Closed-loop nerve synchronous regulation and detection system

Abstract

The invention provides a closed-loop nerve synchronous regulation and detection system, and belongs to the technical field of neuroscience. The system comprises an electrophysiological regulation subsystem which is used for implementing nerve stimulation regulation on a detected object, has a filtering and amplifying preprocessing function and can receive and send out nerve regulation signals; the signal processing subsystem is used for receiving the preprocessed neural signals, sending out control signals for the electrophysiological regulation and control subsystem and synchronously analyzing and processing the preprocessed neural signals; and the neural multi-electrode is used for detecting an original neural signal. According to the system, a neural signal analysis method based on the power spectrum entropy of wavelet packet decomposition node reconstruction signals is used for calculating and analyzing neural signals collected by multiple neural electrodes, and then closed-loop regulation and control are carried out by comparing the neural signals with a set threshold value. According to the system, the experiment operation in the nerve regulation and control work is simplified, and the experiment efficiency is improved.

Description

Technical Field

[0001] The present invention belongs to the fields of electronic information, neuroscience and sensor technology, and specifically relates to a closed-loop neural synchronization control and detection system. Background Art

[0002] The brain is the central nervous organ of the human body, responsible for all life activities such as sensation, movement, and language expression. The organic integration of the nervous system constitutes the physiological basis of cognitive function, reflects the brain's ability to comprehensively organize external environmental information and process and apply it, and determines the work efficiency of humans when performing high-intensity tasks for a long time. Damage and loss of cognitive function will eventually affect the patient's interpersonal communication, work, and learning abilities, reduce the patient's quality of life, and increase the burden on the patient's family and society.

[0003] Neural information is carried by bioelectric signals and transmitted through the connection pathways between tens of billions of neurons. The tightness of the neural network determines the strength of cognitive function. Taking Parkinson's and epilepsy as examples, the pathogenic mechanism of these patients is lesions in the substantia nigra of the brain, which reduces the number of dopaminergic neurons projected to the striatum, leading to excessive excitement of neurons in the subthalamic nucleus, which in turn causes dysfunction of the basal ganglia, resulting in motor symptoms such as muscle rigidity, resting tremor, and bradykinesia. The decline in cognitive function will also affect workers in positions that need to maintain attention for a long time to a certain extent, leading to a decline in vigilance and the occurrence of occupational accidents. Therefore, it is of great scientific significance to make good use of neural regulation technology to achieve multi-modal stimulation and regulation of the nervous system from a single neuron to a specific brain area.

[0004] Neuromodulation technology is an effective means of enhancing cognitive function. It uses physical or chemical means such as light, magnetism, electricity, and ultrasound to directly or indirectly act on specific nuclei and brain regions, stimulate or inhibit neural activity in the target area, and thus affect the entire brain network. In recent years, scientists have discovered that weak direct current stimulation can be used to regulate the discharge threshold of neurons, thereby affecting the connection pathways of neurons, affecting the transmission and targeted regulation of neural information. It is a non-invasive, gentle, and effective means of action.

[0005] At present, most of the neural information regulation and detection systems at home and abroad are separate combinations, that is, a single-mode stimulation and regulation instrument is combined with an electrophysiological signal or electrochemical signal detection instrument, or an electrophysiological signal detection instrument and an electrochemical signal detection instrument are jointly detected. It is difficult to achieve closed-loop feedback regulation of neural information, resulting in data missing and misalignment, affecting the correlation research between regulation signals and detection signals, and between electrophysiological signals and electrochemical signals. Summary of the invention

[0006] In order to solve the above technical problems, the present invention provides a closed-loop neural synchronization regulation and detection system, which can effectively meet the needs of neural closed-loop feedback regulation, simplify experimental operations, and improve the utilization efficiency of neural information.

[0007] To achieve the above objectives, the present invention provides a closed-loop neural synchronization control and detection system, which is worn on the head. The technical solution adopted is as follows:

[0008] A closed-loop neural synchronization control and detection system, comprising:

[0009] The electrophysiological control subsystem is used to implement neural stimulation control on the detected object, pre-process the original neural signals detected by the neural multi-electrodes, receive the control signal of the signal processing subsystem and act on the nervous system of the detected object to perform stable electrophysiological control;

[0010] The signal processing subsystem is used to receive the original neural signals preprocessed by the electrophysiological control subsystem and perform synchronous analysis and processing, issue control signals to the electrophysiological control subsystem, and fine-tune the issued control signals through a closed-loop feedback algorithm;

[0011] The neural multi-electrode is used to connect the electrophysiological control subsystem with the nervous system of the detected object and detect the original neural signal generated from the detected object.

