Transcranial electrical stimulation control method and system for enhancing working memory

Abstract

The invention discloses a transcranial electrical stimulation control method and system for enhancing working memory, and the method comprises the steps: collecting and preprocessing a multi-channel endogenous electroencephalogram signal of a target object during the execution of a cognitive task, and obtaining a to-be-analyzed electroencephalogram signal; aligning the to-be-analyzed electroencephalogram signal with a behavior expression label generated by the target object during execution of the cognitive task to obtain an associated signal; according to the correlation signal, obtaining a channel pair with the highest phase synchronism between the forehead lobe and the apical lobe of the target object as a target channel pair; based on the associated signal data of the target channel pair, extracting frequency band features to generate stimulation parameters of radial and cranial electrical stimulation; and outputting transcranial electrical stimulation according to the stimulation parameters, and collecting endogenous electroencephalogram signals during stimulation in real time, so that the stimulation parameters are dynamically adjusted, and closed-loop regulation and control on the brain area of the target object are realized. Therefore, by the adoption of the method and device, accurate control over the endogenous phase difference between the prefrontal lobe and the apical lobe can be achieved, and therefore the work memory performance is enhanced.

Description

Technical Field

[0001] The present application relates to the field of cognitive neuroscience technology, and in particular to a transcranial electrical stimulation control method and system for enhancing working memory. Background Art

[0002] In the field of cognitive neuroscience, the assessment of working memory is a key research topic. Working memory, as the ability to store and process information in the short term, is essential for the performance of complex cognitive tasks. For example, in the N-back task, participants need to memorize and update a series of presented stimulus items in real time, and judge whether the stimulus is the same as the previous Nth stimulus when a new stimulus appears. This task is of great value for assessing and training working memory ability, especially in the fields of education, military training, and clinical rehabilitation.

[0003] In the prior art, existing working memory enhancement methods mainly rely on exogenous stimulation methods such as transcranial electrical stimulation (tES). These methods improve cognitive performance by applying specific current stimulation. However, traditional stimulation methods are usually based on fixed stimulation parameters and fail to consider the changes in endogenous EEG activity of individuals during task execution in real time, which leads to limited stimulation effects. Summary of the invention

[0004] The embodiments of the present application provide a transcranial electrical stimulation control method and system for enhancing working memory. In order to have a basic understanding of some aspects of the disclosed embodiments, a simple summary is given below. This summary is not a general review, nor is it intended to identify key / important components or describe the scope of protection of these embodiments. Its only purpose is to present some concepts in a simple form as a preface to the detailed description that follows.

[0005] In a first aspect, an embodiment of the present application provides a transcranial electrical stimulation control method for enhancing working memory, which is applied to a transcranial electrical stimulation device, and the method comprises: In the process of performing a cognitive task on a target subject wearing a multi-channel EEG acquisition device, the multi-channel endogenous EEG signals of the target subject during the cognitive task are collected and pre-processed to obtain the EEG signals to be analyzed; Aligning the EEG signal to be analyzed with the behavioral performance label generated by the target subject during the cognitive task to obtain the correlation signal; According to the correlation signal, the channel pair with the highest phase synchronization between the prefrontal lobe and the parietal lobe of the target object is obtained as the target channel pair; Based on the correlation signal data of the target channel pair, frequency band features are extracted to generate stimulation parameters for radial cranial electrical stimulation; Transcranial electrical stimulation is output according to the stimulation parameters, and endogenous EEG signals are collected in real time during the stimulation period, so as to dynamically adjust the stimulation parameters to achieve closed-loop regulation of the brain area of ​​the target object.

[0006] Optionally, collecting and preprocessing multi-channel endogenous EEG signals of the target subject during the cognitive task, including: Collect multi-channel endogenous EEG signals of the target subject during the cognitive task; A Butterworth notch filter is used to remove the power frequency interference of endogenous EEG signals; Perform baseline drift correction on the endogenous EEG signal after removing the power frequency interference; The endogenous EEG signals after baseline drift correction were bandpass filtered using a Butterworth bandpass filter; Independent component analysis was used to identify and remove artifacts from endogenous EEG signals after bandpass filtering.

[0007] Optionally, the EEG signal to be analyzed is aligned with the behavioral performance label generated by the target subject during the cognitive task to obtain a correlation signal, including: Dividing the EEG signal to be analyzed according to a preset period to obtain EEG data of multiple periods; Generate a behavior performance label of the target object in each cycle through the behavior performance of the target object during the cognitive task; Associating the EEG data of each cycle with the behavior performance label of the target object of each cycle to obtain a first mapping relationship between multiple EEG data and the behavior performance labels; Determine whether the behavior performance labels in each first mapping relationship meet a preset condition, and delete the first mapping relationship to which the behavior performance labels that do not meet the preset condition belong, to obtain a plurality of second mapping relationships; Performing baseline correction on the EEG data in each second mapping relationship to remove the second mapping relationship to which the EEG data belonging to chronic drift belongs, to obtain a plurality of third mapping relationships; The EEG data corresponding to the same behavior performance labels in multiple third mapping relationships are superimposed and averaged to obtain an EEG signal that meets preset conditions.

[0008] Optionally, according to the correlation signal, a channel pair with the highest phase synchronization between the prefrontal lobe and the parietal lobe of the target object is obtained as a target channel pair, including: Identify prefrontal electrode pathways that reflect decision-making and emotion regulation, and parietal electrode pathways that reflect attention regulation and sensory information integration; Selecting the to-be-processed associated signals corresponding to the frontal lobe electrode channel and the parietal lobe electrode channel from the associated signals; From the signals to be processed, signals in the target frequency bands closely related to working memory are determined, the target frequency bands including the theta band of 4-8 Hz and the alpha band of 8-13 Hz; Extracting phase information within the signal in the target frequency band through Hilbert transform; According to the phase information, the channel pair with the highest phase synchronization between the prefrontal lobe and parietal lobe of the target subject is determined as the target channel pair.

[0009] Optionally, according to the phase information, determining a channel pair with the highest phase synchronization between the frontal lobe and the parietal lobe of the target object as a target channel pair, including: Based on the extracted phase information, the phase locking value between each prefrontal channel and parietal channel pair is calculated; Based on the phase locking value, a phase synchronization matrix is ​​constructed; In the phase synchronicity matrix, select the channel pair with the highest phase locking value; The channel pair with the highest phase locking value was taken as the channel pair with the highest phase synchronization between the prefrontal and parietal lobes of the target subject; The channel pair with the highest phase synchronization is selected as the target channel pair.

