Multi-channel transcranial direct current stimulation cognitive function evaluation method and system

Abstract

The invention provides a multichannel transcranial direct current stimulation cognitive function assessment method and system, and belongs to the field of neuroscience and brain cognitive function assessment. The method comprises the steps that electroencephalogram signal data of a subject under multi-channel transcranial direct current stimulation are collected and preprocessed; respectively performing micro-state analysis, brain function connectivity analysis and oscillation power analysis on the preprocessed data; constructing a linear mixed effect model, and inputting the results of the micro-state analysis, the brain function connectivity analysis and the oscillation power analysis into the model for quantitative evaluation of the cognitive function state; and a time sequence database is constructed, and the time sequence database is used for storing cross-time-point analysis results and analyzing the long-term change trend of the cognitive function based on a statistical model. Dynamic, accurate and high-temporal-spatial-resolution evaluation of the cognitive state of the subject is achieved, the long-term change trend of the cognitive state is dynamically monitored, and technical support is provided for early warning and rehabilitation effect tracking of neurodegenerative diseases.

Description

Technical Field

[0001] The present invention belongs to the field of neuroscience and brain cognitive function assessment, and in particular relates to a multi-channel transcranial direct current stimulation cognitive function assessment method and system. Background Art

[0002] The statements in this section merely provide background information related to the present invention and do not necessarily constitute prior art.

[0003] With the rapid development of modern society, people are increasingly concerned about cognitive function. Accurate and efficient assessment of individual cognitive status has become an urgent need, especially in the context of an aging population and the high incidence of neurological diseases. Traditional cognitive function assessment methods rely on questionnaires, behavioral tests, and simple EEG signal monitoring. These methods have significant shortcomings, such as high subjectivity, high time costs, a single assessment dimension, and difficulty in dynamically capturing cognitive changes. These methods have severely restricted clinical application and in-depth scientific research.

[0004] To address these issues, a number of innovative patents combining EEG signals and brain stimulation technologies have emerged in recent years. Patent publication number CN119740014A discloses a noise reduction and feature extraction system for smart glasses based on multi-channel EEG signals. This solution leverages a multi-channel design to achieve multi-point control of brain networks. However, the complex noise reduction algorithm and high-computing hardware requirements result in high device power consumption and difficulty in real-time processing, limiting its widespread application in wearable scenarios. Another patent, publication number CN117786600A, proposes a cognitive function assessment platform that integrates multimodal data with reinforcement learning. This platform uses an active learning model to screen high-value data samples and leverages reinforcement learning to dynamically optimize deep learning network parameters, enabling accurate classification of cognitive states and anomaly warnings. Although multimodal data fusion significantly improves assessment sensitivity, expensive synchronous acquisition equipment and complex data calibration processes drive up clinical deployment costs, and there remains a technical gap in the ability to capture millisecond-level dynamic spatiotemporal features, such as EEG microstates.

[0005] At the same time, existing technologies mostly focus on single or short-term cognitive status assessments and lack the ability to track the same subjects longitudinally across time points. Since the evolution of cognitive dysfunction (such as Alzheimer's disease) is gradual, short-term assessments are difficult to capture its dynamic development patterns. In addition, existing systems have technical gaps in data storage, feature alignment across time points, and long-term trend modeling, and are unable to effectively support the dynamic optimization of personalized intervention plans. A cognitive function assessment system that integrates multi-channel precision brain stimulation, dynamic division of EEG microstates, and brain network connectivity analysis has not yet been formed, making it difficult to meet the actual needs of early clinical diagnosis, cognitive intervention, and large-scale health monitoring. Summary of the Invention

[0006] To overcome the shortcomings of the above-mentioned existing technologies, the present invention provides a multi-channel transcranial direct current stimulation cognitive function assessment method and system. Through high-density EEG synchronous acquisition and multi-parameter comprehensive analysis, it effectively improves the temporal and spatial resolution and real-time performance of cognitive assessment, provides key technical support for cognitive impairment screening, personalized treatment and brain-computer interface applications, and promotes the development of smart medical care, brain science research and public health management.