[0012] Furthermore, the electrophysiological regulation subsystem is used to generate or detect stable current and voltage signals, specifically including:

[0013] An electrophysiological detection module, used for acquiring original neural signals from neural multi-electrodes in real time, filtering, amplifying and preprocessing the acquired original neural signals, and transmitting them to the data processing module of the signal processing subsystem via SPI communication;

[0014] An electrophysiological stimulation module, used to receive instructions from the data processing module of the signal processing subsystem, and provide a stable constant current source stimulation output to the neural multi-electrodes, wherein the current intensity of the output constant current source is adjusted by a code;

[0015] The auxiliary module is used to assist the electrophysiological detection module and the electrophysiological stimulation module to protect the human body and the system.

[0016] Furthermore, the electrophysiological detection module includes:

[0017] Low-noise amplifier, used to amplify the original neural signal to an intensity that can be collected and analyzed. The collected reference value is represented by a 16-bit binary value, and the actual voltage value conversion formula is: -19.23μV×(collected value-32768);

[0018] The digital-to-analog conversion unit is used to convert the actual voltage value of the amplified original neural signal into a digital signal.

[0019] Furthermore, the electrophysiological stimulation module includes:

[0020] The constant current source output unit can output a constant current stimulus source ranging from 10nA to 2.55mA, and the output waveform includes sine wave, square wave, sawtooth wave, triangle wave, time delay and their combination;

[0021] The 4×1 electrode system is used to convert the cross-current stimulation source current output by the constant current source output unit into a 4×1 high-precision stimulation mode. Under the 4×1 high-precision stimulation mode, the single electrode in the middle is the anode output, and the remaining four electrodes are the cathode outputs, and each electrode is independently controlled.

[0022] Further, the auxiliary module includes: a compliance detector for detecting the voltage state of the marked electrode and performing current safety protection on the detected object;

[0023] The electrode impedance measurement unit is used to detect the impedance state of the skin of the detected object in real time to match the output of the constant current source;

[0024] The charge recovery switch is used to recover the residual charge generated by the electrophysiological stimulation module to avoid redox reactions at the electrode-tissue interface and prevent damage to the human body and system;

[0025] Current-limited charge recovery circuit, backup charge recovery circuit for electrophysiological stimulation module.

[0026] Furthermore, the signal processing subsystem includes:

[0027] The data processing module is used to analyze and process the original neural signals after each preprocessing, send the analysis and processing result data to the data display and storage module and the sending module, and receive and execute the instructions from the control subsystem;

[0028] The data display and storage module is used to receive the original neural signal analysis and processing results sent by the data processing module and the configuration parameters of the electrophysiological stimulation module, and perform synchronous display and storage;

[0029] The control subsystem is used to convert user input instructions into system language and standardize the operation logic of each module within the system;

[0030] The sending module is used to transmit the original neural signals collected by the system to the host computer, including serial port transmission and wireless transmission modes.

[0031] Furthermore, the data processing module includes: for each group of brain electrophysiological neural signals, wavelet packet decomposition is performed, single-branch reconstruction is performed using wavelet packet coefficients, α waves, β waves and θ waves are extracted from wavelet decomposition signals of different scales, and the power spectrum of the reconstructed signal of each node is obtained. It is necessary to calculate the power spectrum entropy value of each node, determine the energy distribution law of the original neural signal in different frequency ranges, and quantify the complexity of the power spectrum of each rhythm of the neural signal when the brain of the detected object is active.