[0010] Optionally, the phase lock value is calculated as:

[0011] in, is the phase lock value, is the number of signal samples in the target frequency band, Indicated in The phase difference between the two signals at the sample points is the instantaneous phase of the prefrontal channel. and the instantaneous phase of the parietal channels In the The difference between the sample points, is a complex exponent, is the imaginary unit, and the complex exponent represents the The phase difference between the two signals at the sample points is expressed as a vector on the complex plane. Indicated in The accumulation of all complex exponentials within sample points, For the A point in time, For the A point in time, represents the magnitude of a complex number, A mathematical constant representing the exponential of a complex number.

[0012] Optionally, based on the associated signal data of the target channel pair, frequency band features are extracted to generate stimulation parameters for cranial electrical stimulation, including: The EEG signals of the target channel pair are separated by frequency bands, and the signals of the theta band and the alpha band are extracted respectively by bandpass filter or wavelet transform; Calculate the phase locking value of the target channel pair in the theta frequency and alpha frequency band; Compare the phase locking values ​​of theta band and alpha band, and take the band corresponding to the larger phase locking value as the target band; For the target frequency band, the time domain signal is converted into a frequency domain signal by fast Fourier transform to obtain the amplitude and phase information within the target frequency band; Stimulation parameters for transcranial electrical stimulation are generated according to the target frequency band, the amplitude and the phase information within the target frequency band.

[0013] Optionally, stimulation parameters for transcranial electrical stimulation are generated according to the target frequency band, the amplitude and phase information within the target frequency band, including: The frequency of the target frequency band is used as the stimulation frequency; According to the amplitude in the target frequency band, the average amplitude of the target frequency band is calculated; The average amplitude of the target frequency band is taken as the intensity of the stimulation current; According to the phase information of the target frequency band, the phase difference of the target channel pair is calculated; setting a phase parameter of the stimulation signal based on the phase difference of the target channel pair; The stimulation frequency, the intensity of the stimulation current and the phase parameters are used as the stimulation parameters of transcranial electrical stimulation.

[0014] Optionally, the method further includes: During the preset stimulation duration, when the phase lock value in the target frequency band reaches the set threshold, the transcranial electrical stimulation of the target object is suspended; or, Within the preset stimulation duration, when the phase locking value in the target frequency band does not reach the set threshold, transcranial electrical stimulation of the target object is resumed.

[0015] In a second aspect, an embodiment of the present application provides a transcranial electrical stimulation control system for enhancing working memory, the system comprising: An intelligent terminal, a transcranial electrical stimulation device, and a multi-channel EEG acquisition device; the intelligent terminal is communicatively connected to the transcranial electrical stimulation device, and the multi-channel EEG acquisition device is electrically connected to the transcranial electrical stimulation device; wherein, An intelligent terminal is used to generate electrical stimulation instructions according to cognitive tasks and send them to the transcranial electrical stimulation device; A transcranial electrical stimulation device is used to perform transcranial electrical stimulation on a target object wearing a multi-channel EEG acquisition device according to received electrical stimulation instructions; in the process of performing transcranial cognitive tasks on the target object wearing the multi-channel EEG acquisition device, the multi-channel endogenous EEG signals of the target object during the cognitive task are collected and preprocessed to obtain the EEG signals to be analyzed; the EEG signals to be analyzed are aligned with the behavioral performance labels generated by the target object during the cognitive task to obtain related signals; transcranial electrical stimulation is output according to stimulation parameters, and endogenous EEG signals during stimulation are collected in real time, so as to dynamically adjust the stimulation parameters, realize closed-loop regulation of the brain area of ​​the target object, and generate transcranial electrical stimulation information to be fed back to the intelligent terminal.

[0016] The technical solution provided by the embodiments of the present application may have the following beneficial effects: In an embodiment of the present application, stimulation parameters are used to perform transcranial electrical stimulation on the target object, and at the same time, the steps of real-time reception and preprocessing of endogenous EEG signals are continued to be performed, so that closed-loop control is achieved. The closed-loop control mechanism allows the system to continuously optimize the stimulation parameters based on the latest EEG signal feedback to adapt to the changes in the brain state of the target object during the execution of cognitive tasks. Through continuous real-time monitoring and optimization of stimulation parameters, the system can dynamically generate stimulation parameters suitable for different individuals, achieve precise control of endogenous phase difference, and thus enhance working memory performance.

[0017] It should be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0019] Figure 1 is a flow chart of a transcranial electrical stimulation control method for enhancing working memory provided in an embodiment of the present application; Figure 2 It is a structural schematic diagram of a transcranial electrical stimulation control system for enhancing working memory provided by the present application; Figure 3 It is a structural schematic diagram of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0020] The following description and the accompanying drawings sufficiently illustrate specific embodiments of the present application to enable those skilled in the art to practice them.

[0021] It should be clear that the described embodiments are only part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in the field without creative work are within the scope of protection of the present application.

[0022] When the following description refers to the drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present application. Instead, they are only examples of systems and methods consistent with some aspects of the present application as detailed in the attached claims.

[0023] In the description of the present application, it should be understood that the terms "first", "second", etc. are used for descriptive purposes only and should not be understood as indicating or implying relative importance. For those of ordinary skill in the art, the specific meanings of the above terms in the present application can be understood according to the specific circumstances. In addition, in the description of the present application, unless otherwise specified, "multiple" refers to two or more. "And / or" describes the association relationship of associated objects, indicating that three relationships may exist. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. The character " / " generally indicates that the objects associated before and after are in an "or" relationship.

[0024] The present application provides a transcranial electrical stimulation control method and system for enhancing working memory to solve the problems existing in the above-mentioned related technical problems. In the embodiment of the present application, stimulation parameters are used to perform transcranial electrical stimulation on the target object, while continuing to perform the steps of real-time receiving and preprocessing endogenous EEG signals, so as to achieve closed-loop control. The closed-loop control mechanism allows the system to continuously optimize the stimulation parameters according to the latest EEG signal feedback to adapt to the changes in the brain state of the target object during the execution of cognitive tasks. Through continuous real-time monitoring and optimization of stimulation parameters, the system can dynamically generate stimulation parameters suitable for different individuals, achieve precise control of endogenous phase difference, and thus enhance working memory performance. The following is a detailed description using an exemplary embodiment.

[0025] The following will be combined with the attached Figure 1 , a transcranial electrical stimulation control method for enhancing working memory provided by an embodiment of the present application is introduced in detail. The method can be implemented by a computer program and can be run on a transcranial electrical stimulation control system for enhancing working memory based on a von Neumann system. The computer program can be integrated into an application or run as an independent tool application.

