A transcranial electrical stimulation control method and system for enhancing working memory

Abstract

The present application discloses a transcranial electrical stimulation control method and system for enhancing working memory. The method includes: collecting and preprocessing multi-channel endogenous electroencephalogram (EEG) signals of a target object during the execution of a cognitive task to obtain EEG signals to be analyzed; aligning the EEG signals to be analyzed with the behavioral performance labels generated by the target object during the execution of the cognitive task to obtain associated signals; according to the associated signals, obtaining the channel pair with the highest phase synchrony 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, extracting frequency band features to generate stimulation parameters for transcranial electrical stimulation; 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 and achieve closed-loop regulation of the brain region of the target object. Therefore, by using the present application, precise control of the endogenous phase difference between the prefrontal lobe and the parietal lobe can be achieved, thereby enhancing working memory performance.

Description

Technical Field

[0001] This application relates to the field of cognitive neuroscience technology, and particularly relates 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 crucial for the execution of complex cognitive tasks. For example, in the N-back task, participants need to remember and update a series of presented stimulus items in real time, and judge whether the new stimulus is the same as the Nth previous stimulus when it appears. This task has important value for evaluating 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 means such as transcranial electrical stimulation (tES). These methods improve cognitive performance by applying specific current stimulation. However, traditional stimulation methods usually based on fixed stimulation parameters fail to consider the changes in endogenous brain electrical activity of individuals during task execution in real time, which leads to limited stimulation effects. Summary of the Invention

[0004] Embodiments of this application provide a transcranial electrical stimulation control method and system for enhancing working memory. To have a basic understanding of some aspects of the disclosed embodiments, a simple summary is given below. This summary part is not a general review, nor is it to identify key / important constituent elements or delineate the protection scope of these embodiments. Its sole purpose is to present some concepts in a simple form as a preface to the subsequent detailed description.

[0005] In a first aspect, embodiments of this application provide a transcranial electrical stimulation control method for enhancing working memory, which is applied to a transcranial electrical stimulation device. The method includes:

[0006] During the process of a target object wearing a multi-channel electroencephalogram acquisition device performing a cognitive task, collect and preprocess the multi-channel endogenous brain electrical signals of the target object during the execution of the cognitive task to obtain the brain electrical signals to be analyzed;

[0007] Align the brain electrical signals to be analyzed with the behavior performance labels generated by the target object during the execution of the cognitive task to obtain associated signals;

[0008] According to the associated signals, obtain the channel pair with the highest phase synchrony between the prefrontal lobe and the parietal lobe of the target object as the target channel pair;

[0009] Based on the associated signal data of the target channel pair, extract frequency band features to generate stimulation parameters for transcranial electrical stimulation;

[0010] Output transcranial electrical stimulation according to the stimulation parameters, and collect the endogenous electroencephalogram (EEG) signals during the stimulation in real time, so as to dynamically adjust the stimulation parameters to achieve closed-loop regulation of the brain region of the target object.

[0011] Optionally, collect and preprocess the multi-channel endogenous EEG signals of the target object during the execution of the cognitive task, including:

[0012] Collect the multi-channel endogenous EEG signals of the target object during the execution of the cognitive task;

[0013] Use a Butterworth notch filter to remove the power frequency interference of the endogenous EEG signals;

[0014] Perform baseline drift correction on the endogenous EEG signals after removing the power frequency interference;

[0015] Use a Butterworth band-pass filter to perform band-pass filtering on the endogenous EEG signals after baseline drift correction;

[0016] Use independent component analysis technology to identify and remove artifacts from the band-pass filtered endogenous EEG signals.

[0017] Optionally, align the EEG signals to be analyzed with the behavioral performance labels generated by the target object during the execution of the cognitive task to obtain associated signals, including:

[0018] Divide the EEG signals to be analyzed according to a preset period to obtain EEG data for multiple periods;

[0019] Generate the behavioral performance labels of the target object for each period through the behavioral performance of the target object during the execution of the cognitive task;

[0020] Associate the EEG data for each period with the behavioral performance labels of the target object for each period to obtain a first mapping relationship between multiple EEG data and behavioral performance labels;

[0021] Judge whether the behavioral performance labels in each first mapping relationship meet the preset conditions, and delete the first mapping relationships to which the behavioral performance labels that do not meet the preset conditions belong to obtain multiple second mapping relationships;

[0022] Perform baseline correction on the EEG data in each second mapping relationship to remove the second mapping relationships to which the EEG data belonging to chronic drift belong to obtain multiple third mapping relationships;

[0023] Perform superposition and averaging processing on the EEG data corresponding to the same behavioral performance labels in multiple third mapping relationships to obtain EEG signals that meet the preset conditions.

[0024] Optionally, according to the correlation signal, obtain the channel pair with the highest phase synchrony between the prefrontal lobe and the parietal lobe of the target object as the target channel pair, including:

[0025] Determine the prefrontal lobe electrode channels reflecting decision-making and emotion regulation functions and the parietal lobe electrode channels related to attention regulation and sensory information integration;

[0026] Select the correlation signals to be processed corresponding to the prefrontal lobe electrode channels and the parietal lobe electrode channels from the correlation signals;

[0027] Determine the signals in the target frequency band closely related to working memory from the signals to be processed, and the target frequency band includes the theta band of 4 - 8 Hz and the alpha band of 8 - 13 Hz;

[0028] Extract the phase information in the signals in the target frequency band through Hilbert transform;

[0029] According to the phase information, determine the channel pair with the highest phase synchrony between the prefrontal lobe and the parietal lobe of the target object as the target channel pair.

[0030] Optionally, according to the phase information, determine the channel pair with the highest phase synchrony between the prefrontal lobe and the parietal lobe of the target object as the target channel pair, including:

[0031] Calculate the phase locking value between each prefrontal lobe channel and the parietal lobe channel pair according to the extracted phase information;

[0032] Construct a phase synchrony matrix based on the phase locking values;

[0033] In the phase synchrony matrix, screen out the channel pair with the highest phase locking value;

[0034] Take the channel pair with the highest phase locking value as the channel pair with the highest phase synchrony between the prefrontal lobe and the parietal lobe of the target object;

[0035] Take the channel pair with the highest phase synchrony as the target channel pair.