[0032] Furthermore, the control subsystem includes:

[0033] A key input module is used to control the system through mechanical key commands, including modifying the waveform type, amplitude, frequency, and stimulation time of electrophysiological stimulation;

[0034] The timing specification module is used to set the timing of the signal processing subsystem and the sending module: the electrophysiological control subsystem and the signal processing subsystem are transmitted via SPI. The instruction cycle for transmitting 32-bit data is 4000ns. A neural control cycle consists of N instruction cycles. The sampling frequency calculation formula is: F = 1 / (N 4000 10 -9 ), N represents the number of instruction cycles;

[0035] Furthermore, the sending module includes:

[0036] SPI communication unit, used for communication between the signal processing subsystem and the electrophysiological regulation subsystem, with a cycle of 4000ns, 32-bit data read and write in each cycle, and LVDS level transmission is used during this period to prevent interference from external electromagnetic signals;

[0037] The UART communication unit is used for communication between the signal processing subsystem and the host computer, and transmits neural signals to the host computer.

[0038] Furthermore, the electrophysiological detection module, the auxiliary module, and the electrophysiological stimulation module are integrated into one chip;

[0039] The data processing module, data display and storage module, control subsystem, and transmission module are integrated into one core board;

[0040] The electrophysiological control subsystem, signal processing subsystem, and neural multi-electrode are integrated into a set of wearable equipment, and the volume of the wearable equipment is less than 7mm 5mm 5mm and weighs less than 750g.

[0041] The beneficial effects of the present invention are:

[0042] (1) It realizes neural regulation and synchronous detection under the same instrument system, increases integration, and reduces signal problems caused by data transmission errors due to unstable interfaces of multiple devices;

[0043] (2) Through closed-loop feedback, the information misalignment and data loss problems between the control signal and the detection signal are solved, ensuring the synchronization of data, which is of great significance to neuroscience research;

[0044] (3) It solves the problem of information loss caused by single-mode recording and can realize multi-channel and multi-mode control schemes, and can automatically adjust the intensity, waveform, frequency and other parameters of the stimulation according to the physical condition of the subject. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Figure 1 This is a schematic diagram of the structure of a closed-loop neural synchronization control and detection system of the present invention;

[0046] Figure 2 This is a general schematic diagram of the hardware module interface connection of a closed-loop neural synchronization control and detection system of the present invention;

[0047] Figure 3 This is a signal processing flow chart of a closed-loop neural synchronization control and detection system of the present invention;

[0048] Figure 4 This is a method flow chart of a closed-loop neural synchronization control and detection system of the present invention. DETAILED DESCRIPTION

[0049] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in combination with specific implementation cases and with reference to the accompanying drawings.

[0050] It should be noted that in the drawings or descriptions, similar or identical parts are all marked with the same figure numbers. The implementation methods not shown or described in the drawings are known to those of ordinary skill in the art. In addition, although examples of parameters including specific values ​​may be provided herein, it should be understood that the parameters do not need to be exactly equal to the corresponding values, but can be approximated to the corresponding values ​​within an acceptable error tolerance or design constraint.

[0051] The present invention provides a closed-loop neural synchronization control and detection system integrated in a 7mm 5mm The head-mounted device, which is 5mm in diameter and weighs less than 750g, has 16 independently controlled neural multi-electrodes for brain electrophysiological neural signal research, which can achieve closed-loop control of neural information detection and stimulation.

[0052] like Figure 1As shown, the closed-loop neural synchronization control and detection system of this example includes: an electrophysiological control subsystem, a signal processing subsystem, and neural multi-electrodes.

[0053] The neural multi-electrode lead-out system has a maximum of 16 electrode channels. In an exemplary embodiment of the present invention, the system uses 16 electrodes for raw neural signal detection and 5 electrodes for high-precision constant current stimulation by default. Specifically, the middle electrode serves as the anode, and the remaining four electrodes are evenly distributed around as cathodes.

[0054] In an exemplary embodiment of the present invention, the electrophysiological detection module acquires neural information from the outside in real time, performs digital-to-analog conversion and amplifier, and amplifies the weak cortical voltage signal to an intensity that can be collected and analyzed. The collected reference value is represented by a 16-bit binary value, and the conversion formula for the amplified neural signal voltage value is: 0.195μV×(collection value-32768).

[0055] The electrophysiological regulation subsystem is integrated into a chip, which can realize the reception and release of neural electrophysiological signals at the front end of the system. It has pre-processing functions of filtering, amplification, and digital-to-analog conversion, impedance measurement, and stimulation functions of stable constant current output, and is designed with current overload protection function.