[0007] To achieve the above objectives, one or more embodiments of the present invention provide the following technical solutions: A first aspect of the present invention provides a method for evaluating cognitive function through multi-channel transcranial direct current stimulation; A multi-channel transcranial direct current stimulation cognitive function assessment method comprising: Collect the EEG signal data of the subjects under multi-channel transcranial direct current stimulation and perform preprocessing; The preprocessed EEG signal data were subjected to microstate analysis, brain functional connectivity analysis, and oscillation power analysis; A linear mixed-effects model was constructed and the results of microstate analysis, brain functional connectivity analysis, and oscillation power analysis were input into the linear mixed-effects model to quantitatively evaluate the cognitive function status; A time series database is constructed to store the results of microstate analysis, brain functional connectivity analysis, and oscillation power analysis across time points, and to analyze long-term trends in cognitive function based on statistical models.

[0008] As a further technical solution, the preprocessing includes: performing bandpass filtering and notch filtering on the collected EEG signal data to remove clutter interference; using wavelet denoising to reduce high-frequency electromyographic artifacts in the EEG signal data; and using independent component analysis to eliminate artifacts generated by non-EEG activities.

[0009] As a further technical solution, microstate analysis is performed on the preprocessed EEG signal data, including: The preprocessed EEG signal data is decomposed into spatiotemporal patterns using a microstate partitioning algorithm. The continuous EEG signal is represented as a set of stable microstates of a finite category. The improved K-means clustering algorithm is used to cluster and set the microstate categories.

[0010] As a further technical solution, the process of performing brain functional connectivity analysis on the preprocessed EEG signal data is as follows: Brain functional connectivity analysis is performed on the EEG signal data by calculating key connectivity indices of the EEG signal data; wherein the key connectivity indices include: Granger causality, weighted phase lag index and maximum information coefficient.

[0011] As a further technical solution, oscillation power analysis is performed on the pre-processed EEG signal data, including: The Welch method is used to estimate the power spectrum of the preprocessed EEG data to obtain the power spectrum density of different frequency bands; as shown in the following formula:

[0012] in, is the power spectrum density, L is the number of segments, N is the length of each segment, w[n] is the windowing function, For the segment signal, is the window function power normalization factor; Based on the power spectral density, the oscillation power of each frequency band in the target frequency band is calculated, which is defined as the integral of the power spectral density in the corresponding frequency range:

[0013] in, is the oscillation power in a specific frequency band.

[0014] As a further technical solution, the process of constructing a linear mixed-effects model and inputting the results of microstate analysis, brain functional connectivity analysis, and oscillation power analysis into the linear mixed-effects model for quantitative evaluation of cognitive function status includes: Fitting tests were performed on the parameter values ​​before and after stimulation and in different task states, including fixed-effect and random-effect tests, to test the statistical significance of the differences between groups. The parameter values ​​were set as the dependent variables, and the pre- and post-stimulation conditions, task state, and time were set as fixed-effect factors, and possible interactions between them were considered. Participants were set as random-effect factors to explain individual differences, as shown in the following formula:

[0015] in, is the parameter value of the i-th subject in the j-th observation; is a fixed effect term, For the The random effect of participants, is the residual term, and .

[0016] As a further technical solution, the process of constructing a time series database for storing the results of microstate analysis, brain functional connectivity analysis, and oscillation power analysis across time points, and analyzing the long-term trend of cognitive function based on a statistical model is as follows: Performing feature alignment and cross-period comparison on the results of microstate analysis, brain functional connectivity analysis, and oscillation power analysis at different time points stored in the time series database; A statistical model was constructed and a time factor was introduced into the statistical model to analyze the long-term change trend of cognitive function indicators and the sustainability of the intervention effect.

[0017] A second aspect of the present invention provides a multi-channel transcranial direct current stimulation cognitive function assessment system.

[0018] A multi-channel transcranial direct current stimulation cognitive function assessment system, comprising: The EEG signal data acquisition module is configured to: acquire EEG signal data of the subject under multi-channel transcranial direct current stimulation and perform preprocessing; The data analysis module is configured to perform microstate analysis, brain functional connectivity analysis, and oscillation power analysis on the preprocessed EEG signal data; The quantitative evaluation module is configured to: construct a linear mixed-effect model, input the results of microstate analysis, brain functional connectivity analysis, and oscillation power analysis into the linear mixed-effect model to perform a quantitative evaluation of cognitive function status; The longitudinal tracking evaluation module is configured to: construct a time series database, which is used to store the results of microstate analysis, brain functional connectivity analysis and oscillation power analysis across time points, and analyze the long-term change trend of cognitive function based on statistical models.

[0019] The third aspect of the present invention provides a computer-readable storage medium having a program stored thereon, which, when executed by a processor, implements the steps of the multi-channel transcranial direct current stimulation cognitive function assessment method as described in the first aspect of the present invention.

