predetermined set of reference data to create an analysis using at least one of the adaptive learning algorithms or other computational algorithm to create a brain stimulation protocol. Method 1000 ends at step 1010 if the predetermined stimulation target or predetermined stopping point is achieved, otherwise steps 1004-1010 are repeated automatically in an iterative manner until the predetermined stimulation target or predetermined stopping point is achieved. Additionally, processes 1004-1010 may operate iteratively based on instructions received from a data processing device (such as data processing device 140 of FIG. 1).

Steps 1002-1010 are illustrative only, and other alternatives may be provided where one or more steps are added, one or more steps are deleted, or one or more steps are provided in a different sequence without departing from the scope of the claims herein.

Modifications to the embodiments of the present invention described in the foregoing are possible without departing from the scope of the present invention as limited by the accompanying claims. Expressions such as containing, including, embedded, has, is, used to describe and claim the present invention are intended to be construed in a non-exclusive manner, namely, to allow elements, components or elements not expressly described to also be present. A reference to the singular shall also be construed to refer to the plural, where appropriate.

Additional aspects, advantages, features and objects of the present invention will become apparent from the drawings and detailed description of the illustrative embodiments, construed in conjunction with the appended claims that follow.

It will be understood that the features of the present invention may be combined in various combinations without departing from the scope of the present invention as defined by the appended claims.

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Claims (19)