[0026] See also Figure 1, which is a flow chart of a transcranial electrical stimulation control method for enhancing working memory, and is applied to a transcranial electrical stimulation device. Figure 1 As shown, the method of the embodiment of the present application may include the following steps: S101, during the process of performing a cognitive task on a target subject wearing a multi-channel EEG acquisition device, collecting and preprocessing multi-channel endogenous EEG signals of the target subject during the period of performing the cognitive task to obtain EEG signals to be analyzed; Among them, the multi-channel EEG acquisition device is a head-mounted device that can collect brain electrical activity signals from multiple channels at the same time, which can be used to analyze the activity state of the brain. The target object refers to the individual or subject who receives transcranial electrical stimulation and EEG signal acquisition. Transcranial electrical stimulation is a non-invasive neuromodulation technology that transmits weak currents through the scalp to stimulate the brain and regulate brain function.

[0027] In some embodiments of the present application, the specific process of collecting and preprocessing multi-channel endogenous EEG signals of the target object during the performance of cognitive tasks includes: collecting multi-channel endogenous EEG signals of the target object during the performance of cognitive tasks; using a Butterworth notch filter to remove the power frequency interference of the endogenous EEG signals; performing baseline drift correction on the endogenous EEG signals after the power frequency interference is removed; using a Butterworth bandpass filter to bandpass filter the endogenous EEG signals after the baseline drift correction; and using independent component analysis technology to identify and remove artifacts from the endogenous EEG signals after the bandpass filtering.

[0028] Among them, the Butterworth notch filter is a filter used to remove interference of specific frequencies from the signal, such as power frequency (50 Hz or 60 Hz) interference. The Butterworth bandpass filter is a filter that allows signals in a specific frequency band to pass through while blocking signals in other frequency bands. Independent component analysis technology is a computational method used to separate independent signal sources from multivariate signals. It is often used in EEG signal processing to identify and remove artifacts such as eye movements and electromyography.

[0029] For example, volunteers wear a multi-channel EEG acquisition device equipped with 64 electrodes, which are distributed on the volunteer's scalp according to the international 10-20 system layout to cover the main brain areas of the scalp, especially the prefrontal and parietal areas. During the volunteer's performance of a 5-minute working memory task (such as the N-back task), the EEG acquisition device records EEG signals in real time at a sampling rate of 1000 Hz. A 5th-order Butterworth notch filter is used, especially for filtering 50 Hz or 60 Hz power equipment noise to eliminate the negative impact on the signal. The filter is applied to process the entire 5-minute EEG signal recording. The EEG signal after removing the power frequency interference is corrected for baseline drift by calculating the average value of each channel signal within 30 seconds before the start of the task and subtracting this average value from the entire recording to eliminate long-term signal changes. The EEG signal after baseline drift correction was filtered using a 4th-order Butterworth bandpass filter, and the frequency band from 0.5 Hz to 65 Hz was selected to remove low-frequency noise (such as heartbeat, breathing) and high-frequency noise (such as electromyographic interference), so as to focus on the frequency band related to EEG activity. Independent component analysis (ICA) technology was applied to analyze the EEG signal after bandpass filtering to identify non-brain-derived signals, such as eye movements and electromyographic artifacts. These artifacts were removed from the EEG signal, and the clean EEG signal was retained as the EEG signal to be analyzed, providing an accurate data basis for subsequent phase synchronization analysis and transcranial electrical stimulation.

[0030] S102, aligning the EEG signal to be analyzed with the behavioral performance label generated by the target subject during the cognitive task to obtain a correlation signal; Among them, the EEG signal to be analyzed is obtained by completing preliminary preprocessing of the EEG data obtained by real-time acquisition of a multi-channel EEG acquisition device during the performance of cognitive tasks, such as removing noise and artifacts. Behavioral performance labels refer to the response records of the target subject to a specific task (such as a working memory task) during the performance of cognitive tasks. These labels can be correct or incorrect responses, or behavioral data such as reaction time, which are used to evaluate the performance of the target subject in the task. Alignment processing is the process of matching the time series data of the EEG signal with the behavioral performance label of the target object in time. This process ensures that the changes in the EEG signal correspond to the behavioral performance of the target object at a specific moment. Screening out EEG signals that meet the preset conditions refers to selecting EEG signals that match the behavioral performance labels of the target object according to specific analysis purposes. For example, only those EEG signals recorded when the target object responds correctly are selected to study the brain activity pattern during correct cognitive performance. Associated signals refer to EEG signals that have been screened and matched with the behavioral performance labels of the target object. These signals are considered to be associated with specific cognitive processes or behavioral performances.

[0031] In some embodiments of the present application, the specific process of aligning the EEG signal to be analyzed with the behavioral performance label of the target object during the execution of the cognitive task includes: dividing the EEG signal to be analyzed according to preset periods to obtain EEG data of multiple periods; generating the behavioral performance label of the target object in each period through the behavioral performance of the target object during the execution of the cognitive task; associating the EEG data of each period with the behavioral performance label of the target object in each period to obtain multiple first mapping relationships between EEG data and behavioral performance labels; judging whether the behavioral performance labels in each first mapping relationship meet the preset conditions, and deleting the first mapping relationship to which the behavioral performance labels that do not meet the preset conditions belong, to obtain multiple second mapping relationships; performing baseline correction on the EEG data in each second mapping relationship to remove the second mapping relationship to which the EEG data belonging to chronic drift belongs, to obtain multiple third mapping relationships; superimposing and averaging the EEG data corresponding to the same behavioral performance labels in multiple third mapping relationships to obtain EEG signals that meet the preset conditions.

[0032] Among them, the preset cycle refers to the time interval preset according to the experimental design or task characteristics when performing EEG signal analysis. For example, in the N-back task, the preset cycle may be the time interval between each stimulus presentation. The EEG data of the cycle refers to the EEG signal data collected in each preset cycle, which represents the brain activity in a specific time period. Behavioral performance refers to the response of the target object to the task in each preset cycle, such as correct or incorrect judgment, reaction time, etc. The behavioral performance label refers to the record of the target object's behavioral performance corresponding to the EEG data of each cycle, which is used to identify the behavioral results in the cycle. The first mapping relationship refers to the relationship that associates the EEG data of each cycle with its corresponding behavioral performance label. This mapping relationship helps to analyze the brain activity during a specific behavioral performance. The preset condition refers to the standard used to filter data in the analysis, such as selecting only the cycles with correct responses, or the cycles with reaction times within a certain range. The second mapping relationship refers to the association relationship between the remaining EEG data that meet the conditions and the behavioral performance label after deleting the first mapping relationship that does not meet the preset condition. Baseline correction refers to the processing of EEG data to remove chronic drift (long-term changes), which is usually achieved by subtracting the average value of a baseline period. The third mapping relationship refers to the correlation between the baseline-corrected EEG data and the behavioral performance label. The superposition and averaging process refers to the accumulation and averaging of multiple cycles of EEG data corresponding to the same behavioral performance label to improve the signal-to-noise ratio and stability of the signal.