[0036] Optionally, the calculation formula of the phase locking value is:

[0037]

[0038] Wherein, is the phase locking value, is the number of signal samples in the target frequency band, represents at the th sample point the phase difference between the two signals, that is, the instantaneous phase of the prefrontal lobe channel and the instantaneous phase of the parietal lobe channel at the The difference at a sample point, is a complex exponent, where \(i\) is the imaginary unit, and the complex exponent represents the phase difference between two signals at the th sample point, and this phase difference is represented by a vector in the complex plane. denotes the accumulation of all complex exponents within sample points. is the th time point, is the th time point. denotes the magnitude of the complex number, denotes the mathematical constant of the complex exponent.

[0039] Optionally, based on the correlated signal data of the target channel pair, extract frequency band features to generate stimulation parameters for transcranial electrical stimulation, including:

[0040] Perform frequency band separation on the electroencephalogram signals of the target channel pair, and respectively extract the signals in the theta frequency band and the alpha frequency band through a band-pass filter or wavelet transform;

[0041] Calculate the phase locking value of the target channel pair in the theta frequency and the alpha frequency band;

[0042] Compare the phase locking values of the theta frequency band and the alpha frequency band, and take the frequency band corresponding to the larger phase locking value as the target frequency band;

[0043] For the target frequency band, convert the time-domain signal to the frequency-domain signal through fast Fourier transform to obtain the magnitude and phase information within the target frequency band;

[0044] Generate stimulation parameters for transcranial electrical stimulation according to the target frequency band, the magnitude and phase information within the target frequency band.

[0045] Optionally, generate stimulation parameters for transcranial electrical stimulation according to the target frequency band, the magnitude and phase information within the target frequency band, including:

[0046] Take the frequency of the target frequency band as the stimulation frequency;

[0047] Calculate the average magnitude of the target frequency band according to the magnitude within the target frequency band;

[0048] Take the average magnitude of the target frequency band as the intensity of the stimulation current;

[0049] Calculate the phase difference of the target channel pair according to the phase information of the target frequency band;

[0050] Set the phase parameter of the stimulation signal based on the phase difference of the target channel pair;

[0051] The stimulation frequency, the intensity of the stimulation current, and the phase parameter are used as the stimulation parameters for transcranial electrical stimulation.

[0052] Optionally, the method further includes:

[0053] During a pre-set stimulation duration, when the phase locking value within the target frequency band reaches the set threshold, the transcranial electrical stimulation of the target object is paused; or,

[0054] During a pre-set stimulation duration, when the phase locking value within the target frequency band does not reach the set threshold, the transcranial electrical stimulation of the target object is resumed.

[0055] In a second aspect, an embodiment of the present application provides a transcranial electrical stimulation control system for enhancing working memory. The system includes:

[0056] An intelligent terminal, a transcranial electrical stimulation device, and a multi-channel electroencephalogram acquisition device; the intelligent terminal is communicatively connected to the transcranial electrical stimulation device, and the multi-channel electroencephalogram acquisition device is electrically connected to the transcranial electrical stimulation device; wherein,

[0057] The intelligent terminal is configured to generate an electrical stimulation instruction according to a cognitive task and send it to the transcranial electrical stimulation device;

[0058] The transcranial electrical stimulation device is configured to perform transcranial electrical stimulation on a target object wearing the multi-channel electroencephalogram acquisition device according to the received electrical stimulation instruction; during the process of performing a transcranial cognitive task on the target object wearing the multi-channel electroencephalogram acquisition device, collect and preprocess multi-channel endogenous electroencephalogram signals of the target object during the execution of the cognitive task to obtain electroencephalogram signals to be analyzed; align the electroencephalogram signals to be analyzed with the behavioral performance labels generated by the target object during the execution of the cognitive task to obtain associated signals; output transcranial electrical stimulation according to the stimulation parameters, and collect endogenous electroencephalogram signals during the stimulation in real time, so as to dynamically adjust the stimulation parameters, realize closed-loop regulation of the brain region of the target object, and generate transcranial electrical stimulation information and feedback it to the intelligent terminal.

[0059] The technical solution provided by the embodiment of the present application may include the following beneficial effects:

[0060] In the embodiment of the present application, transcranial electrical stimulation is performed on the target object using the stimulation parameters, and at the same time, the step of continuously receiving and preprocessing endogenous electroencephalogram signals in real time is continued, so that closed-loop regulation is realized. The closed-loop regulation mechanism allows the system to continuously optimize the stimulation parameters according to the latest electroencephalogram signal feedback to adapt to the changes in the brain state of the target object during the execution of the cognitive task. Through continuous real-time monitoring and optimization of the stimulation parameters, the system can dynamically generate stimulation parameters suitable for different individuals, realize precise control of the endogenous phase difference, and thus enhance the working memory performance.

[0061] It should be understood that the above general description and the following detailed description are merely exemplary and explanatory, and do not limit this application. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] The drawings herein are incorporated into and constitute a part of this specification, showing embodiments consistent with this application, and are used together with the specification to explain the principles of this application.

[0063] Figure 1 is a schematic flowchart of a transcranial electrical stimulation control method for enhancing working memory provided by an embodiment of this application;

[0064] Figure 2 is a schematic structural diagram of a transcranial electrical stimulation control system for enhancing working memory provided by this application;

[0065] Figure 3 is a schematic structural diagram of an electronic device provided by an embodiment of this application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0066] The following description and the drawings fully illustrate the specific embodiments of this application, enabling those skilled in the art to practice them.

[0067] It should be clear that the described embodiments are only a part of the embodiments of this application, rather than all of them. All other embodiments obtained by those of ordinary skill in the art based on the embodiments in this application without creative efforts belong to the scope of protection of this application.

[0068] 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 this application. On the contrary, they are merely examples of systems and methods consistent with some aspects of this application as detailed in the appended claims.

[0069] In the description of this application, it should be understood that terms such as "first", "second", etc. are only used for descriptive purposes and cannot be construed as indicating or implying relative importance. For those of ordinary skill in the art, the specific meanings of the above terms in this application can be understood according to specific circumstances. In addition, in the description of this application, unless otherwise specified, "a plurality of" means two or more. "And / or" describes the association relationship of associated objects and indicates that three relationships can exist. For example, A and / or B can represent: A exists alone, A and B exist simultaneously, and B exists alone. The character " / " generally represents an "or" relationship between the associated objects before and after.

