CN117796821A

CN117796821A - Nerve feedback intervention method, device and system

Info

Publication number: CN117796821A
Application number: CN202410223865.8A
Authority: CN
Inventors: 潘东旎; 任珏静
Original assignee: Beijing Everything Chengli Technology Co ltd
Current assignee: Beijing Everything Chengli Technology Co ltd
Priority date: 2024-02-29
Filing date: 2024-02-29
Publication date: 2024-04-02

Abstract

The present disclosure provides a neural feedback intervention method, device and system. The nerve feedback intervention method applied to the mobile terminal comprises the following steps: receiving a first electroencephalogram signal sent by electroencephalogram recording equipment and used for a user in a training state; obtaining a first index value of a characteristic index according to the first electroencephalogram signal; normalizing the first index value according to the personalized feedback threshold value of the characteristic index corresponding to the user to generate a feedback signal; and executing corresponding feedback operation according to the feedback signal. According to the embodiment of the disclosure, the personalized feedback threshold is utilized instead of the fixed threshold for signal processing, so that the generated feedback signal is adapted to the control level of the brain electrical signal of the user, and therefore the problems of low brain electrical control feeling and low training participation motivation of the user in the training process can be improved, and the training effect of nerve feedback intervention training can be improved.

Description

Nerve feedback intervention method, device and system

Technical Field

The embodiment of the disclosure relates to the technical field of nerve feedback, in particular to a nerve feedback intervention method, device and system.

Background

The nerve feedback training is to give timely feedback/rewarding to the self-regulation condition (success or failure) of the brain nerve activity to form a learning effect, and to improve or strengthen the connection and efficiency of the nerve loop through nerve plasticity, and finally to improve/promote the behavior and function. The specific operation involves measuring the brain neural activity associated with a specific function in real time, and feeding the measurement result back to the person to be trained in real time in a visual, auditory, tactile or other manner, and helping the person to learn to autonomously regulate and improve the target brain neural activity through a certain training means (such as a reward mechanism). And the improvement of the target brain nerve activity can repair or promote the corresponding brain function.

Brain waves (EEG) are direct, real-time recordings of brain neural activity. Depending on the frequency, brain waves can be classified into the following types: delta (delta) waves, theta (theta) waves, alpha (alpha) waves, beta (beta) brain waves, gamma (gamma) waves, and the like. Depending on the brain state management, the β wave can be subdivided into SMR (sensory molor thythm, sensory-motor rhythm) wave, βl wave (low β wave), βh wave (Gaobo).

Nerve feedback training is one of the important schemes of ADHD (Attention deficit and hyperactivity disorder, attention deficit and hyperactivity disorder, i.e., hyperactivity disorder) non-pharmaceutical intervention systems, and electroencephalographic nerve feedback based on specific indicators (e.g., SMR wave reinforcement) is applied since seventies of the last century. In "guidelines for mental intervention evidence-based on children and adolescents" published by the american pediatric medical college in 2012, brain wave neurological feedback training is used as a first-line therapy for hyperactivity and its evidence-supported strength is rated as secondary. Numerous studies have shown that electroencephalographic nerve feedback has many advantages as an adjuvant therapy, including moderate improvement of symptoms (60% -80% effective rate), remedy of drug therapy deficiency (compliance, side effects, difficulty in maintenance), combined drug use, reduced drug use, durable efficacy (slow onset but prolonged onset), and rich effects (improvement of symptoms as well as possibly the benefits of neuroplasticity, cognitive function, intelligence, other social functions).

However, the existing ADHD nerve feedback training system has the problems that the control of the brain electrical signal of the user is low in the training intervention process, and the training effect is poor.

Disclosure of Invention

The embodiment of the disclosure provides a nerve feedback intervention method, device and system.

In a first aspect, embodiments of the present disclosure provide a neural feedback intervention method, applied to a mobile terminal, the method including: receiving a first electroencephalogram signal sent by electroencephalogram recording equipment and used for a user in a training state; obtaining a first index value of a characteristic index according to the first electroencephalogram signal; normalizing the first index value according to the personalized feedback threshold value of the characteristic index corresponding to the user to generate a feedback signal; and executing corresponding feedback operation according to the feedback signal.

In some alternative embodiments, the characteristic index is selected from the following: spectral features, skewness, kurtosis, variance, sample entropy, C0 complexity, power spectrum, hjorth activity parameters, cross-frequency phase-amplitude coupling, and power spectrum proportions.

In some optional embodiments, before the normalizing the first index value according to the feedback threshold value corresponding to the personalization of the user, the method further includes: receiving a second electroencephalogram signal, sent by the electroencephalogram recording equipment, of the user in a resting state; and obtaining a second index value of the characteristic index according to the second electroencephalogram signal, and determining a personalized feedback threshold value of the characteristic index corresponding to the user according to the second index value.

In some optional embodiments, the determining, according to the second index value, that the feature index corresponds to a personalized feedback threshold of the user includes: acquiring descriptive parameters of the second index values, wherein the descriptive parameters at least comprise a mean MeanX and a standard deviation SD of a plurality of the second index values; and determining the feedback threshold, wherein the feedback threshold comprises an upper limit value XR and a lower limit value XL, wherein XR=MeanX+a×SD, XL=MeanX-a×SD, and a is a weighting coefficient.

In some optional embodiments, the personalized feedback threshold includes an upper limit XR and a lower limit XL, and normalizing the first index value according to the characteristic index corresponding to the personalized feedback threshold of the user to generate a feedback signal includes: if the first index value xi is smaller than the lower limit value XL, setting the feedback signal to 0; if the first index value xi is greater than the upper limit value XR, setting the feedback signal to 1; if the first index value xi is between the lower limit value XL and the upper limit value XR, setting the feedback signal to (xi-XL)/(XR-XL); wherein xi represents the ith first index value, i is a positive integer.

In some alternative embodiments, the performing the corresponding feedback operation according to the feedback signal includes: and adjusting non-procedural parameters of a training program currently operated by the mobile terminal according to the feedback signal.

In some optional embodiments, the adjusting, according to the feedback signal, a non-procedural parameter of a training program currently operated by the mobile terminal includes: determining the stimulus intensity corresponding to the feedback signal; and controlling the adjustment amplitude of the non-procedural parameter according to the stimulus intensity corresponding to the feedback signal.

In some alternative embodiments, the method further comprises: receiving an operation instruction input by a user through an operation key or a touch screen or voice; and controlling a main process of the training program according to the operation instruction, wherein the control difficulty or operability of the main process is associated with the non-procedural parameter.

In some alternative embodiments, the method further comprises: transmitting the identification information of the current training program to a server; and receiving the index recommended by the server and corresponding to the training program, and determining the index recommended by the server as the characteristic index.

In some alternative embodiments, the method further comprises: and sending the second index value to the server, wherein the second index value is stored by the server into a front-end database to serve as the basis of the recommended index.

In some alternative embodiments, the metrics recommended by the server are: the index value of the user is in an abnormal range compared with the index value of other people, or the index which is recommended most historically.

In a second aspect, embodiments of the present disclosure provide a nerve feedback intervention method applied to an electroencephalograph apparatus including a single channel gel electrode, the method comprising: in response to the acquired brain electrical signals of a user, preprocessing the acquired brain electrical signals to remove non-brain electrical interference signals contained in the brain electrical signals; the preprocessed electroencephalogram signals are sent to a mobile terminal; the electroencephalogram signals comprise a first electroencephalogram signal of the user in a training state and a second electroencephalogram signal of the user in a resting state, the second electroencephalogram signal is used by the mobile terminal to generate a personalized feedback threshold, and the feedback threshold is used by the mobile terminal to normalize the first electroencephalogram signal to generate a feedback signal.

In some optional embodiments, the preprocessing the acquired electroencephalogram signals includes: removing high-frequency noise and low-frequency noise in the electroencephalogram signal by using an FIR filter; removing ocular artifacts in the electroencephalogram signals by using a differential algorithm module; and removing head movement artifacts in the electroencephalogram signals by utilizing a motion sensor.

In some optional embodiments, the electroencephalograph further includes two reference electrodes, and the preprocessing the acquired electroencephalogram signal includes: and acquiring a reference signal acquired by the reference electrode, and preprocessing the electroencephalogram signal based on the reference signal.

In a second aspect, embodiments of the present disclosure provide a neurofeedback intervention device applied to a mobile terminal, the neurofeedback intervention device comprising: the receiving module is configured to receive a first electroencephalogram signal sent by the electroencephalogram recording equipment and used for a user in a training state; the training module is configured to obtain a first index value of a characteristic index according to the first electroencephalogram signal; normalizing the first index value according to the personalized feedback threshold value of the characteristic index corresponding to the user to generate a feedback signal; and executing corresponding feedback operation according to the feedback signal.