[0033] In some possible implementations, volunteers are asked to perform a 2-back task in which each stimulus is presented for 2500 milliseconds (ms) with an interval of 500 ms. The volunteer's response to each stimulus, including correct or incorrect judgments, is recorded. The volunteer's EEG signals are recorded in real time using a multi-channel EEG acquisition device at a sampling rate of 1000 Hz. Each 2500ms EEG signal segment is aligned with the corresponding behavioral performance label (correct or incorrect) to ensure that each EEG signal segment is directly associated with a specific task performance. EEG data segments corresponding to incorrect answers or long reaction times are eliminated. Only signals related to good task performance are retained to reduce the interference of invalid data on subsequent analysis. Baseline correction is performed on each retained EEG signal segment, and the baseline value of each signal segment is calculated (e.g., the average value before the task starts). The baseline value is subtracted from the entire signal to eliminate chronic drift and ensure the stability and consistency of the signal in time. All baseline-corrected signal segments are superimposed and averaged to improve the signal-to-noise ratio and reduce the interference of random noise. The EEG activity feature signals with high stability and consistency are extracted, which fully reflect the key brain activity characteristics of users in high-quality task performance.

[0034] S103, according to the correlation signal, obtaining a channel pair with the highest phase synchronization between the prefrontal lobe and the parietal lobe of the target object as a target channel pair; Among them, the prefrontal lobe is the lobe at the front of the brain, involved in high-level cognitive functions such as decision-making, planning, and social behavior. The parietal lobe is the lobe at the top of the brain, involved in processing tactile, visual, and spatial information. Phase synchronization refers to the phase consistency of two or more signals at a specific point in time. In EEG signal analysis, phase synchronization can reflect the functional connectivity and coordinated activities between different brain regions. A channel pair refers to electrode channels at two different locations in an EEG acquisition device, which are used to record signals from different brain regions. The target channel pair refers to those channel pairs between the prefrontal lobe and the parietal lobe that show the highest phase synchronization.

[0035] In some embodiments of the present application, based on the associated signal, the specific process of obtaining the channel pair with the highest phase synchronization between the prefrontal lobe and parietal lobe of the target object as the target channel pair includes: determining the prefrontal electrode channel reflecting decision-making and emotion regulation functions and the parietal electrode channel reflecting attention regulation and sensory information integration; from the associated signal, selecting the to-be-processed associated signal corresponding to the prefrontal electrode channel and the parietal electrode channel; from the to-be-processed signal, determining the signal on the target frequency band closely related to working memory, the target frequency band including the theta frequency band of 4-8Hz and the alpha frequency band of 8-13Hz; extracting the phase information in the signal on the target frequency band through Hilbert transform; based on the phase information, determining the channel pair with the highest phase synchronization between the prefrontal lobe and parietal lobe of the target object as the target channel pair.

[0036] Specifically, the specific process of determining the channel pair with the highest phase synchronization between the prefrontal lobe and parietal lobe of the target object as the target channel pair based on the phase information includes: calculating the phase locking value between each prefrontal channel and parietal channel pair based on the extracted phase information; constructing a phase synchronization matrix based on the phase locking value; screening out the channel pair with the highest phase locking value in the phase synchronization matrix; using the channel pair with the highest phase locking value as the channel pair with the highest phase synchronization between the prefrontal lobe and parietal lobe of the target object; and using the channel pair with the highest phase synchronization as the target channel pair.

[0037] Specifically, the calculation formula of the phase lock value is:

[0038] in, is the phase lock value, is the number of signal samples in the target frequency band, Indicated in The phase difference between the two signals at the sample points is the instantaneous phase of the prefrontal channel. and the instantaneous phase of the parietal channels In the The difference between the sample points, is a complex exponent, is the imaginary unit, and the complex exponent represents the The phase difference between the two signals at the sample points is expressed as a vector on the complex plane. Indicated in The accumulation of all complex exponentials within sample points, For the A point in time, For the A point in time, represents the magnitude of a complex number, A mathematical constant representing the exponential of a complex number.

[0039] In some embodiments of the present application, the prefrontal and parietal electrode channels closely related to working memory are selected based on the international 10-20 electrode positioning system, including the F3, F4, F7, F8, Fp1, Fp2 channels of the prefrontal lobe and the P3, P4, P7, P8, Pz channels of the parietal lobe. This ensures that signals from brain regions with important functions in the execution of working memory tasks are collected, laying the foundation for the accurate extraction of target channel pairs.

[0040] Next, among the associated signals of the selected channels, focus on the frequency bands (4 - 13 Hz) that are closely related to working memory, namely the theta (4 - 8 Hz) and alpha (8 - 13 Hz) bands, and extract the phase information within this frequency band through the Hilbert transform. The Hilbert transform converts the time-domain signal into a complex form, thereby obtaining the instantaneous phase information, which is convenient for subsequent calculation of phase synchrony. For a given channel signal , after its Hilbert transform, the complex form can be expressed as: ; where is the Hilbert transform part of the signal , and is the imaginary unit. The instantaneous phase can then be extracted from the complex form: ; where is the phase angle of the complex number (usually implemented using the arctangent function). This process extracts the instantaneous phase information of each channel signal within a specific frequency band, providing a data basis for subsequent calculation of phase synchrony.

[0041] Then, based on the extracted phase information, calculate the Phase-Locking Value (PLV) between each prefrontal channel and parietal channel pair to quantify the level of phase synchrony between the two channels in the given frequency band. The value range of PLV is between 0 and 1. The higher the value, the stronger the phase synchrony between the two channels. Its specific meaning is as follows: When PLV = 1: It means that the phases of the two signals are completely consistent throughout the entire time period, that is, their phase difference is constant, indicating a high degree of synchrony. This is usually considered a sign of very strong functional connectivity, which may imply the existence of coordinated activities between the two brain regions, especially during the execution of cognitive tasks or specific neural processes.

[0042] When PLV = 0: It means that the phases of the two signals are completely inconsistent throughout the entire time period, and the phase difference varies randomly. A low PLV value usually indicates a lack of synchronous activity between the two channels, or a weak connection between them in this frequency band and time period.