[0070] 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 embodiments of the present application, transcranial electrical stimulation is performed on a target object using stimulation parameters, and at the same time, the step of continuously receiving and preprocessing endogenous electroencephalogram signals is continued, so that closed-loop regulation is achieved. The closed-loop regulation mechanism allows the system to continuously optimize the stimulation parameters according to the latest electroencephalogram 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 the stimulation parameters, the system can dynamically generate stimulation parameters suitable for different individuals, achieve precise control of the endogenous phase difference, and thus enhance working memory performance. The following will be described in detail using exemplary embodiments.

[0071] The following will be combined with the attached Figure 1 , and a transcranial electrical stimulation control method for enhancing working memory provided by the embodiments of the present application will be introduced in detail. This method can be implemented depending on a computer program and can run on a transcranial electrical stimulation control system for enhancing working memory based on the von Neumann architecture. This computer program can be integrated into an application or run as an independent tool-like application.

[0072] Please refer to Figure 1 , which is a schematic flowchart of a transcranial electrical stimulation control method for enhancing working memory provided by the embodiments of the present application, applied to a transcranial electrical stimulation device. As Figure 1 shown, the method of the embodiments of the present application may include the following steps:

[0073] S101, during the process of performing a cognitive task on a target object wearing a multi-channel electroencephalogram acquisition device, collect and preprocess the multi-channel endogenous electroencephalogram signals of the target object during the execution of the cognitive task to obtain the electroencephalogram signals to be analyzed;

[0074] Among them, the multi-channel electroencephalogram acquisition device is a head-mounted device that can simultaneously collect the electrical activity signals of the brain from multiple channels, and this signal can be used to analyze the activity state of the brain. The target object refers to an individual or subject who receives transcranial electrical stimulation and electroencephalogram signal acquisition. Transcranial electrical stimulation is a non-invasive neuromodulation technique that stimulates the brain by transmitting weak current through the scalp to regulate brain function.

[0075] In some embodiments of the present application, the specific process of collecting and preprocessing multi-channel endogenous electroencephalogram (EEG) signals of a target object during the execution of a cognitive task includes: collecting multi-channel endogenous EEG signals of the target object during the execution of the cognitive task; using a Butterworth notch filter to remove power frequency interference from the endogenous EEG signals; performing baseline drift correction on the endogenous EEG signals after removing the power frequency interference; using a Butterworth band-pass filter to perform band-pass filtering on the endogenous EEG signals after baseline drift correction; using independent component analysis (ICA) technology to identify and remove artifacts from the band-pass filtered endogenous EEG signals.

[0076] Among them, the Butterworth notch filter is a filter used to remove interference of specific frequencies, such as power frequency (50 Hz or 60 Hz) interference, from a signal. The Butterworth band-pass 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 and is commonly used in EEG signal processing to identify and remove artifacts such as eye movements and electromyogram (EMG).

[0077] For example, a volunteer wears a multi-channel EEG acquisition device equipped with 64 electrodes. These electrodes are distributed on the volunteer's scalp according to the international 10-20 system layout to cover the main brain regions of the scalp, especially the prefrontal and parietal regions. During the volunteer's execution 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 to filter specifically for power equipment noise at 50 Hz or 60 Hz to eliminate the negative impact on the signal. This filter is applied to process the entire 5-minute EEG signal record. Baseline drift correction is performed on the EEG signals after removing the power frequency interference by calculating the average value of the signals in each channel within 30 seconds before the start of the task and subtracting this average value from the entire record to eliminate long-term signal variations. A 4th-order Butterworth band-pass filter is used to filter the baseline drift corrected EEG signals, selecting a frequency band from 0.5 Hz to 65 Hz to remove low-frequency noise (such as heartbeat and respiration) and high-frequency noise (such as EMG interference), so as to focus on the frequency band related to brain electrical activity. Independent component analysis (ICA) technology is applied to analyze the band-pass filtered EEG signals to identify non-brain-derived signals, such as eye movement and EMG artifacts. These artifacts are removed from the EEG signals, and the clean EEG signals are retained as the EEG signals to be analyzed, providing an accurate data basis for subsequent phase synchrony analysis and transcranial electrical stimulation.

[0078] S102, align the EEG signals to be analyzed with the behavior performance labels generated by the target object during the execution of the cognitive task to obtain associated signals;

[0079] Among them, the electroencephalogram (EEG) signal to be analyzed is obtained by performing preliminary preprocessing on the EEG data collected in real time by a multi-channel EEG acquisition device during the execution of a cognitive task. The preprocessing includes removing noise and artifacts. The behavioral performance label refers to the response record of the target object to a specific task (such as a working memory task) during the execution of the cognitive task. 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 object in the task. The alignment process is a process of matching the time series data of the EEG signal with the behavioral performance label of the target object in time. This process can ensure that the changes in the EEG signal correspond to the behavioral performance of the target object at a specific moment. Screening out the EEG signals that meet the preset conditions means selecting those EEG signals that match the behavioral performance label of the target object according to a specific analysis purpose. For example, only select the EEG signals recorded when the target object makes a correct response to study the brain activity pattern during correct cognitive performance. The associated signals refer to the EEG signals that have been screened and match the behavioral performance label of the target object. These signals are considered to be related to specific cognitive processes or behavioral performances.

[0080] 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 a preset period to obtain EEG data for multiple periods; generating the behavioral performance label of the target object for each period through the behavioral performance of the target object during the execution of the cognitive task; associating the EEG data for each period with the behavioral performance label of the target object for each period to obtain a first mapping relationship between multiple EEG data and behavioral performance labels; determining whether the behavioral performance labels in each first mapping relationship meet the preset conditions, and deleting the first mapping relationships 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 relationships to which the EEG data belonging to chronic drift belong to obtain multiple third mapping relationships; performing superposition and averaging processing on the EEG data corresponding to the same behavioral performance labels in multiple third mapping relationships to obtain the EEG signals that meet the preset conditions.