CLAIM 1. A brain interface device that provides, when operational, monitoring of brain activity and stimulation of the user's brain, the device including: (i) a headgear device that is intended to be placed or positioned on the user's head, wherein the headgear device comprises an electrode device comprising an electrode array that makes electrical contact with the user's scalp, in operation, to detect electrical signals therefrom and to supplying brain stimuli to it; (ii) an input/output device that receives electrical signals from at least one of the group of electrodes, and transmits brain stimuli through a brain stimulation protocol to at least one of the group of electrodes, in operation; (iii) a processing device that processes the detected electrical signals received from the input/output device and creates a brain stimulation protocol that depends on the received electrical signals in operation, wherein the data processing device includes a memory module; and (iv) one or more power supplies that supply electrical power to the input/output device and the data processing device, characterized in that the data processing device compares the received electrical signals with a given set of reference data to create an analysis of the received electrical signals, and applies at least one adaptive learning algorithm incorporating a maximum power point tracking algorithm to the processes of analyzing and creating a brain stimulation protocol, wherein the adaptive learning algorithm iteratively tunes the brain stimulation protocol in real time to apply customized brain stimuli to the user's scalp to produce the desired electrical target user's brain activity. 2. The brain interface device according to claim 1, characterized in that a given set of reference data is stored in a memory module and updated iteratively in real time while the brain interface device is operating. 3. The brain interface device according to claim 1, wherein the data processing device analyzes the received electrical signals and applies a brain stimulation protocol in real time so that the electrical signals are detected on the user's scalp in concert with brain stimuli applied to the user. 4. The brain interface device according to claim 1, characterized in that the data processing device analyzes electrical signals received from the input signal processing device temporarily with brain stimuli supplied to the user. 5. A brain interface device according to any of the previous claims, characterized in that the stimuli are also applied to other parts of the user that are spatially distant from the scalp of the user. 6. The brain interface device of any one of the preceding claims, characterized in that the data processing device uses, but is not limited to, at least one adaptive learning algorithm implemented in at least the form of: executable software, digital hardware (for example, FPGA, ASIC, custom chip design). 7. A device for interacting with the brain according to any of the previous paragraphs, characterized in that at least one adaptive learning algorithm contains, but is not limited to, at least one of machine learning algorithms: k-nearest neighbors algorithm, regression analysis, ensemble of tree algorithms, maximum power point tracking, artificial neural network, deep convolutional neural network, recurrent neural network, reinforcement learning algorithm, random forest algorithm, recommender system, genetic algorithm, Q learning algorithm and deep Q learning algorithm, wherein at least one of these algorithms implemented in a closed loop system. 8. The brain interface device of any one of the preceding claims, wherein the processing device uses, but is not limited to, at least one adaptive learning algorithm to iteratively adjust a brain stimulation protocol such that the electrical activity of the user's brain is modulated to an approximate target according to desire. 9. The brain interface device of any one of the preceding claims, wherein the device further includes a control unit that, in operation, receives input from at least one user or third party device, wherein the control unit is communicatedly coupled to the processing device data and contains a message module for generating a message between the device and a third party device. 10. The device for interacting with the brain according to any of the previous paragraphs, characterized in that the device additionally includes an external stimulation device for providing - 20 044512 at least one of: visual stimulation, audio stimulation and/or virtual reality stimulation to the user's brain, wherein the external stimulation device is communicatedly connected to the control unit. 11. The brain interface device as claimed in any one of the preceding claims, wherein the input/output device includes a safety device, wherein the safety device prohibits the delivery of any brain stimuli to the electrode device and recording from the electrode device if the device malfunctions. 12. A method of using a brain interface device that provides, during operation, monitoring of brain activity and stimulation of the user's brain, characterized in that the method includes the steps of: (i) using one or more power supplies to supply electrical power to the input/output device and the data processing device; (ii) placing or positioning the headgear device on the user's head, wherein the headgear device comprises an electrode device comprising an electrode array that makes electrical contact with the user's scalp, in operation, to detect electrical signals therefrom and to apply brain stimuli to it; (iii) using an input/output device to receive electrical signals from at least one of the group of electrodes and to transmit brain stimuli through a brain stimulation protocol to at least one of the group of electrodes; (iv) using a data processing device to process the detected electrical signals received from the input/output device and to create a brain stimulation protocol corresponding to the received electrical signals, wherein the data processing device includes a memory module; and (v) comparing the received electrical signals and a predetermined set of reference data to create an analysis and applying at least one adaptive learning algorithm including a maximum power point tracking algorithm for analysis to create a brain stimulation protocol, wherein the adaptive learning algorithm iteratively adjusts the brain stimulation protocol in real time to apply customized brain stimuli to the user's scalp to produce the desired target electrical activity of the user's brain. 13. The method of claim 12, wherein the method comprises using a data processing device to update the specified set of reference data iteratively in real time and storing the updated specified set of reference data in a memory module. 14. The method of claim 12, wherein the method comprises using a data processing apparatus to analyze the received electrical signals in real time so that the electrical signals are detected on the user's scalp in concert with brain stimuli provided to the user. 15. The method of claim 12, wherein the method comprises using a data processing device to analyze electrical signals received from the input/output device temporarily, wherein the brain stimuli are supplied to the user. 16. The method according to any one of claims 12 to 15, characterized in that the method comprises using at least one of a group of electrodes of the electrode device for delivering brain stimuli to the scalp of the user, and to other parts of the user spatially distant from the scalp user. 17. The method according to any one of claims 12-16, characterized in that the method contains an arrangement for using a data processing device, but is not limited to, at least one adaptive learning algorithm implemented in at least the form of: executable software, digital hardware tools (e.g. FPGA, ASIC, custom chip design). 18. The method according to any one of claims 12-17, characterized in that at least one adaptive learning algorithm contains, but is not limited to, at least one of machine learning algorithms: k-nearest neighbors algorithm, regression analysis, ensemble of tree algorithms, maximum power point tracking, artificial neural network, deep convolutional neural network, recurrent neural network, reinforcement learning algorithm, random forest algorithm, recommender system, genetic algorithm, Q learning algorithm and deep Q learning algorithm, wherein at least one of these algorithms implemented in a closed loop system. 19. The method according to any one of claims 12 to 18, characterized in that the method comprises an arrangement for using a data processing device, but is not limited to, at least one adaptive learning algorithm for iteratively adjusting brain stimuli such that the electrical activity of the user's brain is adjusted to approximate target electrical brain activity as desired. -