[0043] When 0 < PLV < 1: It means that there is a certain degree of phase synchrony between the two channels, but they are not completely consistent. The higher the PLV value, the stronger the synchrony; conversely, the weaker the synchrony. This situation is relatively common in neural signal processing, indicating that there is a certain degree of functional connectivity between the channels, which may show a certain degree of coordination during specific task stages or brain activity states.

[0044] Furthermore, based on the PLV values ​​of each pair of prefrontal channels and parietal channels, a phase synchronization matrix was constructed, in which each element of the matrix represented the PLV value between a specific prefrontal channel and a parietal channel, thereby systematically presenting the phase synchronization between the prefrontal and parietal lobes.

[0045] Finally, the channel pair with the highest PLV value was selected in the phase synchronization matrix and set as the target channel pair. This channel pair reflects the most significant phase synchronization between the prefrontal cortex and the parietal cortex in the working memory task and is therefore selected as the key stimulation site for working memory enhancement intervention.

[0046] S104, extracting frequency band features based on the associated signal data of the target channel pair to generate stimulation parameters for radial cranial electrical stimulation; In some embodiments of the present application, the specific process of extracting frequency band features to generate stimulation parameters for transcranial electrical stimulation based on the associated signal data of the target channel pair includes: performing frequency band separation on the EEG signals of the target channel pair, and extracting the signals of the theta band and the alpha band respectively through a bandpass filter or a wavelet transform; calculating the phase locking value of the target channel pair on the theta frequency and the alpha frequency band; comparing the phase locking values ​​of the theta band and the alpha band, and taking the frequency band corresponding to the larger phase locking value as the target frequency band; for the target frequency band, converting the time domain signal into a frequency domain signal through a fast Fourier transform to obtain the amplitude and phase information within the target frequency band; generating stimulation parameters for transcranial electrical stimulation based on the target frequency band and the amplitude and phase information within the target frequency band.

[0047] Specifically, the process of generating stimulation parameters for transcranial electrical stimulation based on the target frequency band, the amplitude and phase information within the target frequency band is as follows: using the frequency of the target frequency band as the stimulation frequency; calculating the average amplitude of the target frequency band based on the amplitude within the target frequency band; using the average amplitude of the target frequency band as the intensity of the stimulation current; calculating the phase difference of the target channel pair based on the phase information of the target frequency band; setting the phase parameters of the stimulation signal based on the phase difference of the target channel pair; using the stimulation frequency, the intensity of the stimulation current and the phase parameters as the stimulation parameters for transcranial electrical stimulation.

[0048] In some embodiments of the present application, the method also includes: within a preset stimulation duration, when the phase locking value within the target frequency band reaches a set threshold, pausing the transcranial electrical stimulation of the target object; or, within a preset stimulation duration, when the phase locking value within the target frequency band does not reach the set threshold, resuming the transcranial electrical stimulation of the target object.

[0049] S105, outputting transcranial electrical stimulation according to the stimulation parameters, and collecting endogenous EEG signals during the stimulation in real time, so as to dynamically adjust the stimulation parameters to achieve closed-loop regulation of the brain area of ​​the target object.

[0050] In the embodiment of the present application, the effect of each stimulation is fed back to the system through a closed-loop feedback mechanism, and the stimulation parameters are further optimized after a new round of data collection and analysis. The electrical stimulation device can automatically adjust the stimulation frequency and intensity according to the current working memory performance and PLV value to ensure the optimal regulation effect, thereby further promoting the phase synchronization of the prefrontal lobe and the parietal lobe and improving the cognitive coordination activity of the brain.

[0051] In an embodiment of the present application, stimulation parameters are used to perform transcranial electrical stimulation on the target object, and at the same time, the steps of real-time reception and preprocessing of endogenous EEG signals are continued to be performed, so that closed-loop control is achieved. The closed-loop control mechanism allows the system to continuously optimize the stimulation parameters based on the latest EEG signal feedback to adapt to the changes in the brain state of the target object during the execution of cognitive tasks. Through continuous real-time monitoring and optimization of stimulation parameters, the system can dynamically generate stimulation parameters suitable for different individuals, achieve precise control of endogenous phase difference, and thus enhance working memory performance.

[0052] The following is a system embodiment of the present application, which can be used to execute the method embodiment of the present application. For details not disclosed in the system embodiment of the present application, please refer to the method embodiment of the present application.

[0053] See also Figure 2 , which shows a schematic diagram of the structure of a transcranial electrical stimulation control system for enhancing working memory provided by an exemplary embodiment of the present application. The transcranial electrical stimulation control system for enhancing working memory can be implemented as all or part of an electronic device through software, hardware, or a combination of both. The system includes an intelligent terminal 107, a transcranial electrical stimulation device 106, and a multi-channel EEG acquisition device 108; the intelligent terminal 107 is communicatively connected to the transcranial electrical stimulation device 106, and the multi-channel EEG acquisition device 108 is electrically connected to the transcranial electrical stimulation device 106; wherein, The smart terminal 107 is used to generate an electrical stimulation instruction according to the cognitive task and send it to the transcranial electrical stimulation device 106; the transcranial electrical stimulation device 106 is used to perform transcranial electrical stimulation on the target object wearing the multi-channel EEG acquisition device 108 according to the received electrical stimulation instruction; in the process of performing a transcranial cognitive task on the target object wearing the multi-channel EEG acquisition device, the multi-channel endogenous EEG signals of the target object during the cognitive task are collected and preprocessed to obtain the EEG signals to be analyzed; the EEG signals to be analyzed are aligned with the behavioral performance labels generated by the target object during the cognitive task to obtain the associated signals; according to the associated signals, the channel pair with the highest phase synchronization between the prefrontal lobe and the parietal lobe of the target object is obtained as the target channel pair; based on the associated signal data of the target channel pair, the frequency band characteristics are extracted to generate the stimulation parameters of the transcranial electrical stimulation; the transcranial electrical stimulation is output according to the stimulation parameters, and the endogenous EEG signals during the stimulation are collected in real time, so as to dynamically adjust the stimulation parameters, realize closed-loop regulation of the brain area of ​​the target object, and generate transcranial electrical stimulation information to be fed back to the smart terminal 107.

[0054] Specifically, the transcranial electrical stimulation device 106 includes a power module 101, an acquisition module 105, a stimulation module 104, a control module 102, and a communication module 103; the power module 101 provides the electrical energy required for the transcranial electrical stimulation device 106 to work. The communication module 103 completes the wireless connection between the transcranial electrical stimulation device 106 and the intelligent terminal 107. The stimulation module 104 outputs current stimulation of multiple channels. The acquisition module 105 acquires EEG data of multiple channels.