[0081] Among them, the preset period refers to the time interval preset according to the experimental design or task characteristics when analyzing electroencephalogram (EEG) signals. For example, in the N-back task, the preset period may be the time interval between the presentation of each stimulus. The EEG data of the period refers to the EEG signal data collected within each preset period, and these data represent the activities of the brain within a specific time period. The behavioral performance refers to the response of the target object to the task within each preset period, such as correct or incorrect judgments, reaction times, etc. The behavioral performance label refers to the record of the behavioral performance of the target object corresponding to the EEG data of each period, which is used to identify the behavioral result within that period. The first mapping relationship refers to the relationship that associates the EEG data of each period with its corresponding behavioral performance label. This mapping relationship helps to analyze the brain activities during specific behavioral performances. The preset condition refers to the criteria used to filter data in the analysis, such as only selecting the periods with correct responses, or the periods with reaction times within a certain range. The second mapping relationship refers to the association relationship between the remaining EEG data that meets the conditions and the behavioral performance labels after deleting the first mapping relationship that does not meet the preset conditions. Baseline correction refers to the processing of EEG data to remove chronic drift (long-term changes), usually achieved by subtracting the average value during a certain baseline period. The third mapping relationship refers to the association relationship between the EEG data after baseline correction and the behavioral performance labels. Superposition and averaging processing refers to accumulating and averaging the EEG data of multiple periods corresponding to the same behavioral performance label to improve the signal-to-noise ratio and stability of the signal.

[0082] In some possible implementation manners, let the volunteers perform a 2-back task, where each stimulus is presented for 2500 milliseconds (ms) with an interval of 500 ms. Record the responses of the volunteers to each stimulus, including correct or incorrect judgments. Use a multi-channel EEG acquisition device to record the EEG signals of the volunteers in real time at a sampling rate of 1000 Hz. Align each 2500-ms EEG signal segment with the corresponding behavioral performance label (correct or incorrect) to ensure that each EEG signal segment is directly associated with a specific task performance. Eliminate those EEG data segments corresponding to incorrect answers or overly long reaction times. Only retain the signals related to good task performance to reduce the interference of invalid data on subsequent analysis. Perform baseline correction on each retained EEG signal segment, and calculate the baseline value of each segment (for example, the average value before the start of the task). Subtract the baseline value from the entire signal to eliminate chronic drift and ensure the stability and consistency of the signal in time. Perform superposition and averaging processing on all the baseline-corrected signal segments to improve the signal-to-noise ratio and reduce the interference of random noise. Extract the EEG activity characteristic signals with high stability and consistency, and these signals fully reflect the key brain activity characteristics of the user under high-quality task performance.

[0083] S103. Obtain the channel pair with the highest phase synchrony between the prefrontal lobe and the parietal lobe of the target object as the target channel pair according to the correlation signal;

[0084] Among them, the prefrontal lobe is the lobe in the front part of the brain, which is involved in higher cognitive functions such as decision-making, planning, and social behavior. The parietal lobe is the lobe at the top of the brain, which is involved in processing tactile, visual, and spatial information, etc. Phase synchrony refers to the phase consistency of two or more signals at specific time points. In electroencephalogram (EEG) signal analysis, phase synchrony can reflect the functional connection and collaborative activity between different brain regions. A channel pair refers to two electrode channels at different positions in an EEG acquisition device, which are used to record signals from different brain regions. The target channel pair refers to the channel pair between the prefrontal lobe and the parietal lobe that shows the highest phase synchrony.

[0085] In some embodiments of the present application, the specific process of obtaining the channel pair with the highest phase synchrony between the prefrontal lobe and the parietal lobe of the target object as the target channel pair according to the correlation signal includes: determining the prefrontal lobe electrode channels reflecting decision-making and emotion regulation functions and the parietal lobe electrode channels related to attention regulation and sensory information integration; selecting the to-be-processed correlation signals corresponding to the prefrontal lobe electrode channels and the parietal lobe electrode channels from the correlation signal; determining the signals in the target frequency band closely related to working memory from the to-be-processed signals, and the target frequency band includes the theta band of 4 - 8 Hz and the alpha band of 8 - 13 Hz; extracting the phase information in the signals in the target frequency band through Hilbert transform; determining the channel pair with the highest phase synchrony between the prefrontal lobe and the parietal lobe of the target object as the target channel pair according to the phase information.

[0086] Specifically, the specific process of determining the channel pair with the highest phase synchrony between the prefrontal lobe and the parietal lobe of the target object as the target channel pair according to the phase information includes: calculating the phase locking value between each prefrontal lobe channel and the parietal lobe channel pair according to the extracted phase information; constructing a phase synchrony matrix based on the phase locking value; screening out the channel pair with the highest phase locking value in the phase synchrony matrix; taking the channel pair with the highest phase locking value as the channel pair with the highest phase synchrony between the prefrontal lobe and the parietal lobe of the target object; taking the channel pair with the highest phase synchrony as the target channel pair.

[0087] Specifically, the calculation formula of the phase locking value is:

[0088]

[0089] Among them, is the phase locking value, is the number of signal samples in the target frequency band, represents at the The phase difference between two signals at a sample point, i.e., the instantaneous phase of the prefrontal channel and the instantaneous phase of the parietal channel at the -th sample point, is a complex exponential, is the imaginary unit, and the complex exponential represents the phase difference between two signals at the -th sample point, and this phase difference is represented by a vector in the complex plane. represents the accumulation of all complex exponentials within the sample points, is the -th time point, is the -th time point, represents the magnitude of the complex number, represents the mathematical constant of the complex exponential.

[0090] In some embodiments of the present application, based on the international 10 - 20 electrode positioning system, prefrontal and parietal electrode channels closely related to working memory are selected, including the F3, F4, F7, F8, Fp1, Fp2 channels in the prefrontal lobe and the P3, P4, P7, P8, Pz channels in the parietal lobe. This can ensure the acquisition of brain region signals with important functions during the execution of working memory tasks, laying a foundation for the accurate extraction of target channel pairs.

