An evaluation method and system for visual cognitive ability based on electroencephalogram signals

Abstract

The present invention discloses a method and system for evaluating visual cognitive ability based on electroencephalogram (EEG) signals. The method includes collecting EEG signals from subjects performing a visual cognitive task paradigm test under a specified working memory paradigm; preprocessing the EEG signals to extract the subject's working memory average standard time-frequency diagram; and extracting the beta frequency band window energy from the working memory average standard time-frequency diagram using a moving window and averaging the energy to obtain a desynchronized energy value (DEV) as the evaluation result of the subject's visual cognitive ability. The desynchronized energy value (DEV) obtained by averaging the beta frequency band window energy in the present invention can accurately evaluate the subject's visual cognitive ability and can be widely used in aspects such as the evaluation and training optimization of personnel with high visual cognitive loads, such as vehicle driving and monitoring reading, to provide support for adapting to positions that require visual cognitive ability.

Description

Technical Field

[0001] The present invention relates to the technical field of visual cognitive ability assessment, and in particular to a method and system for assessing visual cognitive ability based on electroencephalogram (EEG) signals. Background Art

[0002] Visual cognitive ability primarily refers to abilities related to task completion, such as visual attention, alertness, and redundancy elimination during visual tasks. Cognitive ability is categorized into six dimensions: memory, attention, spatial ability, reasoning, computational ability, and perception. Currently, assessments of visual cognitive ability primarily rely on mathematical models based on data analysis and classification models based on machine learning.

[0003] Numerous studies using mathematical models based on data analysis methods have used previously collected cognitive dimension scale data and applied the reliability coefficient method to test the questionnaire's consistency and reliability. Multiple methods have been used to test the data, extracting dimensions related to cognitive ability, constructing structural equation models, and determining the fit between the model and the data. This assessment method requires extensive questionnaire surveys and data analysis, resulting in lengthy and cumbersome data analysis and unsatisfactory assessment results.

[0004] Classification models based on machine learning methods are capable of processing large amounts of physiological signals. Traditional machine learning methods have been able to achieve cognitive assessment models with high detection rates based on a single physiological signal. However, due to widespread individual differences, the optimal detection metrics for different individuals vary. Furthermore, the multi-source complexity of cognitive processes can lead to conflicting results between different metrics. A key approach to addressing this issue is to comprehensively utilize multiple signals and develop multi-source heterogeneous signal fusion assessment models based on machine learning methods. However, the feasibility of this approach remains to be considered. Currently, there are no mature multimodal fusion visual cognitive ability assessment models suitable for practical cognitive ability assessment needs. Summary of the Invention

[0005] Technical problems to be solved by the present invention: In response to the above-mentioned problems of the prior art, a method and system for evaluating visual cognitive ability based on EEG signals are provided. The present invention adopts the desynchronized energy value DEV obtained by averaging the window energy of the beta frequency band to accurately evaluate the visual cognitive ability of the subject. The method can be widely used in the evaluation, training optimization, etc. of staff with high visual cognitive loads such as vehicle driving and monitoring reading, and provide support for adapting to positions that require visual cognitive ability.

[0006] In order to solve the above technical problems, the technical solution adopted by the present invention is:

[0007] A method for evaluating visual cognitive ability based on electroencephalogram (EEG) signals, comprising:

[0008] S101, collecting EEG signals of subjects performing visual cognition task paradigm experiments under the specified working memory paradigm;

[0009] S102, preprocessing the EEG signal to extract the average standard time-frequency graph of the subject's working memory;

[0010] S103, for the average standard time-frequency graph of working memory, a moving window is used to extract the window energy of the beta frequency band, and the window energy of the beta frequency band is averaged to obtain a desynchronization energy value DEV as an evaluation result of the subject's visual cognitive ability.

[0011] Optionally, the working memory paradigm specified in step S101 includes three stages: a visual encoding stage, a memory maintenance stage, and a memory awakening stage. In the three stages, the subjects stimulate EEG signals by watching the screen. The visual cognitive task paradigm experiment includes randomly presenting two moving white scattered dots and two fixed red and yellow scattered dots on the screen in the visual encoding stage, the screen is black in the memory maintenance stage, and randomly presenting one moving white scattered dots on the screen in the memory awakening stage. The subjects judge whether the movement direction of the scattered dots is the same as a certain movement direction in the visual encoding stage, and the subjects are instructed to remember the movement direction and ignore the color.