[0020] The fourth aspect of the present invention provides an electronic device, comprising a memory, a processor, and a program stored in the memory and runnable on the processor. When the processor executes the program, the steps in the multi-channel transcranial direct current stimulation cognitive function assessment method as described in the first aspect of the present invention are implemented.

[0021] One or more of the above technical solutions have the following beneficial effects: (1) The present invention collects high-density EEG signals after stimulation and combines them with multi-dimensional comprehensive evaluation methods such as microstate analysis, brain functional connectivity analysis and oscillation power analysis to achieve dynamic, accurate and high spatiotemporal resolution evaluation of the subject's cognitive state. Among them, EEG microstates depict the discrete representation of instantaneous functional states from the spatial topological dimension, brain functional connectivity indicators reveal the network space interaction mechanism during state transition, and oscillation power analysis provides the frequency-specific energy basis of neural activity. This cross-dimensional integration can not only capture the dynamic trajectory of microstate temporal transitions, but also analyze the strength and pattern of functional connections within the state, as well as the contribution of oscillation activities in different frequency bands to state maintenance and transition, thereby more comprehensively revealing the spatiotemporal and frequency dynamic characteristics of neural electrical activity in the cognitive process, and providing multi-level electrophysiological evidence for the accurate evaluation of cognitive state changes. Furthermore, by constructing a linear mixed effects model (LMM) to integrate multi-condition and multi-time point data, the robustness of statistical analysis is improved, which is suitable for cognitive function research with large individual differences.

[0022] (2) The present invention constructs a time series database to store the results of microstate analysis, brain functional connectivity analysis, and oscillation power analysis across time points, and analyzes the long-term trend of cognitive function based on statistical models, thereby achieving longitudinal tracking and evaluation of the cognitive function of subjects. It can dynamically monitor the long-term trend of cognitive status and provide technical support for early warning of neurodegenerative diseases and tracking of rehabilitation effects.

[0023] Advantages of additional aspects of the present invention will be given in part in the following description and in part will be obvious from the following description, or will be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] The accompanying drawings, which constitute a part of the present invention, are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute improper limitations on the present invention.

[0025] Figure 1 This is a flow chart of the method of the first embodiment.

[0026] Figure 2 This is a system structure diagram of the second embodiment. DETAILED DESCRIPTION

[0027] It should be noted that the following detailed descriptions are exemplary and intended to provide further explanation of the present invention. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which the present invention belongs.

[0028] It should be noted that the terms used herein are for describing particular embodiments only and are not intended to limit the exemplary embodiments according to the present invention.

[0029] In the absence of conflict, the embodiments of the present invention and the features thereof may be combined with each other.

[0030] Example 1 This embodiment discloses a method for evaluating cognitive function through multi-channel transcranial direct current stimulation; like Figure 1 As shown, a multi-channel transcranial direct current stimulation cognitive function assessment method comprises: Step S1, collecting EEG signal data of the subject under multi-channel transcranial direct current stimulation and performing preprocessing; Step S2, performing microstate analysis, brain functional connectivity analysis, and oscillation power analysis on the preprocessed EEG signal data; Step S3, constructing a linear mixed-effects model, inputting the results of microstate analysis, brain functional connectivity analysis, and oscillation power analysis into the linear mixed-effects model to perform a quantitative assessment of cognitive function status; Step S4: construct a time series database, which is used to store the results of microstate analysis, brain functional connectivity analysis, and oscillation power analysis across time points, and analyze the long-term change trend of cognitive function based on a statistical model.

[0031] In this example, an experimental paradigm was designed that included a resting state and two task states. Specifically, the resting state refers to first collecting the subject's resting EEG signal data as baseline data before applying stimulation. Based on this baseline data, an initial assessment was performed to establish an individualized cognitive function baseline.

[0032] The two task-based experimental paradigms are the reaction task and the memory task. The reaction task tests the subject's attention and reaction speed by presenting visual stimuli (e.g., the color patch test) and recording EEG signals. The memory task, an N-back test, examines the maintenance and updating of working memory. Both task-based experimental paradigms are conducted before and after stimulation.

[0033] Furthermore, in step S1, a multi-channel transcranial direct current stimulation (m-tDCS) hardware design was employed. Conventional single-channel tDCS stimulation is limited in stimulation positioning and intensity control, making it difficult to precisely regulate brain functional networks. The m-tDCS approach, through multi-electrode combined stimulation, can enhance targeting and individual regulation, providing a technical foundation for cognitive function intervention and assessment. Specifically, the m-tDCS anode electrode was positioned over the left dorsolateral prefrontal cortex (DLPFC), and the cathode electrode was placed in the temporal lobe to ensure that stimulation targeted key cognitive areas. The stimulation intensity was set to 0.5 mA, and the duration was 20 minutes, with a smooth, gradual increase and slow decrease in current to enhance the safety and comfort of the stimulation.