[0055] Specifically, the control module 102 controls the normal operation of the entire host, and the functions completed include: 1) monitoring the remaining battery in the power module 101; 2) completing data communication with the intelligent terminal 107 through the communication module 103, including receiving control instructions and query instructions, and sending EEG data and system status data; 3) generating multi-channel independently controllable current stimulation signals through the stimulation module 104, and the adjustable parameters include mode, amplitude, frequency, phase, etc.; 4) collecting multi-channel independently controllable EEG data through the acquisition module 105, and the adjustable parameters include channel enable, sampling rate, gain, etc. The intelligent terminal 107 completes communication with the host by running the self-developed application software, including sending instructions and receiving, calculating, storing, and displaying data. The multi-channel EEG acquisition device 108 is made of a flexible and lightweight material, and is equipped with a freely detachable EEG stimulation integrated electrode. By sticking to the scalp, it completes the collection of EEG data and the stimulation current is applied to the brain.

[0056] In an embodiment of the present application, stimulation parameters are used to perform transcranial electrical stimulation on the target object, and at the same time, the steps of real-time reception and preprocessing of endogenous EEG signals are continued to be performed, so that closed-loop control is achieved. The closed-loop control mechanism allows the system to continuously optimize the stimulation parameters based on the latest EEG signal feedback to adapt to the changes in the brain state of the target object during the execution of cognitive tasks. Through continuous real-time monitoring and optimization of stimulation parameters, the system can dynamically generate stimulation parameters suitable for different individuals, achieve precise control of endogenous phase difference, and thus enhance working memory performance.

[0057] The present application also provides a computer-readable medium having program instructions stored thereon, which, when executed by a processor, implement the transcranial electrical stimulation control method for enhancing working memory provided by the above-mentioned various method embodiments.

[0058] The present application also provides a computer program product comprising instructions, which, when executed on a computer, enables the computer to execute the transcranial electrical stimulation control method for enhancing working memory of the above-mentioned various method embodiments.

[0059] See also Figure 3 , is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application. Figure 3 As shown, the electronic device 1000 may include: at least one processor 1001 , at least one network interface 1004 , a user interface 1003 , a memory 1005 , and at least one communication bus 1002 .

[0060] The communication bus 1002 is used to realize the connection and communication between these components.

[0061] The user interface 1003 may include a display screen (Display) and a camera (Camera). Optionally, the user interface 1003 may also include a standard wired interface and a wireless interface.

[0062] The network interface 1004 may optionally include a standard wired interface or a wireless interface (such as a WI-FI interface).

[0063] Among them, the processor 1001 may include one or more processing cores. The processor 1001 uses various interfaces and lines to connect various parts of the entire electronic device 1000, and executes various functions and processes data of the electronic device 1000 by running or executing instructions, programs, code sets or instruction sets stored in the memory 1005, and calling data stored in the memory 1005. Optionally, the processor 1001 can be implemented in at least one hardware form of digital signal processing (Digital Signal Processing, DSP), field programmable gate array (Field-Programmable Gate Array, FPGA), and programmable logic array (Programmable Logic Array, PLA). The processor 1001 can integrate one or a combination of a central processing unit (Central Processing Unit, CPU), a graphics processing unit (Graphics Processing Unit, GPU) and a modem. Among them, the CPU mainly processes the operating system, user interface and application programs; the GPU is responsible for rendering and drawing the content to be displayed on the display screen; the modem is used to process wireless communications. It can be understood that the above-mentioned modem may not be integrated into the processor 1001, and it can be implemented separately through a chip.

[0064] Among them, the memory 1005 may include a random access memory (Random Access Memory, RAM) and may also include a read-only memory (Read-Only Memory). Optionally, the memory 1005 includes a non-transitory computer-readable storage medium. The memory 1005 can be used to store instructions, programs, codes, code sets or instruction sets. The memory 1005 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for at least one function (such as a touch function, a sound playback function, an image playback function, etc.), instructions for implementing the above-mentioned various method embodiments, etc.; the data storage area may store data involved in the above-mentioned various method embodiments, etc. The memory 1005 may also be optionally at least one storage system located away from the aforementioned processor 1001. As Figure 3 As shown, the memory 1005 as a computer storage medium may include an operating system, a network communication module, a user interface module, and a transcranial electrical stimulation control application for enhancing working memory.

[0065] exist Figure 3In the electronic device 1000 shown, the user interface 1003 is mainly used to provide an input interface for the user and obtain data input by the user; and the processor 1001 can be used to call the transcranial electrical stimulation control application for enhancing working memory stored in the memory 1005, and specifically perform the following operations: In the process of performing a cognitive task on a target subject wearing a multi-channel EEG acquisition device, the multi-channel endogenous EEG signals of the target subject during the cognitive task are collected and pre-processed to obtain the EEG signals to be analyzed; Aligning the EEG signal to be analyzed with the behavioral performance label generated by the target subject during the cognitive task to obtain the correlation signal; According to the correlation signal, the channel pair with the highest phase synchronization between the prefrontal lobe and the parietal lobe of the target object is obtained as the target channel pair; Based on the correlation signal data of the target channel pair, frequency band features are extracted to generate stimulation parameters for radial cranial electrical stimulation; Transcranial electrical stimulation is output according to the stimulation parameters, and endogenous EEG signals are collected in real time during the stimulation period, so as to dynamically adjust the stimulation parameters to achieve closed-loop regulation of the brain area of ​​the target object.

[0066] In one embodiment, the processor 1001 performs the following operations when collecting and preprocessing the multi-channel endogenous EEG signals of the target subject during the cognitive task: Collect multi-channel endogenous EEG signals of the target subject during the cognitive task; A Butterworth notch filter is used to remove the power frequency interference of endogenous EEG signals; Perform baseline drift correction on the endogenous EEG signal after removing the power frequency interference; The endogenous EEG signals after baseline drift correction were bandpass filtered using a Butterworth bandpass filter; Independent component analysis was used to identify and remove artifacts from endogenous EEG signals after bandpass filtering.