In a fourth aspect, embodiments of the present disclosure provide a nerve feedback intervention device applied to an electroencephalograph including a single channel gel electrode, the nerve feedback intervention device including: the preprocessing module is configured to respond to the acquired brain electrical signals of a user and preprocess the acquired brain electrical signals so as to remove non-brain electrical interference signals contained in the brain electrical signals; and the sending module is configured to send the preprocessed electroencephalogram signals to the mobile terminal.

In a fifth aspect, embodiments of the present disclosure provide a mobile terminal, including: one or more processors; and a storage device having one or more programs stored thereon, which when executed by the one or more processors, cause the one or more processors to implement the method as described in any of the implementations of the first aspect.

In a sixth aspect, embodiments of the present disclosure provide an electroencephalograph apparatus including: a single channel gel electrode; one or more processors; and a storage device having one or more programs stored thereon, which when executed by the one or more processors, cause the one or more processors to implement a method as described in any implementation of the second aspect.

In a seventh aspect, embodiments of the present disclosure provide a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by one or more processors, implements a method as described in any of the implementations of the first aspect and/or a method as described in any of the implementations of the second aspect.

In an eighth aspect, embodiments of the present disclosure provide a neurofeedback intervention system comprising a mobile terminal as described in any of the implementations of the fifth aspect and an electroencephalographic recording apparatus as described in any of the implementations of the sixth aspect.

In order to solve the problems that the existing ADHD nerve feedback training system has low control performance and poor training effect on the brain electrical signal of a user in the training intervention process, the embodiment of the disclosure provides a nerve feedback intervention method, a device and a system.

Drawings

Other features, objects and advantages of the present disclosure will become more apparent upon reading of the detailed description of non-limiting embodiments made with reference to the following drawings. The drawings are only for purposes of illustrating particular embodiments and are not to be construed as limiting the invention. In the drawings:

FIG. 1 is a system architecture diagram of one embodiment of a neurofeedback intervention system in accordance with the present disclosure;

FIG. 2 is a timing diagram of one embodiment of a neurofeedback intervention method in accordance with the present disclosure;

FIG. 3 is a general flow chart of one embodiment of a neurofeedback intervention method in accordance with the present disclosure;

FIG. 4 is a core flow diagram of one embodiment of a neurofeedback intervention method in accordance with the present disclosure;

FIGS. 5 and 6 are schematic diagrams of one embodiment of a game according to the present disclosure;

FIG. 7 is a schematic illustration of a process of evaluating recommendation indicators, according to the present disclosure;

FIG. 8 is a schematic diagram of one embodiment of a game setting metrics interface in accordance with the present disclosure;

FIG. 9 is a flowchart of one embodiment of a neurofeedback intervention method applied to a mobile terminal in accordance with the present disclosure;

FIG. 10 is a flowchart of one embodiment of a neurofeedback intervention method applied to an electroencephalographic recording apparatus according to the present disclosure;

FIG. 11 is a schematic structural view of one embodiment of a neurofeedback intervention device applied to a mobile terminal in accordance with the present disclosure;

FIG. 12 is a schematic structural view of one embodiment of a neurofeedback intervention device applied to an electroencephalograph according to the present disclosure;

fig. 13 is a schematic diagram of a computer system suitable for use in implementing an embodiment of the present disclosure in a mobile terminal or an electroencephalographic recording apparatus.

Detailed Description

The present disclosure is described in further detail below with reference to the drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the invention and are not limiting of the invention. For convenience of description, only a portion related to the present invention is shown in the drawings. In addition, it should be noted that, without conflict, the embodiments of the present disclosure and features of the embodiments may be combined with each other. The present disclosure will be described in detail below with reference to the accompanying drawings in conjunction with embodiments.

Currently, the neurofeedback intervention system with respect to ADHD mainly involves three components:

(1) The electroencephalogram acquisition module is used for recording electroencephalogram signals of a trainer in real time;

(2) The electroencephalogram signal index real-time background calculation module;

(3) And the signal conversion and presentation module is used for converting the electroencephalogram signal index into a visual and audible feedback signal and feeding back to a trainer.

The technical scheme of the existing three modules is generally as follows:

1) Recording signals by adopting a wet electrode electroencephalogram electrode and a large (non-portable) amplifier or a dry electrode head ring;

2) The main stream criteria of the current neurofeedback intervention system with respect to ADHD mainly comprise theta/beta ratio TBR (decrease theta and/or increase beta power at forehead and midline positions), sensory-motor rhythm (SMR), cortex slow potential (SCP, intervention of preparation potential for the pre-mission attention phase for the cortex to be slower); other potentially effective intervention indicators, alpha enhancement training, etc. are also reported in some of the published literature;

3) In an independent computer or other terminal display system, real-time electroencephalogram signal indexes are converted into visual and auditory signals according to a certain rule and fed back to a trainer, and the trainer automatically regulates the visual or auditory signals according to requirements to try to control and regulate the visual or auditory signals.

Neurofeedback intervention for ADHD is a full potential area, but there are several major challenges in the current specific application scenario, mainly including:

1. the traditional nerve feedback technical scheme aiming at ADHD individuals is mainly based on large-scale electroencephalogram acquisition and amplification equipment at a hospital end or a mechanism and an electroencephalogram cap based on a wet electrode, but a great amount of evidence of demonstration research shows that although the nerve feedback intervention is beneficial to ADHD children, the effect is slow. Changes are observable only after about 40 sessions after adherence to long training. To achieve an observable training benefit, if 2 times per week, it takes at least 5 months, with the consent of enormous costs to go to institutions and hospital commutes. In addition, intensive training (e.g., daily training) helps maximize the benefits. Therefore, a portable self-help neurofeedback intervention system is very urgent.

2. Currently, most of existing portable electroencephalogram acquisition devices adopt the design of a dry electrode brain ring, the signal-to-noise ratio of electroencephalogram signals is low, the index output based on acquisition signals is unstable, and the anti-interference capability on the head movement signals is low. In addition, the use experience for ADHD children who need biofeedback intervention training is not friendly due to the dead weight of the dry electrode portable brain ring and the considerable discomfort of wearing. Therefore, based on the above problem, a nerve feedback intervention system which is comfortable to wear, has high signal acquisition precision and is suitable for ADHD children has quite a need.

3. In current neurofeedback intervention training, a significant fraction of the participants (estimated 20% -50%) are unable to gain control of Brain function (Brain waves) by neurofeedback even after a significant number of exercises, a phenomenon which was first seen in Brain-computer interface (BCI) studies, known as BCI blindness (BCI-ility), or BCI inefficiency (BCI-ineffaciyc). Participants in the neurofeedback intervention can also be divided into responders/non-responders, presenters/non-presenters, respectively. Solving the BCI blindness problem to avoid the frustrating and expensive training procedure is one of the biggest challenges in the invention of neurofeedback systems.

4. Since the nerve index does not correspond absolutely to a specific category of mental activity (e.g., attention). That is, for an individual, a change in a particular brain activity pattern does not initially correspond well to a subjectively certain state and feedback is completed, so that children often feel that they cannot control the feedback, losing interest and confidence in training.

In view of the challenges encountered in the application of the above-mentioned existing ADHD neurofeedback intervention system, the present disclosure is intended to solve the following technical problems:

1) The gel electrode is used as recording equipment for training brain electricity real-time acquisition by nerve feedback intervention, so that the light weight and portability of the nerve feedback intervention system are improved to the maximum extent.

2) According to the historical test data, a self-adaptive system based on gel electrode ADHD nerve feedback is designed and realized to improve the key problem of low brain electricity control sense in the training process.

An engineering solution to improve the trainability of the neural feedback may be to design an adaptive system of the neural feedback, which should contain multiple layers: 1) Establishing a multi-level attention training index; 2) Self-adaptive training index recommendation; 3) Adaptive feedback threshold setting. The logic of the self-adaptive feedback is to obtain a personalized feedback parameter system instead of a fixed feedback parameter system by utilizing the history test data of the children, thereby improving the problem of low controllability of the self-brain electrical signals in the training intervention process. This may also be a key element in improving the motivation for individual training and improving the training effect.

3) By innovatively designing the form of brain electrical feedback, the nerve feedback and the active motion control are combined, so that the motivation and the involvement level of individual training are improved.

In fact, the involvement and motivation for training are very important predictors for the benefit of training intervention, so it is critical to improve the child's control while training by a means. This improvement in control is, as mentioned above, related to engineering problems in the feedback loop, i.e. to build an adaptive feedback system, and also to adding elements of high "control" in the design of a specific feedback termination. For example, the link A in the nerve feedback control game is operated according to the electroencephalogram signal index, and the parallel link B is operated by the real motion with higher sense of body control. Thus, the control feeling of the whole nerve feedback system and the investment and the entanglement feeling during the nerve feedback intervention training can be greatly improved.