[0012] Optionally, when collecting EEG signals in step S101, the reference electrode is Fpz, and signals are collected using 59 channels of dry electrodes including Fp1, Fp2, Fpz, AF3, AF4, AF7, AF8, Fz, F1, F2, F3, F4, F5, F6, F7, F8, FCz, FC1, FC2, FC3, FC4, FC5, FC6, FT7, FT8, Cz, C1, C2, C3, C4, C5, C6, T7, T8, CP1, CP2, CP3, CP4, CP5, CP6, TP7, TP8, Pz, P3, P4, P5, P6, P7, P8, POz, PO3, PO4, PO5, PO6, PO7, PO8, Oz, O1 and O2.

[0013] Optionally, when collecting the EEG signal in step S101 , the sampling frequency of the EEG signal is 1200 Hz.

[0014] Optionally, preprocessing the EEG signal in step S102 includes:

[0015] S201, extracting EEG signals for a specified time before the memory maintenance phase and a specified time at the beginning of the memory maintenance phase;

[0016] S202, denoising the EEG signal and removing artifacts;

[0017] S203, performing time-frequency transformation on the subject's EEG signal using a wavelet with certain bandwidth parameters and center parameters to obtain a time-frequency graph, performing Z-score standardization and maximum value normalization on the time-frequency graph over multiple time segments in turn, and superimposing and averaging the resulting time-frequency graphs over multiple time segments to obtain an average standard time-frequency graph of the subject's working memory.

[0018] Optionally, the denoising in step S202 refers to performing band-pass filtering using a 1-50 Hz band-pass filter.

[0019] Optionally, removing artifacts in step S202 refers to removing EEG signals exceeding a voltage threshold of + / - 50 μV as artifacts.

[0020] Optionally, step S103 includes:

[0021] S301, moving the window of the subject's working memory average standard time-frequency graph within the set time range and frequency range, recording the overlapping part of the window and the time-frequency graph as the window energy, and extracting the window energy of the beta frequency band;

[0022] S302: Calculate the desynchronization energy value DEV for the beta frequency band window energy according to the following formula to serve as the evaluation result of the subject's visual cognitive ability:

[0023]

[0024] In the above formula, DEV represents the desynchronization energy value DEV, t1 is the horizontal coordinate of the left edge of the window, t2 is the horizontal coordinate of the right edge of the window, t is the horizontal coordinate of the average standard time-frequency diagram of working memory, f1 is the vertical coordinate of the lower edge of the window, f2 is the vertical coordinate of the upper edge of the window, f is the vertical coordinate of the average standard time-frequency diagram of working memory, E(t,f) is the time-frequency energy of the average standard time-frequency diagram of the subject's working memory at time t and frequency f, and C is the number of nodes in the window.

[0025] Optionally, after step S103, the following steps are further included:

[0026] S401, a visual motion psychophysical experiment is conducted on the subject. In the visual motion psychophysical experiment, the stimulation pattern uses a vertically oriented sinusoidal motion grating. The grating has two motion directions, left and right, and two sizes. Four different stimulus patterns are generated in combination. The subject's discrimination threshold for the large grating and the discrimination threshold for the small grating are obtained. The subject's peripheral inhibition index (SI) is calculated based on the subject's discrimination threshold for the large grating and the discrimination threshold for the small grating:

[0027] SI = log 10 (large threshold)-log 10(small threshold),

[0028] In the above formula, large threshold represents the subject's discrimination threshold for large gratings, and small threshold represents the subject's discrimination threshold for small gratings;

[0029] S402, calculating the correlation coefficient between the subject's peripheral inhibition index SI and the desynchronization energy value DEV to evaluate the correlation between the desynchronization energy value DEV and the peripheral inhibition index SI.

[0030] In addition, the present invention also provides an evaluation system for visual cognitive ability based on EEG signals, comprising a microprocessor and a memory connected to each other, wherein the microprocessor is programmed or configured to execute the evaluation method for visual cognitive ability based on EEG signals.

[0031] In addition, the present invention also provides a computer-readable storage medium, in which a computer program is stored. The computer program is used to be programmed or configured by a microprocessor to execute the method for evaluating visual cognitive ability based on EEG signals.