[0067] In one embodiment, when the processor 1001 performs the alignment process of the EEG signal to be analyzed with the behavior performance label of the target object during the cognitive task, the processor 1001 specifically performs the following operations: Dividing the EEG signal to be analyzed according to a preset period to obtain EEG data of multiple periods; Generate a behavior performance label of the target object in each cycle through the behavior performance of the target object during the cognitive task; Associating the EEG data of each cycle with the behavior performance label of the target object of each cycle to obtain a first mapping relationship between multiple EEG data and the behavior performance labels; Determine whether the behavior performance labels in each first mapping relationship meet a preset condition, and delete the first mapping relationship to which the behavior performance labels that do not meet the preset condition belong, to obtain a plurality of second mapping relationships; Performing baseline correction on the EEG data in each second mapping relationship to remove the second mapping relationship to which the EEG data belonging to chronic drift belongs, to obtain a plurality of third mapping relationships; The EEG data corresponding to the same behavior performance labels in multiple third mapping relationships are superimposed and averaged to obtain an EEG signal that meets preset conditions.

[0068] In one embodiment, when the processor 1001 executes the acquisition of the channel pair with the highest phase synchronization between the frontal lobe and the parietal lobe of the target object as the target channel pair according to the correlation signal, the processor 1001 specifically performs the following operations: Identify prefrontal electrode pathways that reflect decision-making and emotion regulation, and parietal electrode pathways that reflect attention regulation and sensory information integration; Selecting the to-be-processed associated signals corresponding to the frontal lobe electrode channel and the parietal lobe electrode channel from the associated signals; From the signals to be processed, signals in the target frequency bands closely related to working memory are determined, the target frequency bands including the theta band of 4-8 Hz and the alpha band of 8-13 Hz; Extracting phase information within the signal in the target frequency band through Hilbert transform; According to the phase information, the channel pair with the highest phase synchronization between the prefrontal lobe and parietal lobe of the target subject is determined as the target channel pair.

[0069] In one embodiment, when the processor 1001 determines the channel pair with the highest phase synchronization between the frontal lobe and the parietal lobe of the target object as the target channel pair according to the phase information, the processor 1001 specifically performs the following operations: Based on the extracted phase information, the phase locking value between each prefrontal channel and parietal channel pair is calculated; Based on the phase locking value, a phase synchronization matrix is ​​constructed; In the phase synchronicity matrix, select the channel pair with the highest phase locking value; The channel pair with the highest phase locking value was taken as the channel pair with the highest phase synchronization between the prefrontal and parietal lobes of the target subject; The channel pair with the highest phase synchronization is selected as the target channel pair.

[0070] In one embodiment, when the processor 1001 extracts frequency band features based on the associated signal data of the target channel pair to generate stimulation parameters for transcranial electrical stimulation, the processor 1001 specifically performs the following operations: The EEG signals of the target channel pair are separated by frequency bands, and the signals of the theta band and the alpha band are extracted respectively by bandpass filter or wavelet transform; Calculate the phase locking value of the target channel pair in the theta frequency and alpha frequency band; Compare the phase locking values ​​of theta band and alpha band, and take the band corresponding to the larger phase locking value as the target band; For the target frequency band, the time domain signal is converted into a frequency domain signal by fast Fourier transform to obtain the amplitude and phase information within the target frequency band; Stimulation parameters for transcranial electrical stimulation are generated according to the target frequency band, the amplitude and the phase information within the target frequency band.

[0071] In one embodiment, when the processor 1001 generates stimulation parameters for transcranial electrical stimulation according to the target frequency band, the amplitude and phase information within the target frequency band, the processor 1001 specifically performs the following operations: The frequency of the target frequency band is used as the stimulation frequency; According to the amplitude in the target frequency band, the average amplitude of the target frequency band is calculated; The average amplitude of the target frequency band is taken as the intensity of the stimulation current; According to the phase information of the target frequency band, the phase difference of the target channel pair is calculated; setting a phase parameter of the stimulation signal based on the phase difference of the target channel pair; The stimulation frequency, the intensity of the stimulation current and the phase parameters are used as the stimulation parameters of transcranial electrical stimulation.

[0072] In one embodiment, the processor 1001 further performs the following operations: During the preset stimulation duration, when the phase lock value in the target frequency band reaches the set threshold, the transcranial electrical stimulation of the target object is suspended; or, Within the preset stimulation duration, when the phase locking value in the target frequency band does not reach the set threshold, transcranial electrical stimulation of the target object is resumed.

[0073] In an embodiment of the present application, stimulation parameters are used to perform transcranial electrical stimulation on the target object, and at the same time, the steps of real-time reception and preprocessing of endogenous EEG signals are continued to be performed, so that closed-loop control is achieved. The closed-loop control mechanism allows the system to continuously optimize the stimulation parameters based on the latest EEG signal feedback to adapt to the changes in the brain state of the target object during the execution of cognitive tasks. Through continuous real-time monitoring and optimization of stimulation parameters, the system can dynamically generate stimulation parameters suitable for different individuals, achieve precise control of endogenous phase difference, and thus enhance working memory performance.

[0074] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through a computer program, and the program for enhancing working memory through transcranial electrical stimulation control can be stored in a computer-readable storage medium, and when the program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, the storage medium of the program for enhancing working memory through transcranial electrical stimulation control can be a disk, an optical disk, a read-only storage memory, or a random access memory, etc.

[0075] The above disclosure is only the preferred embodiment of the present application, which certainly cannot be used to limit the scope of rights of the present application. Therefore, equivalent changes made according to the claims of the present application are still within the scope covered by the present application.

Claims

1. A transcranial electrical stimulation control method for enhancing working memory, characterized in that: Applied to a transcranial electrical stimulation device, the method comprises: During the process of performing a cognitive task on a target subject wearing a multi-channel EEG acquisition device, the multi-channel endogenous EEG signals of the target subject during the cognitive task are collected and pre-processed to obtain the EEG signals to be analyzed; Aligning the EEG signal to be analyzed with the behavioral performance label generated by the target subject during the performance of the cognitive task to obtain a correlation signal; According to the correlation signal, obtaining a channel pair with the highest phase synchronization between the prefrontal lobe and the parietal lobe of the target object as a target channel pair; Extracting frequency band features based on the associated signal data of the target channel pair to generate stimulation parameters for radial cranial electrical stimulation; Transcranial electrical stimulation is output according to the stimulation parameters, and endogenous EEG signals during the stimulation are collected in real time, so as to dynamically adjust the stimulation parameters and realize closed-loop regulation of the brain area of ​​the target object.

2. The method according to claim 1, characterized in that: The collecting and preprocessing of multi-channel endogenous EEG signals of the target subject during the cognitive task includes: Collecting multi-channel endogenous EEG signals of the target subject during the performance of a cognitive task; A Butterworth notch filter is used to remove power frequency interference of the endogenous EEG signal; Performing baseline drift correction on the endogenous EEG signal after removing power frequency interference; Using a Butterworth bandpass filter to bandpass filter the endogenous EEG signal after baseline drift correction; Independent component analysis technology is used to identify and remove artifacts from the endogenous EEG signal after bandpass filtering.