[0034] During EEG data acquisition, a 32-lead EEG cap was used for multi-channel acquisition. Electrode placement was based on the standard 10-20 system, and the sampling rate was set to 1000 Hz to ensure high temporal and spatial resolution of the data. During acquisition, scalp electrode impedance was strictly controlled to remain below 10 kΩ to ensure signal quality. The system's hardware synchronization control module enables precise coordination of stimulation and EEG acquisition, ensuring high consistency between the timing of current stimulation and EEG data acquisition, thereby guaranteeing the accuracy and reliability of subsequent data analysis.

[0035] Furthermore, in step S1, the collected EEG signal data is preprocessed. This preprocessing process includes: first, bandpass filtering the collected EEG signal data to remove frequency components below 0.5 Hz and above 45 Hz. A notch filter is also used to remove the AC interference frequency of 50 Hz to ensure the integrity of the main EEG frequency band in the signal. Secondly, after filtering, the filtered data is downsampled to 200 Hz to balance computational efficiency and signal resolution. Subsequently, wavelet denoising is applied to further reduce high-frequency EEG artifacts, and independent component analysis (ICA) is used to remove artifacts caused by non-EEG activities such as eye movements and electrocardiograms, thereby obtaining a relatively pure EEG signal for subsequent analysis.

[0036] Step S2: performing microstate analysis, brain functional connectivity analysis, and oscillation power analysis on the preprocessed EEG signal data.

[0037] In step S21, EEG microstates, short, stable spatial potential topological patterns in EEG signals lasting approximately 60-120 milliseconds, serve as electrophysiological markers of dynamic changes in brain functional networks. Microstate analysis can capture transient brain functional state transitions during cognitive processes and is widely used to monitor brain information processing and abnormalities.

[0038] In this embodiment, a microstate partitioning algorithm is used to decompose the spatiotemporal patterns of EEG signals, and continuous EEG signals are represented as a set of stable microstates of a finite category. Clustering is performed using a clustering algorithm and the number of microstate categories is set to K=4.

[0039] Furthermore, by integrating the experimental data of the subjects over a period of time, the mean of the characteristic parameters of each data acquisition stage (including the resting state before stimulation, the resting state after stimulation, the paradigm task before stimulation, and the paradigm task after stimulation) was calculated. Subsequently, cross-subject data were normalized at the group level to eliminate differences in the amplitude and baseline of the EEG signals between individuals, making the data of different subjects comparable, thereby accurately reflecting the EEG microstate characteristics at the group level. The EEG topography at the peak of the global field power (GFP) of all subjects was further extracted as the clustering input, and the instantaneous topography with a high signal-to-noise ratio was selected by using the characteristic of GFP reflecting the spatial complexity of the EEG signal, as shown in the following formula:

[0040] in, For the The potential value recorded by each electrode, is the average value of all electrode potentials, The total number of electrodes involved in the calculation.

[0041] An improved K-means clustering algorithm was used for unsupervised classification of multidimensional EEG maps, with the number of clusters set to k=4. The dissimilarity between data points and microstate templates was measured by calculating orthogonal distances. The covariance matrix of the data points was calculated for each category in an iterative process, and the eigenvector corresponding to the largest eigenvalue was extracted as the new cluster centroid. The algorithm was iterated until the cluster centroid stabilized. The microstate template is the most typical multidimensional EEG map pattern representing each cluster center during the clustering (classification) process. Given sufficient data and no interference, the EEG maps generated from all EEG data at all times are typically clustered into the four most similar templates, typically represented by a / b / c / d, representing the microstate categories.

[0042] Specifically, the improved K-means clustering algorithm divides EEG signals into EEG microstates based on key microstate indicators. The key microstate indicators include: (1) Time coverage: the ratio of the time when the target state actually appears to the total time during the statistical period, reflecting the coverage of the state in the time dimension; (2) Mean durations: the average duration of all target states, which measures the typical level of state maintenance time; (3) Global Explained Variance: The ability of the model or indicator to explain the overall data variation of the system. The higher the value, the better the fitting effect of the data fluctuation. (4) Occurrence per second: the average number of times the target state appears per second, reflecting the frequency of the state in time; (5) Mean correlation: the average value of the linear correlation coefficients between multiple state variables, reflecting the average correlation strength and direction between states.