[0091] Next, among the correlated signals of the selected channels, focus on the frequency bands (4 - 13 Hz) closely related to working memory, namely the theta (4 - 8 Hz) and alpha (8 - 13 Hz) frequency 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 phase synchrony calculation. For a given channel signal , after its Hilbert transform, the complex form can be expressed as:

[0092] ;

[0093] where is the Hilbert transform part of the signal , is the imaginary unit. The instantaneous phase can then be extracted from the complex form:

[0094] ;

[0095] where is the phase angle of a 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.

[0096] Then, based on the extracted phase information, the Phase-Locking Value (PLV) between each prefrontal channel and parietal channel pair is calculated to quantify the level of phase synchrony between the two channels in a given frequency band. The value of PLV ranges from 0 to 1. The higher the value, the stronger the phase synchrony between the two channels. Its specific meaning is as follows:

[0097] When PLV = 1: It means that the phases of the two signals are completely consistent throughout the 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, possibly indicating coordinated activity between two brain regions, especially during the execution of cognitive tasks or specific neural processes.

[0098] When PLV = 0: It means that the phases of the two signals are completely inconsistent throughout the 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.

[0099] 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 a certain degree of functional connectivity between the channels, and may show a certain degree of coordination during specific task phases or brain activity states.

[0100] Furthermore, based on the PLV values of each prefrontal channel and parietal channel pair, a phase synchrony matrix is constructed, where each element of the matrix represents the PLV value between a specific prefrontal channel and a parietal channel, thus systematically presenting the phase synchrony situation between the prefrontal and parietal lobes.

[0101] Finally, the channel pair with the highest PLV value is selected from the phase synchrony matrix and set as the target channel pair. This channel pair reflects the most significant phase synchrony between the prefrontal and parietal lobes during the working memory task and is therefore selected as the key stimulation site for working memory enhancement intervention.

[0102] S104, based on the correlated signal data of the target channel pair, extract frequency band features to generate the stimulation parameters of transcranial electrical stimulation;

[0103] In some embodiments of the present application, the specific process of extracting frequency band features based on the correlation signal data of the target channel pair to generate the stimulation parameters for transcranial electrical stimulation includes: separating the frequency bands of the electroencephalogram signals of the target channel pair, and respectively extracting the signals in the theta frequency band and the alpha frequency band through a band-pass filter or wavelet transform; calculating the phase locking values of the target channel pair at the theta frequency and the 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 through fast Fourier transform to obtain the amplitude and phase information within the target frequency band; generating the stimulation parameters for transcranial electrical stimulation according to the target frequency band, the amplitude and phase information within the target frequency band.

[0104] Specifically, the process of generating the stimulation parameters for transcranial electrical stimulation according to the target frequency band, the amplitude and phase information within the target frequency band is as follows: taking 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; taking the average amplitude of the target frequency band 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 the phase parameter of the stimulation signal based on the phase difference of the target channel pair; taking the stimulation frequency, the intensity of the stimulation current, and the phase parameter as the stimulation parameters for transcranial electrical stimulation.

[0105] In some embodiments of the present application, the method further includes: within the preset stimulation duration, when the phase locking value within the target frequency band reaches the set threshold, pausing the transcranial electrical stimulation of the target object; or, within the 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.

[0106] S105, outputting transcranial electrical stimulation according to the stimulation parameters, and collecting the endogenous electroencephalogram signals during the stimulation in real time, so as to dynamically adjust the stimulation parameters to achieve closed-loop regulation of the brain region of the target object.

[0107] In the embodiments of the present application, through a closed-loop feedback mechanism, the effect of each stimulation is fed back to the system, 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 adjustment effect, thereby further promoting the phase synchronization between the prefrontal lobe and the parietal lobe and improving the cognitive collaborative activities of the brain.

[0108] In the embodiments of the present application, transcranial electrical stimulation is performed on a target object using stimulation parameters, and at the same time, the step of continuously receiving and preprocessing endogenous electroencephalogram signals is continued, so as to achieve closed-loop regulation. The closed-loop regulation mechanism allows the system to continuously optimize the stimulation parameters according to the latest electroencephalogram signal feedback to adapt to the changes in the brain state of the target object during the execution of the cognitive task. Through continuous real-time monitoring and optimization of the stimulation parameters, the system can dynamically generate stimulation parameters suitable for different individuals, achieve precise control of the endogenous phase difference, and thus enhance working memory performance.

[0109] The following is an embodiment of the system of the present application, which can be used to execute the method embodiment of the present application. For the details not disclosed in the system embodiment of the present application, please refer to the method embodiment of the present application.

[0110] Please refer to Figure 2 , which shows a schematic structural diagram 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 electroencephalogram acquisition device 108; the intelligent terminal 107 is communicatively connected to the transcranial electrical stimulation device 106, and the multi-channel electroencephalogram acquisition device 108 is electrically connected to the transcranial electrical stimulation device 106; wherein,

[0111] The intelligent terminal 107 is configured to generate an electrical stimulation instruction according to a cognitive task and send it to the transcranial electrical stimulation device 106; the transcranial electrical stimulation device 106 is configured to perform transcranial electrical stimulation on a target object wearing the multi-channel electroencephalogram acquisition device 108 according to the received electrical stimulation instruction; during the transcranial cognitive task of the target object wearing the multi-channel electroencephalogram acquisition device, collect and preprocess the multi-channel endogenous electroencephalogram signals of the target object during the execution of the cognitive task to obtain the electroencephalogram signals to be analyzed; align the electroencephalogram signals to be analyzed with the behavior performance labels generated by the target object during the execution of the cognitive task to obtain associated signals; according to the associated signals, obtain the channel pair with the highest phase synchrony 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 frequency band features to generate stimulation parameters for transcranial electrical stimulation; output transcranial electrical stimulation according to the stimulation parameters, and collect the endogenous electroencephalogram signals during stimulation in real time, so as to dynamically adjust the stimulation parameters, achieve closed-loop regulation of the brain region of the target object, and generate transcranial electrical stimulation information and feedback it to the intelligent terminal 107.

[0112] Specifically, the transcranial electrical stimulation device 106 includes a power supply module 101, an acquisition module 105, a stimulation module 104, a control module 102, and a communication module 103; the power supply module 101 provides the electrical energy required for the operation of the transcranial electrical stimulation device 106. 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 electroencephalogram data of multiple channels.