[0056] In an exemplary embodiment of the present invention, the system acquires the raw neural signals of 16 electrodes one by one at a scanning frequency of 1000 Hz, and controls 1.5 mA to flow between each electrode pair in a complete cycle. 1 / 4 of the constant current. At a frequency of 1000Hz, the input impedance of the amplifier is about 1.7MΩ, the impedance between the electrode pairs is calculated as 10kΩ, and the measured voltage is about 3.7V, which is within the safe and acceptable range for the human body, and the weak current makes the user basically have no obvious feeling.

[0057] The signal processing subsystem is integrated into a core board, which realizes the analysis, processing and feature value extraction of brain electrophysiological neural signals on the wearable system. The parameters are set by the host computer or buttons to establish a communication connection with the electrophysiological control subsystem and the host computer.

[0058] like Figure 2 The figure shows a schematic diagram of the hardware module interface connection of a closed-loop neural synchronization control and detection system of the present invention. After the neural signal is processed by the electrophysiological control subsystem, it is transmitted to the host computer for display and storage through the signal processing subsystem, and the control signal is sent by the host computer or a button and transmitted back to the electrophysiological control subsystem. The signal processing subsystem of this example requires a 5V power supply, and the electrophysiological control subsystem requires a 3.3V power supply and a ±6V power supply. For multiple power supply requirements, a rechargeable 18650 lithium battery with a 3.7V output and a corresponding power conversion module are selected.

[0059] Figure 3 This is a signal processing flow chart of a closed-loop neural synchronization control and detection system of the present invention. The collected original EEG neural signals are subjected to low-pass filtering and denoising by this system, and wavelet packet decomposition is performed. Single-branch reconstruction is performed using wavelet packet coefficients, and the power spectrum entropy is obtained by FFT transformation. After the power spectrum of the reconstructed signal of each node is obtained, the power spectrum entropy value of each node is calculated in combination with the definition of sample entropy, so as to more accurately examine the energy distribution law of brain electrophysiological neural signals in different frequency ranges. The complexity of the power spectrum of each EEG rhythm during brain activity is quantified, and the specificity of the local frequency band indicators of brain electrophysiological neural signals of different populations is found.

[0060] In an exemplary embodiment of the present invention, within a complete command cycle, the electrodes collected 16 brain electrophysiological neural signals and performed 5 electrode current output adjustments. For each group of collected neural signals f(t), a notch filter is used to eliminate the 50Hz frequency, and then wavelet packet decomposition is performed after removing the abnormal channels without signals, and the wavelet packet coefficients are extracted, and the wavelet packet reconstruction is performed using each coefficient. Specifically:

[0061] Each group of collected neural signals f(t) is decomposed by wavelet packets. After f(t) is decomposed by wavelet packets, 2 can be obtained at the ath decomposition layer. a Sub-bands:

[0062] ,

[0063] In the formula, a is the number of decomposition layers, b is the number of nodes in the ath layer, t represents the node of the wavelet packet decomposition algorithm, and f a,b (t a,b ) is the reconstructed signal of the neural signal f(t) at the (a,b) node after wavelet packet decomposition;

[0064] Reconstruct the signal x(n) for each subspace of length L, that is, f a,b (t a,b ), and then perform discrete Fourier transform and power spectrum calculation:

[0065] ,

[0066] Where, X L (k) is the DFT transform of the subspace reconstructed signal x(n); j is the imaginary unit; k is the ordinal number of the reconstructed signal; L is the length of the subspace reconstructed signal x(n); P(k) is X L (k) The power spectrum sequence;

[0067] Suppose the power spectrum sequence P(k) of the subspace reconstruction signal x(n) includes N points: p(1), p(2),…, p(N), which are connected in order of sequence to form an m-dimensional vector:

[0068] ,

[0069] Where N is the total number of points in the power spectrum sequence, and P(i) represents the values ​​of m+1 consecutive p starting from the i-th point;

[0070] According to the formula, 1≤i1, i2≤N-m+1, the distance d[P(i1), P(i2)] between P(i1) and P(i2) is the one with the largest difference between the corresponding elements of the two:

[0071] ,

[0072] Where kk is a parameter in the range of 0~(m-1);

[0073] For each i1 value, calculate the distance d[P(i1), P(i2)] between P(i1) and the remaining vector P(i2);