4) According to the characteristics of the gel electrode signal record and the character characteristics of the gel electrode signal record embedded in the ADHD nerve feedback intervention system, a self-adaptive systematic nerve feedback intervention flow and protocol suitable for the gel electrode are designed.

Referring to fig. 1, fig. 1 illustrates an exemplary system architecture of one embodiment of a neurofeedback intervention system 100 of the present disclosure.

As shown in fig. 1, the neurofeedback intervention system 100 may include an electroencephalographic recording device 101 and a mobile terminal 102. In some alternative embodiments, the neurofeedback intervention system 100 can further comprise a server 103 and a network 104. The network 104 is a medium used to provide a communication link between the electroencephalographic recording apparatus 101, the mobile terminal 102, and the server 103. The network 104 may include various connection types, such as wired, wireless communication links, or fiber optic cables, among others.

The electroencephalogram recording apparatus 101 is used for acquiring an electroencephalogram signal of a user, preprocessing the acquired electroencephalogram signal, and transmitting the preprocessed electroencephalogram signal to the mobile terminal. The electroencephalograph 101 includes an electroencephalogram electrode for acquiring an electroencephalogram signal. For example, the electroencephalograph 101 can employ a gel electrode as an electroencephalogram electrode. A gel electrode is a type of semi-dry electrode that includes a conductive material (e.g., silver) and a gel material with which an electrolyte (e.g., water) within the gel material may function to electrically conduct to reduce impedance. In addition, the gel material can deform along with the action of a human body to a certain extent, so that the gel material can be closely attached to the skin of the human body. The gel electrode may take the form of a patch. In some alternative embodiments, the electroencephalographic device 101 can also employ wet or dry electrodes. The electroencephalograph 101 may further include a signal processing device for preprocessing an electroencephalogram signal, and a signal transmission device for transmitting a signal.

The mobile terminal 102 may be a variety of electronic devices having a display screen including, but not limited to, smartphones, tablets, laptop portable computers, desktop computers, and the like. The mobile terminal 102 is configured to receive an electroencephalogram signal sent by the electroencephalogram recording apparatus 101, and perform a corresponding feedback operation according to the received electroencephalogram signal.

The server 103 may be a server providing various services. The server 103 may be hardware or software. When the server 103 is hardware, it may be implemented as a distributed server cluster composed of a plurality of servers, or may be implemented as a single server. When the server is software, it may be implemented as a plurality of software or software modules (e.g., to provide distributed services), or as a single software or software module. The present invention is not particularly limited herein.

The nerve feedback intervention system can be used for conducting nerve feedback intervention training on a user, helping the user to conduct autonomous regulation and improvement on brain nerve activity, and repairing or improving corresponding brain functions. The user may be ADHD patients, such as ADHD children, or other brain dysfunction people with intervention by nerve feedback.

Referring to fig. 2-4, fig. 2 illustrates a timing 200 of one embodiment of a neurofeedback intervention method according to the present disclosure, fig. 3 illustrates an overall flowchart of one embodiment of a neurofeedback intervention method according to the present disclosure, and fig. 4 illustrates a core flowchart of one embodiment of a neurofeedback intervention method according to the present disclosure. The neurofeedback intervention method of embodiments of the present disclosure may be implemented by a neurofeedback intervention system 100 as shown in fig. 1.

The sequence 200 as shown in fig. 2 may include the following stages and steps:

1. stage of front test

The nerve feedback intervention method is suitable for nerve feedback intervention training of users such as ADHD children, and a specific training scheme is to provide a training program for the users to operate, so that the children control the brain electrical signals of the children to achieve the aim in man-machine interaction. In the existing scheme, fixed index thresholds are adopted for different people, but because the brain electrical level individuals of each person are very different, the control feeling of partial users in the training process is low, and the training cannot be completed well. Therefore, the method of the embodiments of the present disclosure is divided into two phases of pre-measurement and training, wherein the pre-measurement phase is mainly used for determining a feedback threshold value corresponding to the personalization of the user; in the training stage, the personalized feedback threshold value determined in the pre-measurement stage is combined for formal training, so that the control feeling of a user in the training process is improved, and the training is truly benefited.

The pre-test phase may include the steps of:

step 211, the electroencephalogram recording equipment collects electroencephalogram signals of a user, performs preprocessing, and sends the preprocessed high-quality single-channel electroencephalogram signals to the mobile terminal.

In the front measurement stage, the collected electroencephalogram signal is an electroencephalogram signal of a user in a resting state, and is called a second electroencephalogram signal in the text in order to be conveniently distinguished from other electroencephalogram signals.

Here, the electroencephalogram recording apparatus may include a gel electrode that is attached to the forehead She Weizhi (forehead position) of the user, and performs electroencephalogram signal acquisition in a single-channel manner to obtain a single-channel electroencephalogram signal. The gel electrode may be referred to as a single channel gel electrode or a single channel frontal lobe gel electrode. The electroencephalogram signal collected by the electroencephalogram recording equipment is a time signal sequence.

In some alternative embodiments, the electroencephalograph may further include two reference electrodes, which are attached to the forehead of the user and are located on either side of the single channel gel electrode. The reference electrode may also be a gel electrode. And the electroencephalogram signals acquired by the reference electrode are used as reference signals.

After the electroencephalogram recording equipment collects the electroencephalogram signals of the user, preprocessing the collected electroencephalogram signals in real time, and the purpose of removing non-electroencephalogram interference signals contained in the electroencephalogram signals is to obtain high-quality electroencephalogram signals.

In some alternative embodiments, the step of preprocessing the acquired brain electrical signals may include:

(1) And removing high-frequency noise and low-frequency noise in the electroencephalogram signal by using a FIR (Finite Impulse Response) filter.

The FIR filter may be implemented as a band pass filter (FIR band pass filter) to remove high frequency noise and low frequency noise in the electroencephalogram signal. The FIR filter may be implemented in hardware or software, without limitation.

(2) And removing the ocular artifacts in the electroencephalogram signals by utilizing a differential algorithm module.

Since the electro-oculogram signal is far orders of magnitude larger than the electroencephalogram signal, it is the most dominant source of pollution for electroencephalogram signals, especially frontal lobe electroencephalogram signals. Removal of ocular artifacts (contamination) is particularly important for single channel electroencephalograms. Removing ocular artifacts using a differential algorithm module is one way in brain electrical signal preprocessing. The differential algorithm module may be implemented in hardware or software, and is not limited herein.

(3) And removing head movement artifacts in the electroencephalogram signals by using a motion sensor.

Another source of pollution in brain electrical signals is the electromyographic signals due to muscle movement, and signal drift due to head movements, collectively known as head movement artifacts. In order to remove the head movement artifact, a motion sensor built in the electroencephalogram recording equipment can be used for recording head movement signals, and then muscle movement signals (electromyographic signals) convolved in the electroencephalogram signals are stripped so as to extract cleaner electroencephalogram signals. Here, the motion sensor may employ, for example, a multi-spiral motion sensor.

The brain electrical signals are very weak, and the extraction is carried out, and one necessary step is to remove various non-brain electrical interference signals through pretreatment. Removing ocular artifacts is one of the key links. Removing (or correcting) head movement artifacts is another key element. Through the steps, after various non-brain electrical interference signals are removed, high-quality single-channel brain electrical signals can be obtained. Here, the removal of the non-electroencephalogram interference signal by preprocessing is a function of the electroencephalogram recording apparatus itself.

In some alternative embodiments, the preprocessing step may use the left and right reference electrodes as an aid, and the reference signals acquired by the reference electrodes are acquired and used as references of the electroencephalogram signals, and preprocessing is performed on the electroencephalogram signals based on the reference signals. For example, the reference signal may be used as a reference signal recording point for ocular artifact removal during processing by the differential calculation module. Here, by employing the reference signal acquired by the reference electrode in the preprocessing, the effect of the preprocessing can be improved.

After preprocessing is completed by the electroencephalogram recording device and the high-quality single-channel electroencephalogram signal is obtained, the preprocessed electroencephalogram signal can be sent to the mobile terminal in a wireless transmission mode (or in a wired transmission mode).

The above description has been made of the process of the electroencephalogram recording apparatus for collecting and preprocessing an electroencephalogram signal.

It should be noted that in many electroencephalography and neurofeedback devices, multichannel recording is employed, and thus is not portable. The electroencephalogram recording device disclosed by the invention adopts a single-channel gel electrode, and is very small. The single channel means that only one electroencephalogram electrode is attached to the head of a subject.

It should also be noted that, the embodiment of the disclosure adopts the gel electrode to collect the brain electrical signal, which can have the following advantages:

a. the electroencephalogram signals acquired by the gel electrode are more stable (compared with a dry electrode head ring), the signal to noise ratio is higher, and the extraction index is simpler.

b. Forehead lobe single channel recording. The single-channel gel electrode is attached to the forehead She Weizhi of a user, is small and light, has better resistance to noise such as head movement and the like, and compared with a traditional device (such as an electroencephalogram cap), the gel electrode is lower in constraint feeling and better in experience when worn by the user (such as an ADHD child), and the electroencephalogram recording device is light and can provide an important effect for improving the overall benefit of the system in consideration of the characteristic of the multi-impulse of the ADHD child.