[0113] Specifically, the 102 control module controls the normal operation of the entire host, and the functions completed include: 1) monitoring the battery margin in the 101 power supply module; 2) through the 103 communication module, completing data communication with the 107 intelligent terminal, including receiving control instructions and query instructions, and sending electroencephalogram data and system status data; 3) generating multi-channel independently controllable current stimulation signals through the 104 stimulation module, and the adjustable parameters include mode, amplitude, frequency, phase, etc.; 4) acquiring multi-channel independently controllable electroencephalogram data through the 105 acquisition module, and the adjustable parameters include channel enabling, sampling rate, gain, etc. The 107 intelligent terminal completes communication with the host by running self-developed application software, including sending instructions and receiving, calculating, storing, and displaying data. The 108 multi-channel electroencephalogram acquisition device is made of a flexible and lightweight material, and is equipped with a detachable integrated electroencephalogram stimulation electrode. By closely attaching to the scalp, it completes the acquisition of electroencephalogram data and applies the stimulation current to the brain.

[0114] In the embodiment of the present application, transcranial electrical stimulation is performed on the target object using stimulation parameters, and at the same time, the step of continuously receiving and preprocessing endogenous electroencephalogram signals is continued, so that closed-loop regulation is realized. The closed-loop regulation mechanism allows the system to continuously optimize the stimulation parameters according to the latest electroencephalogram 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 the stimulation parameters, the system can dynamically generate stimulation parameters suitable for different individuals, realize precise control of the endogenous phase difference, and thus enhance the working memory performance.

[0115] The present application also provides a computer-readable medium, on which program instructions are stored. When the program instructions are executed by a processor, the transcranial electrical stimulation control method for enhancing working memory provided by each of the above method embodiments is implemented.

[0116] The present application also provides a computer program product containing instructions. When it runs on a computer, it causes the computer to execute the transcranial electrical stimulation control method for enhancing working memory provided by each of the above method embodiments.

[0117] Please refer to Figure 3 For a schematic structural diagram of an electronic device provided by an embodiment of the present application. As Figure 3As 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.

[0118] Among them, the communication bus 1002 is used to realize the connection and communication between these components.

[0119] Among them, the user interface 1003 may include a display screen (Display) and a camera (Camera). Optionally, the user interface 1003 may further include a standard wired interface and a wireless interface.

[0120] Among them, the network interface 1004 may optionally include a standard wired interface and a wireless interface (such as a WI-FI interface).

[0121] Among them, the processor 1001 may include one or more processing cores. The processor 1001 connects various parts within the entire electronic device 1000 through various interfaces and lines. By running or executing instructions, programs, code sets, or instruction sets stored in the memory 1005, and by calling data stored in the memory 1005, it executes various functions of the electronic device 1000 and processes data. Optionally, the processor 1001 may be implemented in at least one hardware form of digital signal processing (DSP), field-programmable gate array (FPGA), or programmable logic array (PLA). The processor 1001 may integrate a combination of one or several of a central processing unit (CPU), a graphics processing unit (GPU), and a modem, etc. Among them, the CPU mainly processes the operating system, user interface, and application programs, etc.; the GPU is responsible for the rendering and drawing of the content to be displayed on the display screen; the modem is used to process wireless communication. It can be understood that the above modem may not be integrated into the processor 1001 and may be implemented separately by a single chip.

[0122] Among them, the memory 1005 may include a Random Access Memory (RAM), or may also include a 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. Among them, the program storage area can 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 can store data involved in the above-mentioned various method embodiments. Optionally, the memory 1005 may also be at least one storage system located far from the aforementioned processor 1001. As Figure 3 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 program for enhancing working memory.

[0123] In Figure 3 the electronic device 1000 shown, the user interface 1003 is mainly used to provide an input interface for the user to obtain user input data; while the processor 1001 can be used to call the transcranial electrical stimulation control application program stored in the memory 1005 and specifically perform the following operations:

[0124] During the process of performing a cognitive task on a target object wearing a multi-channel electroencephalogram acquisition device, collect and preprocess the multi-channel endogenous electroencephalogram signals of the target object during the execution of the cognitive task to obtain the electroencephalogram signals to be analyzed;

[0125] Align the electroencephalogram signals to be analyzed with the behavioral performance labels generated by the target object during the execution of the cognitive task to obtain associated signals;

[0126] According to the associated signals, obtain the channel pair with the highest phase synchrony between the prefrontal lobe and the parietal lobe of the target object as the target channel pair;

[0127] Based on the associated signal data of the target channel pair, extract frequency band features to generate stimulation parameters for transcranial electrical stimulation;

[0128] Output transcranial electrical stimulation according to the stimulation parameters, and collect the endogenous electroencephalogram signals during the stimulation in real time, so as to dynamically adjust the stimulation parameters to achieve closed-loop regulation of the brain regions of the target object.

[0129] In one embodiment, when the processor 1001 executes the acquisition and preprocessing of multi-channel endogenous electroencephalogram (EEG) signals of a target object during the execution of a cognitive task, the following operations are specifically performed:

[0130] Acquire multi-channel endogenous EEG signals of the target object during the execution of the cognitive task;

[0131] Use a Butterworth notch filter to remove the power frequency interference of the endogenous EEG signals;

[0132] Perform baseline drift correction on the endogenous EEG signals after removing the power frequency interference;

[0133] Use a Butterworth band-pass filter to perform band-pass filtering on the endogenous EEG signals after baseline drift correction;

[0134] Use independent component analysis technology to identify and remove artifacts from the band-pass filtered endogenous EEG signals.

[0135] In one embodiment, when the processor 1001 executes the alignment process of aligning the EEG signals to be analyzed with the behavior performance labels of the target object during the cognitive task process, the following operations are specifically performed:

[0136] Divide the EEG signals to be analyzed according to a preset period to obtain EEG data for multiple periods;

[0137] Generate behavior performance labels for the target object for each period through the behavior performance of the target object during the execution of the cognitive task;

[0138] Associate the EEG data for each period with the behavior performance labels of the target object for each period to obtain a first mapping relationship between multiple EEG data and behavior performance labels;

[0139] Judge whether the behavior performance labels in each first mapping relationship meet the preset conditions, and delete the first mapping relationships to which the behavior performance labels that do not meet the preset conditions belong to obtain multiple second mapping relationships;

[0140] Perform baseline correction on the EEG data in each second mapping relationship to remove the second mapping relationships to which the EEG data belonging to chronic drift belong, to obtain multiple third mapping relationships;

[0141] Perform superposition and averaging processing on the EEG data corresponding to the same behavior performance labels in multiple third mapping relationships to obtain EEG signals that meet the preset conditions.