[0074] Given a threshold r, which is related to the standard deviation SD of the subspace reconstructed signal x(n), statistics are taken for each value of i1≤N-m+1, and the number of values ​​whose distance d[P(i1), P(i2)] is less than the threshold r is counted. The ratio of this number to the total number of distances Nm is recorded as :

[0075] ,

[0076] In the formula, SD is the standard deviation of the subspace reconstructed signal x(n),

[0077] For all Take the average value and record it as :

[0078] ,

[0079] Add 1 to the dimension of the m-dimensional vector to make it an m+1-dimensional vector. Repeat the above calculation process to get and ;

[0080] Calculate the entropy of the power spectrum sequence for:

[0081] .

[0082] The entropy value obtained It can be used to examine the distribution law of power values ​​in each frequency band of neural signals.

[0083] Figure 4 The flowchart is a method for closed-loop neural regulation waveform output according to an embodiment of the present disclosure. The system is initially set by the user through the host computer or buttons to set the stimulation waveform, including sine wave, square wave, sawtooth wave, triangle wave, time delay and combination; then the stimulation parameters are adjusted, such as stimulation amplitude, frequency and stimulation time. At this time, you can still return to reset the stimulation waveform. After the setting is completed, the system starts the stimulation / detection function. If the power spectrum entropy characteristic value of each frequency band of the detected neural signal exceeds the set threshold, the system returns to the stimulation parameter adjustment part. During the analysis process, if the system receives a stop command, it enters the data export mode, and then the system ends.

[0084] Specifically, the system time-multiplexes the neural multi-electrode channels and gradually completes the tasks of collection, analysis, and stimulation within a complete command cycle. If the result of the analysis is higher than the set threshold, the current is slightly reduced, otherwise the current is slightly increased.

[0085] In an exemplary embodiment of the present invention, the neural signals after analysis and processing are synchronously transferred to the data display and storage module, which not only allows the operator to observe and evaluate the user's mental state in real time on the display screen, but also allows the data to be stored, facilitating the transmission of information to the host computer for storage.

[0086] In an exemplary embodiment of the present invention, the electrophysiological regulation subsystem and the signal processing subsystem are transmitted via the SPI protocol LVDS level. A single complete instruction consists of a 32-bit binary number with a cycle of 4000ns. The electrophysiological regulation subsystem itself performs four functions: acquisition, stimulation, reading, and clearing. Different functional effects can be produced according to different instruction string formats, and instruction feedback is given after two cycles. A complete neural regulation cycle consists of N separate instructions, and the frequency calculation formula for acquisition is: F = 1 / (N 4000 10 -9 ), by default, the value of N is defined as 250, that is, the sampling frequency is 1000Hz; the signal processing subsystem communicates with the host computer via the UART serial port, with a baud rate of 200000, and 16 channels are collected cyclically and sent to the host computer.

[0087] The closed-loop neural synchronization control and detection system of the disclosed embodiment is finally integrated into a 7mm 5mm The overall mass is controlled within 750g within a 5mm space, and can complete the independent control of 16 stimulation / collection neural information channels. When in use, the customized brain electrode cap should be covered on the head, and the neural regulation and synchronization detection system device should be installed in the occipital position of the brain. The output channel contacts the scalp through the brain electrode wire and the patch electrode to reduce impedance.

[0088] The above description of the disclosed embodiments enables one skilled in the art to implement or use the present invention. Various modifications to these embodiments will be apparent to one skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but rather to the widest scope consistent with the principles and novel features disclosed herein.

[0089] It should be noted that the implementation methods not shown or described in the drawings or the text of the specification are all forms known to ordinary technicians in the relevant technical field and are not described in detail. In addition, the above definitions of various elements and methods are not limited to the various specific structures, shapes or methods mentioned in the embodiments, and ordinary technicians in the field can change or replace them.