As described above, in this step, the electroencephalograph collects the second electroencephalogram signal of the user in the resting state, performs preprocessing to remove the non-electroencephalogram interference signal, and then transmits the preprocessed second electroencephalogram signal to the mobile terminal.

Step 212, the mobile terminal receives the electroencephalogram signal sent by the electroencephalogram recording equipment, and performs electroencephalogram feature extraction to obtain an index value of the feature index.

The mobile terminal may be deployed with an API (Application Programming Interface ) for signal recording and signal computing and feedback interfaces, which may be used for data interaction with an electroencephalographic recording device, for example, for receiving an electroencephalographic signal transmitted by the electroencephalographic recording device in a wireless transmission manner. In the front measurement stage, the electroencephalogram signal received by the mobile terminal is a preprocessed second electroencephalogram signal of the user in a resting state, and the mobile terminal can record and store the received second electroencephalogram signal as front measurement data.

After receiving the electroencephalogram signals, the mobile terminal firstly performs calculation of electroencephalogram feature extraction. The electroencephalogram signals acquired and preprocessed by the electroencephalogram recording equipment are output in real time every second. Aiming at the electroencephalogram signals output in real time, the mobile terminal carries out calculation of electroencephalogram feature extraction in real time to obtain feature indexes. Alternatively, the index value of the corresponding feature index may be output every set period (for example, three seconds, and the time length of the three seconds may be customized). For example, n feature indexes may be extracted, and then n index values are output every three seconds, where n is a positive integer, for example, n=8.

In the embodiment of the disclosure, the extracted characteristic index may be selected from various power spectrum characteristics, various linear and nonlinear indexes, ratios thereof, and the like, and may include, by way of example: spectrum characteristics, skewness, kurtosis, variance, sample entropy, C0 complexity, power spectrum, hjorth activity parameters, cross-frequency phase-amplitude coupling, and power spectrum proportions, etc., as described in one-to-one.

However, it should be noted that the above-mentioned feature indexes and the extraction method thereof may be implemented by referring to the prior art. The following is a brief description.

1. Spectral features.

The method for obtaining the frequency spectrum characteristic comprises the fast Fourier transformation, and the electroencephalogram signal is converted into a frequency domain from a time domain. Assume that an electroencephalogram signal is expressed as:

where x (N) represents the high-quality single-channel electroencephalogram signal obtained in step 210, N represents the sampling time points, and N represents the number of sampling time points.

The spectral features can be represented by X (k) as follows:

here, k represents a frequency.

The extraction of each frequency band of the electroencephalogram signal follows the following specifications:

Δ（delta）= 1-4 hz

θ（theta） = 4-8 hz

α（alpha） = 8-12 hz

β（beta）= 12- 30 hz

γ（gamma） = 30-55 hz

among other things, the theta/beta ratio may be used as a conventional characteristic index for ADHD training.

2. And acquiring the skewness of the signal in a specified number of seconds (set duration).

The calculation formula is as follows:

wherein xi represents the electroencephalogram signal corresponding to the ith sampling time point, mu represents the mean value, sigma represents the standard deviation, and E represents the mean value operation.

3. Kurtosis of the signal within a specified number of seconds (set duration) is obtained.

The calculation formula is as follows:

4. the variance of the signal over a specified number of seconds (set duration) is obtained.

The calculation formula is as follows:

where Σ represents the summation operation.

5. Sample entropy of the signal for a specified number of seconds (set duration) is obtained.

Sample entropy is a method used to measure the complexity of time series, which is very sensitive to small changes in the signal and is therefore commonly used for analysis of brain electrical signals, reflecting the likelihood of new patterns being generated in the brain, and also for the recognition of attention.

The calculation formula is as follows:

where Dm (r) represents the probability that two electroencephalogram signal time sequences match m points, and dm+1 (r) represents the probability that two electroencephalogram signal time sequences match m+1 points.

6. The C0 complexity of the signal is obtained for a specified number of seconds (set duration).

C0 Complexity is used to describe the proportion of irregular components in an electroencephalogram signal, the greater the proportion, the closer the time domain signal is to a random sequence, and therefore the greater the complexity.

The calculation formula is as follows:

7. and acquiring the power spectrum entropy of the signal in the specified seconds.

The power spectrum entropy is a sequence of power densities, and the entropy of the power spectrum (called power spectrum entropy) can be calculated quickly by fourier transformation. The power spectral entropy can be used to analyze the timing signals in the EEG data and serve as a physical index for estimating the quality and intensity of brain activity, the greater the entropy, the more active the brain.

The calculation formula is as follows:

wherein PD (i) represents the probability density distribution of the power spectrum, which can be obtained by normalizing the power spectrum to the total power.

8. The Hjorth activity parameter of the signal is obtained for a specified number of seconds.

The Hjorth activity parameter is a time domain feature proposed by Hjorth, and is generally used for analyzing the amplitude change of an electroencephalogram signal in the time domain, and comprises 3 important parameters: mobility, and complexity. The activity parameter can measure the change of the brain electrical amplitude.

The calculation formula is as follows:

here, x (T) is the electroencephalogram signal x (N) described above, T is N, and T is N.

9. Cross frequency phase-amplitude coupling (phase-amplitude coupling, PAC) within the specified signal is extracted.

The prior art reports that there is a substantial synchronization between the PAC between theta-gamma and alpha-gamma, i.e., between the phase of the low band EEG and the amplitude/power fluctuations of the high band. By low and high frequencies are meant, for example, theta-gamma, which is two distinct frequency bands.

The 1 st PAC measurement index was proposed by researchers such as canola, and should be the most widely used PAC index at present, which is defined as follows:

where n represents the total number of time points in the EEG data, t represents the time point, at represents the power of the high band EEG at time t, phit represents the phase of the low band EEG at time t, and i represents the complex unit. Here, the PAC index may take any value greater than 0.

The 2 nd commonly used PAC index is proposed by Cohen, which normalizes this index to be a synchronization index (synchronization index, SI), the SI being defined by the following formula:

where n represents the total number of time points in the EEG data, t represents the time point, phi lt represents the phase of the low band EEG at time t, and phi ut represents the phase of the power time series of the high band EEG at time t. Since SI is a complex number, the absolute value of SI (modulo the complex number) is typically used to characterize PAC size. Here, the SI absolute value is in the range of [0, 1]. The two PAC index calculations described above require extraction of the phase of the low band EEG and the power information of the high band EEG, and may be implemented by wavelet transform or hilbert transform.

10. Power spectrum ratio index.

The power spectral density index may include:

TBR = P (theta) / P (beta)

ABR = P (alpha) / P (beta)

BATR = P (beta) /[ P (alpha) + P (theta)]

ARR = 1/P (alpha)

wherein P refers to the P value.

In the above, it is described how to extract the above various feature indexes from the electroencephalogram signal, such as spectral features, skewness, kurtosis, variance, sample entropy, C0 complexity, power spectrum, hjorth activity parameters, cross-frequency phase-amplitude coupling, power spectrum ratio, and the like.

In the method of the embodiment of the disclosure, at least one of the characteristic indexes can be selected as the characteristic index for training in the subsequent training stage. It should be noted that existing ADHD neurofeedback intervention training systems typically use only tbr=p (theta)/P (beta) of the above-mentioned feature indicators as the training feature indicators. The scheme of the present disclosure may further employ skewness, kurtosis, sample entropy, C0 complexity, power spectrum entropy, hjorth activity parameter, cross-frequency phase-amplitude coupling in ADHD and other feature indexes as feature indexes for training. The use of these indices has the advantage over the classical index TBR in improving the concentration of children.

As described above, in this step, the obtained second electroencephalogram signal is subjected to feature extraction to obtain the index value of each feature index, and in order to facilitate distinction from other index values, the index value obtained from the second electroencephalogram signal is referred to herein as a second index value.

Step 213, determining, according to the obtained second index value of each feature index, a personalized feedback threshold value corresponding to each feature index to the user.

The step determines that each characteristic index corresponds to a personalized feedback threshold value of the user, and aims to improve the control feeling of the user in the training process, so that the training is truly benefited.

In some alternative embodiments, the step of determining the personalized feedback threshold may specifically comprise the sub-steps of:

(1) front test data sampling

In the last step: the mobile terminal receives the preprocessed second electroencephalogram signal sent by the electroencephalogram recording equipment, records the preprocessed second electroencephalogram signal as front measurement data, and the front measurement data are time signal sequences. And extracting the electroencephalogram characteristics of the front measurement data to obtain an index value (namely a second index value) of the characteristic index. Alternatively, a second index value may be generated and output every predetermined period of time, for example, 3 seconds, and a set of (for example, 20) second index values may be obtained after a certain period of time (for example, 60 seconds).