[0142] In one embodiment, when the processor 1001 executes the operation of obtaining the channel pair with the highest phase synchrony between the prefrontal lobe and the parietal lobe of the target object as the target channel pair according to the correlation signal, the following operations are specifically performed:

[0143] Determine the prefrontal electrode channels reflecting decision-making and emotion regulation functions and the parietal electrode channels related to attention regulation and sensory information integration;

[0144] Select the association signals to be processed corresponding to the prefrontal electrode channels and the parietal electrode channels from the association signals;

[0145] Determine the signals in the target frequency bands closely related to working memory from the signals to be processed. The target frequency bands include the theta band of 4 - 8 Hz and the alpha band of 8 - 13 Hz;

[0146] Extract the phase information in the signals in the target frequency bands through Hilbert transform;

[0147] Determine the channel pair with the highest phase synchrony between the prefrontal lobe and the parietal lobe of the target object as the target channel pair according to the phase information.

[0148] In one embodiment, when the processor 1001 executes to determine the channel pair with the highest phase synchrony between the prefrontal lobe and the parietal lobe of the target object as the target channel pair according to the phase information, it specifically performs the following operations:

[0149] Calculate the phase locking value between each prefrontal channel and the parietal channel pair according to the extracted phase information;

[0150] Construct a phase synchrony matrix based on the phase locking values;

[0151] Screen out the channel pair with the highest phase locking value in the phase synchrony matrix;

[0152] Take the channel pair with the highest phase locking value as the channel pair with the highest phase synchrony between the prefrontal lobe and the parietal lobe of the target object;

[0153] Take the channel pair with the highest phase synchrony as the target channel pair.

[0154] In one embodiment, when the processor 1001 executes to extract frequency band features from the association signal data of the target channel pair to generate the stimulation parameters of transcranial electrical stimulation, it specifically performs the following operations:

[0155] Perform frequency band separation on the electroencephalogram signals of the target channel pair, and extract the signals in the theta band and the alpha band respectively through a band-pass filter or wavelet transform;

[0156] Calculate the phase locking values of the target channel pair in the theta frequency and the alpha band;

[0157] Compare the phase locking values of the theta band and the alpha band, and take the frequency band corresponding to the larger phase locking value as the target frequency band;

[0158] For the target frequency band, the time-domain signal is converted into a frequency-domain signal through fast Fourier transform to obtain the amplitude and phase information within the target frequency band;

[0159] Generate transcranial electrical stimulation (tES) stimulation parameters based on the target frequency band, the amplitude and phase information within the target frequency band.

[0160] In one embodiment, when the processor 1001 executes to generate the tES stimulation parameters based on the target frequency band, the amplitude and phase information within the target frequency band, the following operations are specifically performed:

[0161] Use the frequency of the target frequency band as the stimulation frequency;

[0162] Calculate the average amplitude of the target frequency band according to the amplitude within the target frequency band;

[0163] Use the average amplitude of the target frequency band as the intensity of the stimulation current;

[0164] Calculate the phase difference of the target channel pair according to the phase information of the target frequency band;

[0165] Set the phase parameter of the stimulation signal based on the phase difference of the target channel pair;

[0166] Use the stimulation frequency, the intensity of the stimulation current, and the phase parameter as the tES stimulation parameters.

[0167] In one embodiment, the processor 1001 also performs the following operations:

[0168] During the pre-set stimulation duration, when the phase-locking value within the target frequency band reaches the set threshold, suspend the tES of the target object; or,

[0169] During the pre-set stimulation duration, when the phase-locking value within the target frequency band does not reach the set threshold, resume the tES of the target object.

[0170] In the embodiments of the present application, the target object is subjected to tES using the stimulation parameters, and at the same time, the step of continuously receiving and preprocessing the endogenous electroencephalogram (EEG) signal is continued, so that closed-loop regulation is achieved. The closed-loop regulation mechanism allows the system to continuously optimize the stimulation parameters according to the feedback of the latest EEG signal to adapt to the changes in the brain state of the target object during the execution of the cognitive task. Through continuous real-time monitoring and optimization of the stimulation parameters, the system can dynamically generate stimulation parameters suitable for different individuals, achieve precise control of the endogenous phase difference, and thus enhance working memory performance.

[0171] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing relevant hardware through a computer program. The program for transcranial electrical stimulation control for enhancing working memory can be stored in a computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above methods. Among them, the storage medium of the program for transcranial electrical stimulation control for enhancing working memory can be a magnetic disk, an optical disk, a read-only memory, or a random access memory, etc.

[0172] The above-disclosed are only the preferred embodiments of the present application. Of course, the scope of rights of the present application cannot be limited thereby. Therefore, equivalent changes made according to the claims of the present application still fall within the scope covered by the present application.

Claims

1. A transcranial electrical stimulation control system for enhancing working memory, characterized in that, The system includes: An intelligent terminal, a transcranial electrical stimulation device, and a multi-channel electroencephalogram acquisition device; the intelligent terminal is communicatively connected to the transcranial electrical stimulation device, and the multi-channel electroencephalogram acquisition device is electrically connected to the transcranial electrical stimulation device; wherein, The intelligent terminal is configured to generate an electrical stimulation instruction according to a cognitive task and send it to the transcranial electrical stimulation device; The transcranial electrical stimulation device is configured to perform transcranial electrical stimulation on a target object wearing the multi-channel electroencephalogram acquisition device according to the received electrical stimulation instruction; during the process of the target object wearing the multi-channel electroencephalogram acquisition device performing a cognitive task, collect and preprocess the multi-channel endogenous electroencephalogram signals of the target object during the performance of the cognitive task to obtain electroencephalogram signals to be analyzed; align the electroencephalogram signals to be analyzed with the behavior performance labels generated by the target object during the performance of the cognitive task to obtain associated signals; according to the associated signals, obtain the channel pair with the highest phase synchrony 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 frequency band features to generate stimulation parameters for transcranial electrical stimulation; output transcranial electrical stimulation according to the stimulation parameters, and collect the endogenous electroencephalogram signals during stimulation in real time, so as to dynamically adjust the stimulation parameters, realize closed-loop regulation of the brain region of the target object, and generate transcranial electrical stimulation information and feedback it to the intelligent terminal.