[0090] The specific embodiments described above further illustrate the objectives, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A closed-loop neural synchronization control and detection system, characterized in that: The system comprises: The electrophysiological control subsystem is used to implement neural stimulation control on the detected object, pre-process the original neural signals detected by the neural multi-electrodes, receive the control signal of the signal processing subsystem and act on the nervous system of the detected object to perform stable electrophysiological control; The signal processing subsystem is used to receive the original neural signals preprocessed by the electrophysiological control subsystem and perform synchronous analysis and processing, issue control signals to the electrophysiological control subsystem, and fine-tune the issued control signals through a closed-loop feedback algorithm; The neural multi-electrode is used to connect the electrophysiological control subsystem with the nervous system of the subject to be detected, and detect the original neural signals generated from the subject to be detected.

2. A closed-loop neural synchronization control and detection system according to claim 1, characterized in that: The electrophysiological regulation subsystem comprises: An electrophysiological detection module, used for acquiring original neural signals from neural multi-electrodes in real time, filtering, amplifying and preprocessing the acquired original neural signals, and transmitting them to the data processing module of the signal processing subsystem via SPI communication; An electrophysiological stimulation module, used to receive instructions from the data processing module of the signal processing subsystem, and provide a stable constant current source stimulation output to the neural multi-electrodes, wherein the current intensity of the output constant current source is adjusted by a code; Auxiliary module, used to assist the electrophysiological detection module and the electrophysiological stimulation module.

3. A closed-loop neural synchronization control and detection system according to claim 2, characterized in that: The electrophysiological detection module comprises: A low-noise amplifier to amplify raw neural signals; The digital-to-analog conversion unit is used to convert the actual voltage value of the amplified original neural signal into a digital signal.

4. A closed-loop neural synchronization control and detection system according to claim 2, characterized in that: The electrophysiological stimulation module comprises: A constant current source output unit, used for outputting a constant current stimulus source in a range of 10nA to 2.55mA; The 4×1 electrode system is used to convert the constant current stimulation source output by the constant current source output unit into a 4×1 high-precision stimulation mode.

5. A closed-loop neural synchronization control and detection system according to claim 2, characterized in that: The auxiliary module comprises: Compliance detector, used to detect the voltage state of the marked electrode and provide current safety protection for the object being detected; The electrode impedance measurement unit is used to detect the impedance state of the skin of the detected object in real time to match the output of the constant current source; A charge recovery switch for recovering residual charge generated by the electrophysiological stimulation module; Current-limited charge recovery circuit for backup charge recovery of the electrophysiological stimulation module.

6. A closed-loop neural synchronization control and detection system according to claim 1, characterized in that: The signal processing subsystem comprises: The data processing module is used to analyze and process the original neural signals after each preprocessing, send the analysis and processing result data to the data display and storage module and the sending module, and receive and execute the instructions from the control subsystem; The data display and storage module is used to receive the original neural signal analysis and processing results sent by the data processing module and the configuration parameters of the electrophysiological stimulation module, and perform synchronous display and storage; A control subsystem, used to convert user input instructions into system language; The sending module is used to transmit the original neural signal analysis and processing results to the host computer.

7. A closed-loop neural synchronization control and detection system according to claim 6, characterized in that: The data processing module is used to analyze and process the original neural signal after each preprocessing, including: The original neural signal is decomposed by wavelet packets, and a single branch reconstruction is performed using the wavelet packet coefficients. After obtaining the power spectrum of the reconstructed signal of each node, the power spectrum entropy value of each node is calculated based on the definition of sample entropy to determine the energy distribution law of the original neural signal in different frequency ranges.

8. A closed-loop neural synchronization control and detection system according to claim 6, characterized in that: The control subsystem comprises: A key input module is used to control the system through mechanical key commands, including modifying the waveform type, amplitude, frequency, and stimulation time of electrophysiological stimulation; The timing specification module is used to set the timing of the signal processing subsystem and the sending module.

9. A closed-loop neural synchronization control and detection system according to claim 6, characterized in that: The sending module comprises: SPI communication unit, used for communication between the signal processing subsystem and the electrophysiological regulation subsystem; UART communication unit, used for communication between the signal processing subsystem and the host computer.

10. A closed-loop neural synchronization control and detection system according to any one of claims 1 to 9, characterized in that: The electrophysiological detection module, auxiliary module, and electrophysiological stimulation module are integrated into one chip; The data processing module, data display and storage module, control subsystem, and transmission module are integrated into one processing board; The electrophysiological control subsystem, signal processing subsystem, and neural multi-electrodes are integrated into a set of wearable equipment.

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