3. The method according to claim 1, characterized in that Aligning the EEG signal to be analyzed with the behavior performance label generated by the target subject during the performance of the cognitive task to obtain a correlation signal, including: Dividing the EEG signal to be analyzed according to preset cycles to obtain EEG data of multiple cycles; Generate a behavior performance label of the target object in each cycle based on the behavior performance of the target object during the execution of the cognitive task; Associating the EEG data of each cycle with the behavior performance label of the target object of each cycle to obtain a first mapping relationship between multiple EEG data and behavior performance labels; Determine whether the behavior performance labels in each first mapping relationship meet a preset condition, and delete the first mapping relationship to which the behavior performance labels that do not meet the preset condition belong, to obtain a plurality of second mapping relationships; Performing baseline correction on the EEG data in each second mapping relationship to remove the second mapping relationship to which the EEG data belonging to chronic drift belongs, to obtain a plurality of third mapping relationships; The EEG data corresponding to the same behavior performance labels in the multiple third mapping relationships are superimposed and averaged to obtain an EEG signal that meets preset conditions.

4. The method according to claim 1, characterized in that: The step of acquiring, according to the correlation signal, a channel pair with the highest phase synchronization between the frontal lobe and the parietal lobe of the target object as a target channel pair comprises: Identify prefrontal electrode pathways that reflect decision-making and emotion regulation, and parietal electrode pathways that reflect attention regulation and sensory information integration; Selecting, from the associated signals, the associated signals to be processed corresponding to the prefrontal electrode channel and the parietal electrode channel; Determining, from the signals to be processed, signals in a target frequency band that is closely related to working memory, the target frequency band comprising a theta frequency band of 4-8 Hz and an alpha frequency band of 8-13 Hz; Extracting phase information in the signal on the target frequency band by Hilbert transform; According to the phase information, a channel pair with the highest phase synchronization between the prefrontal lobe and the parietal lobe of the target object is determined as a target channel pair.

5. The method according to claim 4, characterized in that The step of determining, according to the phase information, a channel pair with the highest phase synchronization between the frontal lobe and the parietal lobe of the target object as a target channel pair comprises: Calculating the phase locking value between each prefrontal channel and parietal channel pair according to the extracted phase information; Based on the phase locking value, construct a phase synchronicity matrix; In the phase synchronicity matrix, selecting a channel pair with the highest phase locking value; The channel pair with the highest phase locking value is used as the channel pair with the highest phase synchronization between the prefrontal lobe and the parietal lobe of the target object; The channel pair with the highest phase synchronization is taken as the target channel pair.

6. The method according to claim 5, characterized in that The calculation formula of the phase lock value is: in, is the phase lock value, is the number of signal samples in the target frequency band, Indicated in The phase difference between the two signals at the sample points is the instantaneous phase of the prefrontal channel. and the instantaneous phase of the parietal channels In the The difference between the sample points, is a complex exponent, is the imaginary unit, and the complex exponent represents the The phase difference between the two signals at the sample points is expressed as a vector on the complex plane. Indicated in The accumulation of all complex exponentials within sample points, For the A point in time, For the A point in time, represents the magnitude of a complex number, A mathematical constant representing the exponential of a complex number.

7. The method according to claim 1, characterized in that The step of extracting frequency band features based on the associated signal data of the target channel pair to generate stimulation parameters for cranial electrical stimulation includes: Performing frequency band separation on the EEG signal of the target channel pair, and extracting the signals of theta frequency band and alpha frequency band respectively by bandpass filter or wavelet transform; Calculating the phase locking value of the target channel pair in theta frequency and alpha frequency band; Comparing the phase locking values ​​of the theta frequency band and the alpha frequency band, and taking the frequency band corresponding to the larger phase locking value as the target frequency band; For the target frequency band, converting the time domain signal into a frequency domain signal by fast Fourier transform to obtain amplitude and phase information within the target frequency band; Stimulation parameters for transcranial electrical stimulation are generated according to the target frequency band, the amplitude and the phase information within the target frequency band.

8. The method according to claim 7, characterized in that Generating stimulation parameters for transcranial electrical stimulation according to the target frequency band, the amplitude and phase information within the target frequency band, comprises: Using the frequency of the target frequency band as the stimulation frequency; Calculating the average amplitude of the target frequency band according to the amplitude within the target frequency band; The average amplitude of the target frequency band is used as the intensity of the stimulation current; Calculating the phase difference of the target channel pair according to the phase information of the target frequency band; Setting a phase parameter of a stimulation signal based on a phase difference of the target channel pair; The stimulation frequency, the intensity of the stimulation current and the phase parameters are used as stimulation parameters of transcranial electrical stimulation.

9. The method according to claim 7, characterized in that: The method further comprises: During the preset stimulation duration, when the phase lock value in the target frequency band reaches a set threshold, suspending the transcranial electrical stimulation of the target object; or, Within the preset stimulation duration, when the phase locking value in the target frequency band does not reach the set threshold, the transcranial electrical stimulation of the target object is resumed.

10. A transcranial electrical stimulation control system for enhancing working memory, characterized in that: The system comprises: An intelligent terminal, a transcranial electrical stimulation device and a multi-channel EEG acquisition device; the intelligent terminal is communicatively connected to the transcranial electrical stimulation device, and the multi-channel EEG acquisition device is electrically connected to the transcranial electrical stimulation device; wherein, The intelligent terminal is used to generate electrical stimulation instructions according to cognitive tasks and send them to the transcranial electrical stimulation device; The transcranial electrical stimulation device is used to perform transcranial electrical stimulation on a target object wearing a multi-channel EEG acquisition device according to the received electrical stimulation instructions; in the process of performing cognitive tasks on the target object wearing the multi-channel EEG acquisition device, collect and preprocess the multi-channel endogenous EEG signals of the target object during the cognitive task to obtain the EEG signals to be analyzed; align the EEG signals to be analyzed with the behavioral performance labels generated by the target object during the cognitive task to obtain associated signals; according to the associated signals, obtain the channel pair with the highest phase synchronization between the prefrontal lobe and the parietal lobe of the target object as the target channel pair; based on the associated signal data of the target channel pair, extract the frequency band characteristics to generate stimulation parameters for transcranial electrical stimulation; output transcranial electrical stimulation according to the stimulation parameters, and collect endogenous EEG signals during the stimulation in real time, so as to dynamically adjust the stimulation parameters, realize closed-loop regulation of the brain area of ​​the target object, and generate transcranial electrical stimulation information to feedback to the intelligent terminal.

Images