2. The system according to claim 1, characterized in that, The collecting and preprocessing the multi-channel endogenous electroencephalogram signals of the target object during the performance of the cognitive task includes: Collecting the multi-channel endogenous electroencephalogram signals of the target object during the performance of the cognitive task; Using a Butterworth notch filter to remove the power frequency interference of the endogenous electroencephalogram signals; Performing baseline drift correction on the endogenous electroencephalogram signals after removing the power frequency interference; Using a Butterworth band-pass filter to perform band-pass filtering on the endogenous electroencephalogram signals after baseline drift correction; Using independent component analysis technology to identify and remove artifacts from the band-pass filtered endogenous electroencephalogram signals.

3. The system according to claim 1, wherein Aligning the electroencephalogram signals to be analyzed with the behavior performance labels generated by the target object during the performance of the cognitive task to obtain associated signals includes: Dividing the electroencephalogram signals to be analyzed according to a preset period to obtain electroencephalogram data of multiple periods; Generating the behavior performance labels of the target object for each period through the behavior performance of the target object during the performance of the cognitive task; Associating the electroencephalogram data of each period with the behavior performance labels of the target object for each period to obtain a first mapping relationship between multiple electroencephalogram data and behavior performance labels; Judging whether the behavior performance labels in each first mapping relationship meet a preset condition, and deleting the first mapping relationship to which the behavior performance labels that do not meet the preset condition belong to obtain multiple second mapping relationships; Performing baseline correction on the electroencephalogram data in each second mapping relationship to remove the second mapping relationship to which the electroencephalogram data belonging to chronic drift belongs to obtain multiple third mapping relationships; Superimpose and average the EEG data corresponding to the same behavioral performance labels in the multiple third mapping relationships to obtain an EEG signal that meets the preset conditions.

4. The system according to claim 1, wherein The obtaining of the channel pair with the highest phase synchrony between the prefrontal lobe and the parietal lobe of the target object according to the correlation signal includes: Determine the prefrontal lobe electrode channels that reflect decision-making and emotion regulation functions and the parietal lobe electrode channels related to attention regulation and sensory information integration; Select the correlation signals to be processed corresponding to the prefrontal lobe electrode channels and the parietal lobe electrode channels from the correlation signals; Determine the signals in the target frequency bands that are closely related to working memory from the correlation signals to be processed, where the target frequency bands include the theta band of 4 - 8 Hz and the alpha band of 8 - 13 Hz; Extract the phase information in the signals in the target frequency bands through Hilbert transform; Determine the channel pair with the highest phase synchrony between the prefrontal lobe and the parietal lobe of the target object as the target channel pair according to the phase information.

5. The system according to claim 4, wherein The determining of the channel pair with the highest phase synchrony between the prefrontal lobe and the parietal lobe of the target object as the target channel pair according to the phase information includes: Calculate the phase locking value between each prefrontal lobe channel and parietal lobe channel pair according to the extracted phase information; Construct a phase synchrony matrix based on the phase locking values; In the phase synchrony matrix, screen out the channel pair with the highest phase locking value; Take the channel pair with the highest phase locking value as the channel pair with the highest phase synchrony between the prefrontal lobe and the parietal lobe of the target object; Take the channel pair with the highest phase synchrony as the target channel pair.

6. The system according to claim 5, wherein The calculation formula of the phase locking value is: Among them, PLV is the phase-locking value, N is the number of signal samples in the target frequency band, and △Φ j represents the phase difference between two signals at the j-th sample point, that is, the instantaneous phase Θ1(t i ) of the prefrontal channel and the instantaneous phase Θ2(t j ) of the parietal channel at the j-th sample point, is a complex exponential, i is the imaginary unit, the complex exponential represents the phase difference between two signals at the j-th sample point, and this phase difference is represented by a vector in the complex plane, represents the accumulation of all complex exponentials within N sample points, t i is the i-th time point, t j is the j-th time point, |.| represents the magnitude of a complex number, and e represents the mathematical constant of the complex exponential.

7. The system according to claim 1, characterized in that The extracting of frequency band characteristics based on the correlation signal data of the target channel pair to generate the stimulation parameters of transcranial electrical stimulation includes: Perform frequency band separation on the EEG signals of the target channel pair, and extract the signals of the theta band and the alpha band respectively through a band-pass filter or wavelet transform; Calculate the phase locking values of the target channel pair in the theta frequency and the alpha band; Compare the phase locking values of the theta band and the alpha band, and take the frequency band corresponding to the larger phase locking value as the target frequency band; For the target frequency band, convert the time-domain signal to a frequency-domain signal through fast Fourier transform to obtain the amplitude and phase information within the target frequency band; Generate the stimulation parameters of transcranial electrical stimulation according to the target frequency band, the amplitude and phase information within the target frequency band.

8. The system according to claim 7, wherein The generating of the stimulation parameters of transcranial electrical stimulation according to the target frequency band, the amplitude and phase information within the target frequency band includes: Take the frequency of the target frequency band as the stimulation frequency; Calculate the average amplitude of the target frequency band according to the amplitude within the target frequency band; Take the average amplitude of the target frequency band as the intensity of the stimulation current; Calculate the phase difference of the target channel pair according to the phase information of the target frequency band; Set the phase parameter of the stimulation signal based on the phase difference of the target channel pair; Take the stimulation frequency, the intensity of the stimulation current, and the phase parameter as the stimulation parameters for transcranial electrical stimulation.

9. The system according to claim 7, characterized in that The system is also specifically configured to: During a preset stimulation duration, when the phase locking value within the target frequency band reaches a set threshold, pause transcranial electrical stimulation of the target object; or, During a preset stimulation duration, when the phase locking value within the target frequency band does not reach the set threshold, resume transcranial electrical stimulation of the target object.

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