[0043] After clustering (classifying) all microstates, four templates are generated. The EEG data is then reverse-mapped (i.e., which of the microstate templates a / b / c / d the EEG topography at a given moment most resembles). This allows the EEG data to be classified and specific microstate indicators calculated for subsequent evaluation.

[0044] Furthermore, to establish a mapping relationship between clustering patterns and experimental conditions, the back-projection method was used to remap the group-level clustering results to the individual original data space. By calculating the spatial correlation coefficient between the EEG topography and the clustering template at each time point, the template category with the largest correlation coefficient was selected as the membership label at that time point. Then, the frequency, duration, and other parameters of each microstate of each subject under different experimental conditions were statistically analyzed to form a cluster membership feature vector for subsequent dynamic difference analysis, as shown in the following formula:

[0045] in, For individuals at all times The EEG topographic map vector, For the group clustering template vectors, represents the Euclidean distance, is the EEG topographic map vector corresponding to time point t The transpose of .

[0046] In step S22, brain functional connectivity (BFC) reveals the dynamic interaction patterns of brain functional networks by quantifying the temporal correlation of neural activity across different brain regions. This metric, based on analytical methods such as coherence, cross-correlation, and Granger causality, extracts the strength and directional characteristics of functional connectivity between brain nodes from resting-state and task-state EEG signals, reflecting the efficiency and organization of information transmission within brain networks. For example, during working memory tasks, changes in the strength of functional connectivity between the prefrontal and parietal regions can characterize the neural mechanisms underlying information maintenance and integration.

[0047] Furthermore, brain functional connectivity indicators include weighted phase lag index (wPLI), Granger causality (GC) and maximum information coefficient (MIC), which are used to reflect the dynamic changes of functional coupling and information flow between brain regions.

[0048] In this example, during brain functional connectivity analysis, the MNE-Connectivity library was used to calculate multiple key connectivity metrics to comprehensively assess the dynamic functional interactions between brain regions. These metrics include Granger Causality (GC), Weighted Phase Lag Index (wPLI), and Maximum Information Coefficient (MIC), which reflect the dynamic changes in functional coupling and information flow between brain regions.

[0049] Granger causality (GC) describes whether the activity of one brain region can predict the future activity of another brain region and the directionality of information flow in cognitive regulation, as shown in the following formula:

[0050] Where s is the starting electrode variable, t is the target electrode variable, H is the spectral transfer function, Σ is the residual matrix of the autoregressive model, and S is the result of Σ transformed by H; is the determinant of the S matrix corresponding to the target electrode t; is the spectral transfer function from the starting electrode s to the target electrode t; is the residual matrix of the autoregressive model of the starting electrode s when the target electrode t is given; for The conjugate transpose of .

[0051] The weighted phase lag index (wPLI) is insensitive to volume conduction effects and can more reliably measure the true connectivity between brain regions based on phase synchronization. It is used to quantify the phase synchronization between signals after removing the volume conduction effect, as shown in the following formula:

[0052] Among them, E[·] represents the expected operation on the variable, Indicates taking the imaginary part of the variable, represents the cross-spectral density of the signals of the starting electrode and the target electrode.

[0053] The Maximum Information Coefficient (MIC) is used to capture the linear and nonlinear statistical dependencies between signals, as shown in the following formula:

[0054] Among them, E is the imaginary part of the cross-spectral density matrix after transformation between the starting electrode variable and the target electrode variable, α and β are the eigenvectors of the starting electrode variable and the target electrode variable, respectively. The maximum value is obtained to satisfy the maximum extraction of the coherence imaginary coupling strength between the starting electrode variable and the target electrode variable.

[0055] In step S23, the oscillation power analysis focuses on different frequency bands of the EEG signal (e.g. These oscillatory activities are closely related to specific cognitive functions. Increased wave power is often associated with resting or sleeping states. The change of wave power reflects the allocation of attention resources. Increased wave power may correspond to information processing and neural synchronization. Time-frequency analysis techniques can dynamically capture the transient changes in oscillation power in each frequency band during cognitive tasks, providing a quantitative basis for the energy metabolism and functional status of neural activity.