In this step, each second index value of the characteristic index is obtained and stored to form a data set. And further, calculating descriptive parameters, such as minimum, maximum, calculated mean, standard deviation, etc., of the second index values in the dataset.

Taking the collected delta index as an example, in the previous measurement data, the data set obtained according to sampling every 3 seconds is:

X=[22.45， 30.54，..50.45, ...10.34......40.46]。

the following descriptive parameters may be calculated:

maximum maxx=50.45;

minimum minx=10.34;

the arithmetic mean meanx=20.34;

standard deviation sd=17.45.

(2) Determining personalized feedback thresholds

Here, the personalized feedback threshold may include an upper limit value and a lower limit value of the feature index. The upper and lower limits of each characteristic index may be determined from the average and standard deviation.

For example, the upper limit value and the lower limit value of each feature index may be determined as:

upper limit value: xr=meanx+a×sd

And (3) lower limit planting: xl=meanx-a SD

Where a is a weighting coefficient, which is a real number, and a=1.96 is exemplary.

Taking the collected delta index as an example, in the front measurement data, the data set obtained by sampling every 3 seconds is:

X=[22.45， 30.54，..50.45, ...10.34......40.46]。

then there are:

MeanX=20.35；

SD= 17.45；

upper limit value of characteristic index:

XR= 20.35+1.96*17.45=54.2；

lower limit value of characteristic index:

XL=20.35-1.96*17.45=-13.8。

as described above, this step describes how to determine the feedback threshold of the characteristic index corresponding to the personalization of the user. Here, the personalized feedback threshold is determined from the second electroencephalogram signal of the user in the resting state, a value associated with the second electroencephalogram signal of the user in the resting state.

It should be noted that, for a certain user, each characteristic index has its own feedback threshold; for a determined characteristic index, there is a feedback threshold for each user for which the user has a personalization. After determining that each characteristic index corresponds to the personalized feedback threshold of the user, the mobile terminal can store the determined personalized feedback threshold locally or send the feedback threshold to the server.

2. Training phase

In the training stage of the embodiment of the present disclosure, a training program (such as a game, which is described below as an example) is provided on the mobile terminal side, and the game is operated by a user, and the user achieves the goal of the game by controlling the electroencephalogram signals of the user during the operation. Here, the type of game is not limited.

The training phase may include the steps of:

in step 221, the mobile terminal determines a training feature index (which may be simply referred to as a training index), and obtains a feedback threshold value corresponding to the individuation of the user.

As described in the previous measurement stage, various characteristic indexes can be extracted from the electroencephalogram signals. All the characteristic indexes are not needed in the training stage, and the training effect of different characteristic indexes for different games can be different. Thus, one or more of the plurality of characteristic indices may be selected as the training index.

Here, there are a number of ways to determine the training metrics: firstly, a default mode of the system is adopted, and the mobile terminal has predetermined training indexes; providing a selection interface for a user, namely providing a selection interface for the mobile terminal, listing a plurality of characteristic indexes, and selecting at least one characteristic index from the characteristic indexes as a training index by the user; thirdly, evaluating and recommending modes, and recommending at least one characteristic index by the system as a training index.

The mobile terminal may obtain, from a local store (or server), a feedback threshold corresponding to the personalization of the user, based on the determined training index. In general, the feedback threshold may include, for example, an upper limit value and a lower limit value.

Then, the game starts and formally goes into the training process.

Step 222, the electroencephalogram recording device collects electroencephalogram signals of the user, performs preprocessing, and sends the preprocessed high-quality single-channel electroencephalogram signals to the mobile terminal.

After the game is started, the electroencephalogram recording equipment can acquire electroencephalogram signals of a user in a training state in real time, and after preprocessing, the acquired high-quality single-channel electroencephalogram signals are sent to the mobile terminal. For convenience of distinction, the electroencephalogram signal of the user in the training state obtained in this step is referred to herein as a first electroencephalogram signal.

For a more detailed description of this step, please refer to the description of step 211 above, the difference is only that: in step 211, the second electroencephalogram signal of the user in the resting state is collected, and the first electroencephalogram signal of the user in the training state is collected in this step, which is not described in detail here.

Step 223, the mobile terminal receives the electroencephalogram signal (first electroencephalogram signal) sent by the electroencephalogram recording device and used for the user in the training state, and performs electroencephalogram feature extraction to obtain the index value of the feature index (i.e. training index) for training.

In this step, the received first electroencephalogram signal is subjected to electroencephalogram feature extraction to obtain an index value of the training index, which is called a first index value for convenience of distinguishing.

Here, the training index may be selected from the following indexes: spectral features, skewness, kurtosis, variance, sample entropy, C0 complexity, power spectrum, hjorth activity parameters, cross-frequency phase-amplitude coupling, power spectrum proportions, and the like.

For a more detailed description of this step, please refer to the description of step 212 above, the difference is only that: in step 212, the second electroencephalogram signal is subjected to feature extraction to obtain a second feature value, and in this step, the first electroencephalogram signal is subjected to feature extraction to obtain a first feature value, which is not described in detail herein.

In step 224, the mobile terminal normalizes the first index value according to the personalized feedback threshold value corresponding to the training feature index to generate a feedback signal.

For the selected training index (i.e., training index), after the first index value of the training index is obtained, the first index value is normalized according to the personalized feedback threshold value (including the upper limit value XR and the lower limit value XL) corresponding to the user and the training index, so as to generate a feedback signal (or referred to as a feedback control parameter) matched with the electroencephalogram characteristic level of the user.

In some alternative embodiments, the feedback signal may be calculated according to the following formula:

wherein y (i) normalizes the processed feedback signal and xi is the first index value.

The value range of the feedback signal y (i) is 0-1.

From the above formula:

when the first index value xi is smaller than the lower limit value of the feedback threshold value, the value of y (i) is 0;

when the first index value xi is between the lower limit value and the upper limit value of the feedback threshold value, the value of y (i) is (xi-XL)/(XR-XL);

when the first index value xi is greater than the upper limit value of the feedback threshold value, y (i) takes a value of 1.

y (i) can be understood as a basic parameter of game control, the smaller the value, the smaller the game stimulus intensity, and the larger the value, the larger the game stimulus intensity.

In some alternative embodiments, the feedback signal y (i) corresponding to the training index xi may be mapped to the stimulus intensity. The stimulus intensity may be set empirically and continuously. For example, a five-gear stimulation intensity may be set, denoted by 1-5, respectively, with a larger number indicating a greater stimulation intensity. Referring to table 1, a mapping of feedback signal y (i) to stimulus intensity is shown.

TABLE 1

Step 225, executing corresponding feedback operation according to the feedback signal.

The nerve feedback intervention training method is characterized in that a training program is run on a mobile terminal for a user to operate, and in man-machine interaction, the user controls own brain electrical signals to achieve the training purpose. The specific training steps may include: the mobile terminal presents an interface of a training program; and the mobile terminal executes corresponding feedback operation according to the feedback signal generated in the previous step.

Wherein the training program includes, but is not limited to, a game, such as a game.

It should be noted that, the feedback operation in the embodiments of the present disclosure may be a feedback operation combined with an active operation. That is, on the one hand, the user can control the game by inputting an operation instruction manually (operating a key or touching a screen) or by voice like normal game play; on the other hand, the electroencephalogram signals can be used for controlling the game, namely, the electroencephalogram signals are utilized for adjusting and controlling a certain continuous non-procedural parameter in the game.

Here, the non-procedural parameter refers to a parameter that does not affect the game master process (main task) for one game. However, the non-procedural parameter is related to the control difficulty or operability of the main process of the game, and may affect the control difficulty of the game and the operability of the main process.

In the following, a small fish game is exemplified, the main objective of the game is to allow the child to collect as many small fish as possible on the sea floor, the collection of small fish is to click the small fish appearing on the game interface by hand, and the appearance of the small fish is not controlled by the brain signals of the individual users. In a game, the user's electroencephalogram feature indicators manipulate video visibility in the game (i.e., the intensity of bubbles in the subsea goggles, as shown in fig. 5 and 6). The user needs to collect fish at the sea bottom, but when the density of bubbles is relatively high (as shown in fig. 5), the small fish is shielded from view; only when the density of the bubbles is small (as shown in fig. 6) can the small fish be seen and thus clicked on to collect. That is, the goggles must be made clear for the small fish to be collected, and the degree of the clear goggles is controlled by the electroencephalogram signals of the user. Referring to fig. 5 and 6, when the index value of the characteristic index of a certain nerve feedback is low, the blocked level is high, and the target (small fish) cannot be presented; when the index value of the characteristic index of a certain nerve feedback is high, the blocked level is reduced, and the target object (small fish) is displayed and can be manually collected.