[0056] Furthermore, oscillation power analysis quantifies the intensity of neural activity by calculating the power spectral density (PSD) of EEG signals within different frequency bands. The Welch method is used to estimate the power spectrum of preprocessed EEG data to reduce the estimated variance and obtain smoother spectral characteristics. Oscillatory power quantification reflects the activity of neural regions in specific frequency bands and is an important indicator for evaluating cognitive function and the effectiveness of neural regulation.

[0057] Assuming the collected EEG signal is x(t), its discrete time series is represented as x[n]. The core steps of the Welch method include segmenting the signal, calculating the periodogram of each segment, and averaging them. The specific PSD calculation formula is:

[0058] Where L is the number of segments, N is the length of each segment, w[n] is the windowing function, is the first segment signal, is the window function power normalization factor.

[0059] Based on PSD, the target frequency band is calculated as follows (0.5–4 Hz), (4–8 Hz), (8–13 Hz), (13–30 Hz), The oscillation power in each frequency band (30–45 Hz) is defined as the integral of the PSD in the corresponding frequency range:

[0060] in, is the oscillation power in a specific frequency band.

[0061] Step S3: construct a linear mixed-effects model and input the results of microstate analysis, brain functional connectivity analysis, and oscillation power analysis into the linear mixed-effects model to quantitatively evaluate the cognitive function state.

[0062] During the statistical analysis of EEG microstates, analysis of variance (ANOVA) was first used to conduct preliminary exploratory analyses and descriptive comparisons before and after stimulation and between task states (resting state, paradigm 1, and paradigm 2). Categorical variables, such as transition probabilities, were statistically analyzed using paired chi-square (McNemar) tests. Specifically, ANOVA decomposed the total variance (the variance between all observations in an ANOVA, reflecting the overall degree of dispersion of the data, which can be decomposed into multiple components, such as between-group variance and within-group variance, based on the source of variance. This decomposition allows for the investigation of the influence of different factors on data variation) into between-group variance and within-group variance, calculating the corresponding mean squares. The F test was then used to compare the ratios of the between-group to within-group mean squares to determine the statistical significance of the differences in the mean values ​​before and after stimulation and between task states.

[0063] In further analysis, a linear mixed effects model (LMM) was constructed to fit the parameter values ​​before and after stimulation and in different task states, including fixed effect and random effect tests, to test the statistical significance of the differences between groups.

[0064] During model construction, stimulation condition (pre- and post-stimulation) and task status were incorporated as fixed effects. The fixed-effect term encompassed the main effects and interactions of each factor, quantifying the average impact of different experimental conditions on the parameter value. Furthermore, subject was included as a random effect, with a random intercept or random slope (if there were trend differences in repeated measurements within individuals) set to capture interindividual variability and control for noise caused by non-experimental factors. The model was fitted using maximum likelihood estimation (ML) to test the significance of the fixed effects. The likelihood ratio test (LRT) was used to infer the significance of the fixed effects.

[0065] Specifically, we set the parameter value as the dependent variable, the pre- and post-stimulation conditions, task state, and time as fixed-effect factors, and considered possible interactions between them; participants were used as random-effect factors to account for individual differences. The significance level of the statistical test was set at p = 0.05. This is as follows:

[0066] in, is the parameter value of the i-th subject in the j-th observation, is a fixed effect term, For the The random effect of participants, is the residual term, and .

[0067] Step S4: construct a time series database, which is used to store the results of microstate analysis, brain functional connectivity analysis, and oscillation power analysis across time points, and analyze the long-term change trend of cognitive function based on a statistical model.

[0068] Performing feature alignment and cross-period comparison on the results of microstate analysis, brain functional connectivity analysis, and oscillation power analysis at different time points stored in the time series database; When performing cross-period comparative analysis in a time series database, data checking and calibration are required for each individual's microstate analysis, brain functional connectivity analysis, and oscillation power analysis results at different time points. Data checking requires ensuring consistency in indicator definitions, electrode positioning, and preprocessing procedures at each time point. Data calibration requires specific analysis based on the specific experiment: (1) Based on the microstate analysis results, the microstate category sequence, average duration, occurrence frequency, and transition probability matrix at each time point are extracted, and the dynamic time warping algorithm is used to align the microstate temporal trajectories at different time points. The relevant formula is as follows: Suppose there are two time series and , distance matrix element , the cumulative distance matrix G is:

[0069] Later, the number of days was set as a fixed effect factor and possible interactions with other fixed effect factors were considered.