Here, the sharpness of the goggles is a non-procedural parameter. Although the main process of the game is to collect fish, the nerve feedback signal does not directly control fish collection, and the user still needs to complete the collection action by clicking. This mode of operation is a feedback operation in combination with active operation. Here, the active operation refers to an operation instruction input by a user through a manual (operation key or touch screen) or a voice. Here, the magnitude of the feedback signal controls the game in a non-direct (nonlinear) mapping.

In conventional neuro-feedback game designs, there is typically only a linear mapping of neural index value changes to visual or auditory stimuli, and the game is single in form (only one game parameter is controlled). For example, the sun is controlled to rise or fall by using the height of the brain electricity (a certain characteristic index). Under the condition, the user has no other controllable content and can only rely on brain electricity, and the game experience is poor and the training effect is not necessarily good at this time.

The mapping relation between the training content of the nerve feedback intervention training and the brain electrical characteristic index is non-unitary, namely, the game content (main process) is not controlled, but the game parameters (non-procedural parameters) are controlled, so that the input degree and the involvement feeling of a user can be greatly improved. The scheme can well reduce the boring feeling and the uncontrolled feeling of the traditional nerve feedback intervention training, achieve the combination of implicit learning and explicit control and promote the integral motivation of the training.

As described above, performing the corresponding feedback operation according to the feedback signal in this step may include: and adjusting non-procedural parameters of a training program currently operated by the mobile terminal according to the feedback signal.

In a further embodiment, the feedback signal may be associated with the stimulus intensity, and the step of adjusting the non-procedural parameter of the training program currently operated by the mobile terminal according to the feedback signal may include: determining the stimulus intensity corresponding to the feedback signal; and controlling the adjustment amplitude of the non-procedural parameter according to the stimulus intensity corresponding to the feedback signal.

In step 226, the mobile terminal receives the recommended index of the server, and determines the recommended index of the server as the training index.

In the embodiment of the disclosure, the training index may be determined by the user (a user selection manner) at the mobile terminal side, may be determined by default by the mobile terminal (a default manner of the system), and may be further evaluated by the server (an evaluation recommendation manner). The server can realize training index recommendation through big data quick matching formed by individual front measurement data. The function is also based on the portable characteristic of the gel electrode signal record, and the nerve feedback intervention system can accumulate a large amount of individual pre-measurement historical data to form a pre-measurement database, so as to provide personalized training index recommendation service for users such as ADHD children. For each training task, a training index may be selected. Different training metrics may correspond to different neural processes and abilities and thus may have different effects.

Here, the index recommended by the server may correspond to a training program currently operated by the mobile terminal. Optionally, the mobile terminal may send the identification information of the current training program to the server, and the server recommends an index corresponding to the current training program, and after receiving the index recommended by the server, the mobile terminal determines the index recommended by the server as the training index of the current training program.

Referring to FIG. 7, a process of recommending metrics in some embodiments is shown. In some alternative embodiments, the step may specifically include:

first, the front measurement data (second index value of training index) is sent to the server, and the front measurement data is stored in the front measurement database by the server as the basis for evaluating the recommended index.

Secondly, the server recommends indexes to the mobile terminal according to the front measurement data (the front measurement data of a plurality of users are integrated into big data) recorded in the front measurement database. The recommended index may be one or more, and when plural, may be ranked according to a predetermined rule. Alternatively, index recommendation or ranking may be based on the following principles: 1. abnormal principle: the index value of a characteristic index measured before a user individual is in an abnormal position in the crowd, namely, the recommended index is an index that the index value of the user individual is in an abnormal range (for example, more than 96% of plus or minus) compared with other people; 2. category mode principle: in certain categories, such as multiple ADHD, the index that is historically most recommended.

And finally, the mobile terminal receives the recommended index of the server and determines the recommended index as a training index.

Referring to FIG. 8, an interface is shown for a user to set an indicator by himself in some implementations. After a user opens a game on the mobile terminal, an index selection interface can be displayed on the game interface, and various characteristic indexes can be selected, wherein the selected indexes are used as training indexes of the game. In addition, two options of system defaults and assessment recommendation can be provided on the game interface, and when the user cannot determine the training index by himself, the default mode of the system can be selected, or the assessment recommendation mode (namely, the mode of recommending the index by the server) can be selected.

In the above, the neural feedback intervention method provided by the present disclosure is described. In general, the key points of the method include:

(1) The gel electrode is used as an electroencephalogram electrode of electroencephalogram recording equipment, portability and signal precision are improved, and certain innovation is achieved;

(2) Innovative multiple nerve feedback brain electrical characteristic indexes such as sample entropy, C0 complexity, power spectrum entropy, hjorth activity parameters, cross-frequency phase-amplitude coupling and the like, and brain electrical nerve feedback intervention training is applied;

(3) Setting a self-adaptive personalized feedback threshold value by utilizing the personal calendar pre-history data;

(4) Personalized training index recommendation based on pre-measurement data and big data;

(5) Non-procedural parameters are controlled using brain waves.

Technical effects achieved by the technical scheme of the present disclosure include, but are not limited to:

(1) The nerve feedback intervention system based on the single-channel gel electrode is portable and comfortable to wear, high in signal acquisition precision and strong in anti-interference signal, and is particularly used for ADHD children;

(2) The method can be applied to families, and can accumulate large-scale data to form personalized index recommendation based on big data;

(3) The self-adaptive feedback threshold setting is carried out based on the individual pre-historic measurement data, so that the control sense of users such as children in the nerve feedback intervention training is improved;

(4) And the game development is carried out by combining the nerve feedback and the active operation (active control), the participation degree and the motivation of the nerve feedback game are improved by the system individual, the frustration is reduced, the training control is improved, and the higher training benefit is obtained.

With continued reference to fig. 9, there is shown a flow 900 of one embodiment of a neurofeedback intervention method applied to a mobile terminal in accordance with the present disclosure, comprising the steps of:

Step 901, receiving a first electroencephalogram signal sent by electroencephalogram recording equipment and used for a user in a training state;

step 902, obtaining a first index value of a characteristic index according to a first electroencephalogram signal;

step 903, normalizing the first index value according to the feedback threshold value of the characteristic index corresponding to the individuation of the user, and generating a feedback signal;

step 904, executing corresponding feedback operation according to the feedback signal.

In this embodiment, the specific operations and the technical effects of steps 901 to 904 are substantially the same as those of steps 223 to 225 in the embodiment shown in fig. 1, and are not described herein.

In some alternative embodiments, the characteristic index is selected from the following indices: spectral features, skewness, kurtosis, variance, sample entropy, C0 complexity, power spectrum, hjorth activity parameters, cross-frequency phase-amplitude coupling, and power spectrum proportions.

In some alternative embodiments, the above-described process further includes, prior to step 903: receiving a second electroencephalogram signal sent by the electroencephalogram recording equipment when a user is in a resting state; and obtaining a second index value of the characteristic index according to the second electroencephalogram signal, and determining a personalized feedback threshold value of the characteristic index corresponding to the user according to the second index value.

In some alternative embodiments, determining, from the second index value, that the characteristic index corresponds to the personalized feedback threshold of the user comprises: acquiring descriptive parameters of the second index values, wherein the descriptive parameters at least comprise a mean MeanX and a standard deviation SD of a plurality of the second index values; determining a feedback threshold, wherein the feedback threshold comprises an upper limit value XR and a lower limit value XL, wherein xr=meanx+a×sd, xl=meanx-a×sd, and a is a weighting coefficient.

In some optional embodiments, the personalized feedback threshold includes an upper limit XR and a lower limit XL, and the normalizing the first index value according to the characteristic index corresponding to the personalized feedback threshold of the user, to generate the feedback signal includes: if the first index value xi is smaller than the lower limit value XL, setting the feedback signal to 0; if the first index value xi is greater than the upper limit value XR, setting the feedback signal to be 1; if the first index value xi is between the lower limit value XL and the upper limit value XR, the feedback signal is set to (xi-XL)/(XR-XL); where xi represents the i-th first index value, i being a positive integer.

In some alternative embodiments, step 904 may be performed as follows: and adjusting non-procedural parameters of a training program currently operated by the mobile terminal according to the feedback signal.

In some alternative embodiments, adjusting non-procedural parameters of a training program currently running on the mobile terminal based on the feedback signal includes: determining the stimulus intensity corresponding to the feedback signal; and controlling the adjustment amplitude of the non-procedural parameter according to the stimulus intensity corresponding to the feedback signal.

In some alternative embodiments, the above-described process further comprises: receiving an operation instruction input by a user through an operation key or a touch screen or voice; and controlling a main process of the training program according to the operation instruction, wherein the control difficulty or operability of the main process is related to the non-procedural parameter.