[0070] For brain functional connectivity data, based on the functional connectivity matrix (three indicators GC, MIC, and wPLI) at each time point, the connection pattern difference matrix was constructed by calculating the difference in matrix Frobenius norm, common edge ratio, and longitudinal changes in graph theory indicators. The relevant formula is as follows: The matrix Frobenius norm is the norm of the difference between two matrices M and N:

[0071] Common edge ratio (binary adjacency matrices M and N):

[0072] Node degree centrality (degree centrality of node i in the adjacency matrix M):

[0073] in, is the node degree centrality, m is the element of the M matrix, and n is the element of the N matrix. The permutation test is combined to determine the cross-period significance of the connection strength and network structure; (3) Based on the results of oscillation power analysis, the power spectral density value of each electrode point was extracted according to the EEG frequency band (δ, θ, α, β, γ). The linear mixed effects model (LMM) was used to fit the power change trend at different time points within the individual. Time was used as a fixed effect and the individual was used as a random effect. After controlling for inter-individual variation, the main effect of frequency band power across time and the interaction effect with electrode position were tested.

[0074] A statistical model was constructed and a time factor was introduced into the statistical model to analyze the long-term change trend of cognitive function indicators and the sustainability of the intervention effect.

[0075] Example 2 This embodiment discloses a multi-channel transcranial direct current stimulation cognitive function assessment system; like Figure 2 As shown, a multi-channel transcranial direct current stimulation cognitive function assessment system comprises: A multi-channel transcranial direct current stimulation cognitive function assessment system, comprising: The EEG signal data acquisition module is configured to: acquire EEG signal data of the subject under multi-channel transcranial direct current stimulation and perform preprocessing; The data analysis module is configured to perform microstate analysis, brain functional connectivity analysis, and oscillation power analysis on the preprocessed EEG signal data; The quantitative evaluation module is configured to: construct a linear mixed-effect model, input the results of microstate analysis, brain functional connectivity analysis, and oscillation power analysis into the linear mixed-effect model to perform a quantitative evaluation of cognitive function status; The longitudinal tracking evaluation module is configured to: construct a time series database, which is used to store the results of microstate analysis, brain functional connectivity analysis and oscillation power analysis across time points, and analyze the long-term change trend of cognitive function based on statistical models.

[0076] Example 3 The purpose of this embodiment is to provide a computer-readable storage medium.

[0077] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of a multi-channel transcranial direct current stimulation cognitive function assessment method as described in Example 1.

[0078] Example 4 The purpose of this embodiment is to provide an electronic device.

[0079] An electronic device comprises a memory, a processor, and a program stored in the memory and executable on the processor, wherein when the processor executes the program, the steps of the multi-channel transcranial direct current stimulation cognitive function assessment method as described in Example 1 are implemented.

[0080] The steps involved in the apparatuses of Examples 2, 3, and 4 above correspond to those of Method Example 1. For detailed implementations, please refer to the relevant description of Example 1. The term "computer-readable storage medium" should be understood to mean a single medium or multiple media containing one or more instruction sets; it should also be understood to include any medium capable of storing, encoding, or carrying an instruction set for execution by a processor and causing the processor to perform any method of the present invention.

[0081] Those skilled in the art will appreciate that the modules or steps of the present invention described above can be implemented using a general-purpose computer device. Alternatively, they can be implemented using program code executable by a computing device, which can then be stored in a storage device and executed by the computing device. Alternatively, they can be fabricated into separate integrated circuit modules, or multiple modules or steps can be fabricated into a single integrated circuit module for implementation. The present invention is not limited to any specific combination of hardware and software.

[0082] Although the above describes the specific embodiments of the present invention in conjunction with the accompanying drawings, it is not intended to limit the scope of protection of the present invention. Those skilled in the art should understand that various modifications or variations that can be made by those skilled in the art on the basis of the technical solution of the present invention without any creative work are still within the scope of protection of the present invention.

Claims

1. A multi-channel transcranial direct current stimulation cognitive function assessment method, characterized in that: include: Collect the EEG signal data of the subjects under multi-channel transcranial direct current stimulation and perform preprocessing; The preprocessed EEG signal data were subjected to microstate analysis, brain functional connectivity analysis, and oscillation power analysis; A linear mixed-effects model was constructed and the results of microstate analysis, brain functional connectivity analysis, and oscillation power analysis were input into the linear mixed-effects model to quantitatively evaluate the cognitive function status; A time series database is constructed to store the results of microstate analysis, brain functional connectivity analysis, and oscillation power analysis across time points, and to analyze long-term trends in cognitive function based on statistical models.