In some alternative embodiments, the above-described process further comprises: transmitting the identification information of the current training program to a server; and receiving the index recommended by the server and corresponding to the training program, and determining the index recommended by the server as a characteristic index.

In some alternative embodiments, the above-described process further comprises: and sending the second index value to the server, and storing the second index value into the front test database by the server as the basis of the recommended index.

In some alternative embodiments, the server recommended metrics are: the index value of the user is in an abnormal range compared with the index value of the other people, or the index which is recommended most historically.

According to the method provided by the embodiment of the disclosure, due to the fact that the personalized feedback threshold is utilized instead of the fixed threshold for normalization processing, the generated feedback signal is adapted to the control level of the brain electrical signal of the user, so that the problem that the brain electrical control feeling is low and the training participation motivation is low in the training process of the user can be solved, and the training effect of nerve feedback intervention training is improved.

With continued reference to fig. 10, there is shown a flow 1000 of one embodiment of a neurofeedback intervention method applied to an electroencephalographic recording apparatus according to the present disclosure, which can include a single channel gel electrode, the flow 1000 described above including the steps of:

step 1001, in response to the collected brain electrical signals of the user, preprocessing the collected brain electrical signals to remove non-brain electrical interference signals contained in the brain electrical signals;

step 1002, sending the preprocessed electroencephalogram signal to a mobile terminal; the electroencephalogram signals comprise first electroencephalogram signals of a user in a training state and second electroencephalogram signals of the user in a resting state, the second electroencephalogram signals are used by the mobile terminal for generating personalized feedback thresholds, and the feedback thresholds are used by the mobile terminal for carrying out normalization processing on the first electroencephalogram signals to generate feedback signals.

In this embodiment, the specific operations and effects of step 1001 and step 1002 are substantially the same as those of step 211 and step 222 in the embodiment shown in fig. 1, and will not be described herein.

In some alternative embodiments, step 1001 may be performed as follows: removing high-frequency noise and low-frequency noise in the electroencephalogram signal by using an FIR filter; removing ocular artifacts in the electroencephalogram signals by utilizing a differential algorithm module; and removing head movement artifacts in the electroencephalogram signals by using the motion sensor.

In some alternative embodiments, the electroencephalograph further includes two reference electrodes, and preprocessing the acquired electroencephalogram signals includes: and acquiring a reference signal acquired by a reference electrode, and preprocessing an electroencephalogram signal based on the reference signal.

According to the method provided by the embodiment of the disclosure, due to the fact that the gel electrode is adopted for electroencephalogram signal acquisition, the wearing portability and the comfort, the signal acquisition precision and the anti-interference capability of the electroencephalogram recording equipment can be improved; the method is suitable for household use and is beneficial to large-scale data accumulation.

With further reference to fig. 11, as an implementation of the method shown in the above figures, the present disclosure provides an embodiment of a neurofeedback intervention device, which corresponds to the method embodiment shown in fig. 9, and which is particularly applicable in a mobile terminal.

As shown in fig. 11, the neurofeedback intervention device 1100 of the present embodiment includes:

the receiving module 1101 is configured to receive a first electroencephalogram signal sent by the electroencephalogram recording device and used for a user in a training state;

the training module 1102 is configured to obtain a first index value of the characteristic index according to the first electroencephalogram signal; normalizing the first index value according to the personalized feedback threshold value of the characteristic index corresponding to the user to generate a feedback signal; and executing corresponding feedback operation according to the feedback signal.

In some alternative embodiments, neurofeedback intervention device 1100 further comprises:

the front measurement module 1103 is configured to receive a second electroencephalogram signal sent by the electroencephalogram recording device when the user is in a resting state; and obtaining a second index value of the characteristic index according to the second electroencephalogram signal, and determining a personalized feedback threshold value of the characteristic index corresponding to the user according to the second index value.

In some alternative embodiments, the front test module 1103 is further configured to: acquiring descriptive parameters of the second index values, wherein the descriptive parameters at least comprise a mean MeanX and a standard deviation SD of a plurality of the second index values; determining a feedback threshold, wherein the feedback threshold comprises an upper limit value XR and a lower limit value XL, wherein xr=meanx+a×sd, xl=meanx-a×sd, and a is a weighting coefficient.

In some alternative embodiments, the personalized feedback threshold includes an upper value XR and a lower value XL, the front test module 1103 being further configured to: if the first index value xi is smaller than the lower limit value XL, setting the feedback signal to 0; if the first index value xi is greater than the upper limit value XR, setting the feedback signal to be 1; if the first index value xi is between the lower limit value XL and the upper limit value XR, the feedback signal is set to (xi-XL)/(XR-XL); where xi represents the i-th first index value, i being a positive integer.

In some alternative embodiments, training module 1102 is further configured to: and adjusting non-procedural parameters of a training program currently operated by the mobile terminal according to the feedback signal.

In some alternative embodiments, training module 1102 is further configured to: determining the stimulus intensity corresponding to the feedback signal; and controlling the adjustment amplitude of the non-procedural parameter according to the stimulus intensity corresponding to the feedback signal.

In some alternative embodiments, training module 1102 is further configured to: receiving an operation instruction input by a user through an operation key or a touch screen or voice; and controlling a main process of the training program according to the operation instruction, wherein the control difficulty or operability of the main process is related to the non-procedural parameter.

In some alternative embodiments, training module 1102 is further configured to send identification information of the current training program to the server; and receiving the index recommended by the server and corresponding to the training program, and determining the index recommended by the server as a characteristic index.

In some alternative embodiments, training module 1102 is further configured to send a second index value to the server, which is stored by the server into the look-ahead database as a basis for the recommendation index.

It should be noted that, the implementation details and technical effects of each module in the nerve feedback intervention device provided in the embodiments of the present disclosure may refer to the descriptions of other embodiments in the present disclosure, which are not described herein again.

With further reference to fig. 12, as an implementation of the method illustrated in the above figures, the present disclosure provides an embodiment of a neurofeedback intervention device, which corresponds to the method embodiment illustrated in fig. 10, which is particularly applicable in an electroencephalographic recording apparatus comprising a single channel gel electrode.

As shown in fig. 12, the neurofeedback intervention device 1200 of the present embodiment includes:

a preprocessing module 1201 configured to perform preprocessing on the acquired electroencephalogram signal in response to the acquired electroencephalogram signal of the user to remove non-electroencephalogram interference signals contained in the electroencephalogram signal;

a transmitting module 1202 configured to transmit the preprocessed brain electrical signal to the mobile terminal; the electroencephalogram signals comprise first electroencephalogram signals of a user in a training state and second electroencephalogram signals of the user in a resting state, the second electroencephalogram signals are used by the mobile terminal for generating personalized feedback thresholds, and the feedback thresholds are used by the mobile terminal for carrying out normalization processing on the first electroencephalogram signals to generate feedback signals.

In some alternative embodiments, the preprocessing module 1201 is further configured to: removing high-frequency noise and low-frequency noise in the electroencephalogram signal by using an FIR filter; removing ocular artifacts in the electroencephalogram signals by utilizing a differential algorithm module; and removing head movement artifacts in the electroencephalogram signals by using the motion sensor.

In some alternative embodiments, the electroencephalographic recording apparatus further comprises two reference electrodes, the preprocessing module 1201 being further configured to: and acquiring a reference signal acquired by a reference electrode, and preprocessing an electroencephalogram signal based on the reference signal.

Referring now to FIG. 13, there is illustrated a schematic diagram of a computer system 1300 suitable for use in a mobile terminal or an electroencephalographic recording apparatus for implementing embodiments of the present disclosure. The computer system 1300 shown in fig. 13 is merely an example, and should not be construed as limiting the functionality and scope of use of the embodiments of the present disclosure.

As shown in fig. 13, a computer system 1300 may include a processor (e.g., a central processing unit, a graphics processor, etc.) 1301 that may perform various suitable actions and processes in accordance with programs stored in a Read Only Memory (ROM) 1302 or loaded from a storage 1308 into a Random Access Memory (RAM) 1303. In the RAM 1303, various programs and data necessary for the operation of the computer system 1300 are also stored. The processor 1301, the ROM 1302, and the RAM 1303 are connected to each other through a bus 1304. An input/output (I/O) interface 1305 is also connected to bus 1304.

In general, the following devices may be connected to the I/O interface 1305: input devices 1306 including, for example, a touch screen, touchpad, keyboard, mouse, camera, microphone, and the like; an output device 1307 including, for example, a Liquid Crystal Display (LCD), a speaker, a vibrator, and the like; storage 1308 including, for example, magnetic tape, hard disk, etc.; and communication means 1309. The communications apparatus 1309 can allow the computer system 1300 to communicate with other devices wirelessly or by wire to exchange data. While FIG. 13 illustrates a computer system 1300 having various devices, it should be understood that not all illustrated devices are required to be implemented or provided. More or fewer devices may be implemented or provided instead.