2. A multi-channel transcranial direct current stimulation cognitive function assessment method according to claim 1, characterized in that: The preprocessing includes: performing bandpass filtering and notch filtering on the collected EEG signal data to remove clutter interference; using wavelet denoising to reduce high-frequency myoelectric artifacts in the EEG signal data; and using independent component analysis to eliminate artifacts generated by non-EEG activities.

3. The multi-channel transcranial direct current stimulation cognitive function assessment method according to claim 1, characterized in that: Perform microstate analysis on the preprocessed EEG signal data, including: The preprocessed EEG signal data is decomposed into spatiotemporal patterns using a microstate partitioning algorithm. The continuous EEG signal is represented as a set of stable microstates of a finite category. The improved K-means clustering algorithm is used to cluster and set the microstate categories.

4. The multi-channel transcranial direct current stimulation cognitive function assessment method according to claim 1, characterized in that: The process of performing brain functional connectivity analysis on the preprocessed EEG signal data is as follows: Brain functional connectivity analysis is performed on the EEG signal data by calculating key connectivity indices of the EEG signal data; wherein the key connectivity indices include: Granger causality, weighted phase lag index and maximum information coefficient.

5. The multi-channel transcranial direct current stimulation cognitive function assessment method according to claim 1, characterized in that: Perform oscillation power analysis on the preprocessed EEG signal data, including: The Welch method is used to estimate the power spectrum of the preprocessed EEG data to obtain the power spectrum density of different frequency bands; as shown in the following formula: in, is the power spectrum density, L is the number of segments, N is the length of each segment, w[n] is the windowing function, For the segment signal, is the window function power normalization factor; Based on the power spectral density, the oscillation power of each frequency band in the target frequency band is calculated, which is defined as the integral of the power spectral density in the corresponding frequency range: in, is the oscillation power in a specific frequency band.

6. The multi-channel transcranial direct current stimulation cognitive function assessment method according to claim 1, characterized in that: The process of constructing a linear mixed-effects model and inputting the results of microstate analysis, brain functional connectivity analysis, and oscillation power analysis into the linear mixed-effects model for quantitative evaluation of cognitive function status includes: Fitting tests were performed on the parameter values ​​before and after stimulation and in different task states, including fixed-effect and random-effect tests, to test the statistical significance of the differences between groups. The parameter values ​​were set as the dependent variables, and the pre- and post-stimulation conditions, task state, and time were set as fixed-effect factors, and the interactions between them were considered. Participants were set as random-effect factors to explain individual differences, as shown in the following formula: in, is the parameter value of the i-th subject in the j-th observation; is a fixed effect term, For the The random effect of participants, is the residual term, and .

7. The multi-channel transcranial direct current stimulation cognitive function assessment method according to claim 1, characterized in that: The process of constructing a time series database for storing the results of microstate analysis, brain functional connectivity analysis, and oscillation power analysis across time points, and analyzing the long-term trend of cognitive function based on a statistical model is as follows: Performing feature alignment and cross-period comparison on the results of microstate analysis, brain functional connectivity analysis, and oscillation power analysis at different time points stored in the time series database; A statistical model was constructed and a time factor was introduced into the statistical model to analyze the long-term change trend of cognitive function indicators and the sustainability of the intervention effect.

8. A multi-channel transcranial direct current stimulation cognitive function assessment system, characterized in that: include: The EEG signal data acquisition module is configured to: acquire EEG signal data of the subject under multi-channel transcranial direct current stimulation and perform preprocessing; The data analysis module is configured to perform microstate analysis, brain functional connectivity analysis, and oscillation power analysis on the preprocessed EEG signal data; The quantitative evaluation module is configured to: construct a linear mixed-effect model, input the results of microstate analysis, brain functional connectivity analysis, and oscillation power analysis into the linear mixed-effect model to perform a quantitative evaluation of cognitive function status; The longitudinal tracking evaluation module is configured to: construct a time series database, which is used to store the results of microstate analysis, brain functional connectivity analysis and oscillation power analysis across time points, and analyze the long-term change trend of cognitive function based on statistical models.

9. A computer-readable storage medium having a program stored thereon, characterized in that: When the program is executed by a processor, the steps of the multi-channel transcranial direct current stimulation cognitive function assessment method as described in any one of claims 1 to 7 are implemented.

10. An electronic device comprising a memory, a processor, and a program stored in the memory and executable on the processor, wherein: When the processor executes the program, the steps of the multi-channel transcranial direct current stimulation cognitive function assessment method according to any one of claims 1 to 7 are implemented.

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