In particular, according to embodiments of the present disclosure, the processes described above with reference to flowcharts may be implemented as computer software programs. For example, embodiments of the present disclosure include a computer program product comprising a computer program embodied on a computer readable medium, the computer program comprising program code for performing the method shown in the flowcharts. In such an embodiment, the computer program may be downloaded and installed from a network via the communications device 1309, or installed from the storage device 1308, or installed from the ROM 1302. The above-described functions defined in the methods of the embodiments of the present disclosure are performed when the computer program is executed by the processor 1301.

It should be noted that the computer readable medium described in the present disclosure may be a computer readable signal medium or a computer readable storage medium, or any combination of the two. The computer readable storage medium can be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any of the foregoing. More specific examples of the computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the context of this disclosure, a computer-readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In the present disclosure, however, the computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, with the computer-readable program code embodied therein. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination of the foregoing. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: electrical wires, fiber optic cables, RF (radio frequency), and the like, or any suitable combination of the foregoing.

The computer readable medium may be contained in the electronic device; or may exist alone without being incorporated into the electronic device.

The computer readable medium carries one or more programs which, when executed by the electronic device, cause the electronic device to implement a biofeedback intervention method as shown in the embodiment of fig. 9 and its alternative implementation, and/or a biofeedback intervention method as shown in the embodiment of fig. 10 and its alternative implementation.

Computer program code for carrying out operations of the present disclosure may be written in one or more programming languages, including an object oriented programming language such as Java, smalltalk, C ++ and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computer (for example, through the Internet using an Internet service provider).

The flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.

The units involved in the embodiments described in the present disclosure may be implemented by means of software, or may be implemented by means of hardware. Where the name of the unit does not constitute a limitation on the unit itself in some cases, for example, the request generation unit may also be described as "a unit that generates a meeting query request in response to detecting a meeting query operation".

The foregoing description is only of the preferred embodiments of the present disclosure and description of the principles of the technology being employed. It will be appreciated by persons skilled in the art that the scope of the disclosure referred to in this disclosure is not limited to the specific combinations of features described above, but also covers other embodiments which may be formed by any combination of features described above or equivalents thereof without departing from the spirit of the disclosure. Such as those described above, are mutually substituted with the technical features having similar functions disclosed in the present disclosure (but not limited thereto).

Claims

1. A neural feedback intervention method applied to a mobile terminal, the method comprising:

receiving a first electroencephalogram signal sent by electroencephalogram recording equipment and used for a user in a training state;

obtaining a first index value of a characteristic index according to the first electroencephalogram signal;

normalizing the first index value according to the personalized feedback threshold value of the characteristic index corresponding to the user to generate a feedback signal;

and executing corresponding feedback operation according to the feedback signal.

2. The method of claim 1, wherein the characteristic index is selected from the following: spectral features, skewness, kurtosis, variance, sample entropy, C0 complexity, power spectrum, hjorth activity parameters, cross-frequency phase-amplitude coupling, and power spectrum proportions.

3. The method of claim 1, wherein prior to normalizing the first index value according to the characteristic index corresponding to the personalized feedback threshold value of the user, the method further comprises:

receiving a second electroencephalogram signal, sent by the electroencephalogram recording equipment, of the user in a resting state;

and obtaining a second index value of the characteristic index according to the second electroencephalogram signal, and determining a personalized feedback threshold value of the characteristic index corresponding to the user according to the second index value.

4. A method according to claim 3, wherein said determining from said second index value that said characteristic index corresponds to a personalized feedback threshold for said user comprises:

acquiring descriptive parameters of the second index values, wherein the descriptive parameters at least comprise a mean MeanX and a standard deviation SD of a plurality of the second index values;

and determining the feedback threshold, wherein the feedback threshold comprises an upper limit value XR and a lower limit value XL, wherein XR=MeanX+a×SD, XL=MeanX-a×SD, and a is a weighting coefficient.

5. The method of claim 1, wherein the personalized feedback threshold includes an upper limit XR and a lower limit XL, the normalizing the first index value according to the characteristic index corresponding to the personalized feedback threshold of the user, generating a feedback signal, comprising:

If the first index value xi is smaller than the lower limit value XL, setting the feedback signal to 0;

if the first index value xi is greater than the upper limit value XR, setting the feedback signal to 1;

if the first index value xi is between the lower limit value XL and the upper limit value XR, setting the feedback signal to (xi-XL)/(XR-XL);

wherein xi represents the ith first index value, i is a positive integer.

6. The method of claim 1, wherein the performing the corresponding feedback operation in accordance with the feedback signal comprises:

and adjusting non-procedural parameters of a training program currently operated by the mobile terminal according to the feedback signal.

7. The method of claim 6, wherein the adjusting non-procedural parameters of a training program currently operated by the mobile terminal according to the feedback signal comprises:

determining the stimulus intensity corresponding to the feedback signal;

and controlling the adjustment amplitude of the non-procedural parameter according to the stimulus intensity corresponding to the feedback signal.

8. The method of claim 6, wherein the method further comprises:

receiving an operation instruction input by a user through an operation key or a touch screen or voice;

And controlling a main process of the training program according to the operation instruction, wherein the control difficulty or operability of the main process is associated with the non-procedural parameter.

9. A method according to claim 3, wherein the method further comprises:

transmitting the identification information of the current training program to a server;

and receiving the index recommended by the server and corresponding to the training program, and determining the index recommended by the server as the characteristic index.

10. The method of claim 9, wherein the method further comprises:

and sending the second index value to the server, wherein the second index value is stored by the server into a front-end database to serve as the basis of the recommended index.

11. The method of claim 9, wherein,

the index recommended by the server is: the index value of the user is in an abnormal range compared with the index value of other people, or the index which is recommended most historically.

12. A nerve feedback intervention method applied to an electroencephalograph comprising a single channel gel electrode, the method comprising:

in response to the acquired brain electrical signals of a user, preprocessing the acquired brain electrical signals to remove non-brain electrical interference signals contained in the brain electrical signals;

The preprocessed electroencephalogram signals are sent to a mobile terminal;

the electroencephalogram signals comprise a first electroencephalogram signal of the user in a training state and a second electroencephalogram signal of the user in a resting state, the second electroencephalogram signal is used by the mobile terminal to generate a personalized feedback threshold, and the feedback threshold is used by the mobile terminal to normalize the first electroencephalogram signal to generate a feedback signal.

13. The method of claim 12, wherein the preprocessing the acquired brain electrical signals comprises:

removing high-frequency noise and low-frequency noise in the electroencephalogram signal by using an FIR filter;

removing ocular artifacts in the electroencephalogram signals by using a differential algorithm module; and

and removing head movement artifacts in the electroencephalogram signals by using a motion sensor.

14. The method of claim 13, wherein the electroencephalographic recording apparatus further comprises two reference electrodes, the preprocessing of the acquired electroencephalographic signals comprising:

and acquiring a reference signal acquired by the reference electrode, and preprocessing the electroencephalogram signal based on the reference signal.

15. A neurofeedback intervention device for use in a mobile terminal, the neurofeedback intervention device comprising:

The receiving module is configured to receive a first electroencephalogram signal sent by the electroencephalogram recording equipment and used for a user in a training state;

the training module is configured to obtain a first index value of a characteristic index according to the first electroencephalogram signal; normalizing the first index value according to the personalized feedback threshold value of the characteristic index corresponding to the user to generate a feedback signal; and executing corresponding feedback operation according to the feedback signal.

16. A neurofeedback intervention device for use in an electroencephalograph, the electroencephalograph comprising a single channel gel electrode, the neurofeedback intervention device comprising:

the preprocessing module is configured to respond to the acquired brain electrical signals of a user and preprocess the acquired brain electrical signals so as to remove non-brain electrical interference signals contained in the brain electrical signals;

and the sending module is configured to send the preprocessed electroencephalogram signals to the mobile terminal.

17. A mobile terminal, comprising:

one or more processors;

a storage device having one or more programs stored thereon,

the one or more programs, when executed by the one or more processors, cause the one or more processors to implement the neurofeedback intervention method of any of claims 1-11.

18. An electroencephalogram recording apparatus comprising:

a single channel gel electrode;

one or more processors;

a storage device having one or more programs stored thereon,

the one or more programs, when executed by the one or more processors, cause the one or more processors to implement the neurofeedback intervention method of any of claims 12-14.

19. A computer readable storage medium having stored thereon a computer program, wherein the computer program when executed by one or more processors implements the neurofeedback intervention method of any of claims 1-10 or the neurofeedback intervention method of any of claims 12-14.

20. A neurofeedback intervention system, comprising: a mobile terminal as claimed in claim 17 and an electroencephalographic recording apparatus as claimed in claim 18.