[0032] Compared with the existing technology, the present invention mainly has the following advantages: the method of the present invention includes collecting EEG signals of subjects performing a visual cognitive task paradigm test under a specified working memory paradigm; preprocessing the EEG signals to extract the average standard time-frequency diagram of the subject's working memory; using a moving window to extract the window energy of the beta frequency band from the average standard time-frequency diagram of the working memory, and averaging the window energy of the beta frequency band to obtain a desynchronized energy value DEV as the evaluation result of the subject's visual cognitive ability. The desynchronized energy value DEV obtained by averaging the window energy of the beta frequency band in the present invention can achieve an accurate assessment of the subject's visual cognitive ability, and can be widely used in the assessment, training optimization, and other aspects of personnel with high visual cognitive loads such as vehicle driving, monitoring reading, etc., providing support for adapting to positions that require visual cognitive ability, making the assessment of visual cognitive ability more complete. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] Figure 1 Schematic diagram of the basic process of the method of the embodiment of the present invention.

[0034] Figure 2 2 is a diagram of the working memory paradigm in an embodiment of the present invention.

[0035] Figure 3 This is the average standard time-frequency diagram of the working memory of the subjects in the embodiment of the present invention.

[0036] Figure 4 4 is a correlation coefficient diagram between the desynchronization energy value DEV and the peripheral inhibition index SI in an embodiment of the present invention. DETAILED DESCRIPTION

[0037] The principles of the present EEG-based visual cognitive ability assessment method and system are as follows: Both exogenous and endogenous attention allocation influence our perception of the environment. Endogenous attention, or top-down control, is driven by factors such as expectations and goals and allows us to successfully perform higher cognitive functions. This goal-directed control manifests as enhanced neural activity in sensory cortical areas for relevant information and suppressed neural responses to irrelevant information, a process known as top-down modulation. The neural bias provided by top-down modulation is believed to serve memory by favoring the efficient encoding of relevant visual information. Therefore, the ability of top-down modulation can be used as a metric for assessing visual cognitive ability. Previous studies have suggested that the content of working memory can be conceptualized as internal representations maintained within the focus of visual attention. Neuroimaging studies have supported this by identifying mechanistic overlap between attention and working memory. Selective attention modulation during working memory encoding is significantly correlated with subsequent recognition performance. We propose that beta-band power during the maintenance phase of working memory serves as the basis for assessing visual cognitive ability, thereby enabling the effective EEG-based assessment of visual cognitive ability.

[0038] like Figure 1 As shown, the method for evaluating visual cognitive ability based on EEG signals in this embodiment includes:

[0039] S101, collecting EEG signals of subjects performing visual cognition task paradigm experiments under the specified working memory paradigm;

[0040] S102, preprocessing the EEG signal to extract the average standard time-frequency graph of the subject's working memory;

[0041] S103, for the average standard time-frequency graph of working memory, use a moving window to extract the window energy of the beta frequency band (10-15Hz), and average the window energy of the beta frequency band to obtain the desynchronization energy value DEV (Desynchronization Energy Value) as the evaluation result of the subject's visual cognitive ability.

[0042] The working memory paradigm specified in step S101 of this embodiment includes three stages: the visual encoding stage, the memory maintenance stage, and the memory awakening stage. In the three stages, the subjects stimulate the EEG signals by watching the screen. The visual cognitive task paradigm experiment includes randomly presenting two moving white scattered dots and two fixed red and yellow scattered dots through the screen in the visual encoding stage. In the memory maintenance stage, the screen is black. In the memory awakening stage, the screen randomly presents one moving white scattered dot. The subject judges whether the movement direction of the scattered dots is the same as a certain movement direction in the visual encoding stage, and the subject is instructed to remember the movement direction and ignore the color. In this embodiment, the working memory paradigm is presented 10 times, and the EEG signal acquisition time includes the full time of the experimental paradigm. In this embodiment, 20 subjects were specifically selected to participate in the experiment. The subjects conducted a visual cognitive task paradigm experiment and collected EEG signals of the users in a standard physiological state. The experimental paradigm is divided into three stages: the visual encoding stage, the memory maintenance stage, and the memory awakening stage. The stimulus consisted of 290 dots (0.08° × 0.08°) that covered an 8° visual angle at a viewing distance of 75 cm and were centered. Two types of dots were used during the experiment: (1) gray and moving at a constant speed of 10° per second, or (2) stationary and colored along the axis. The stimuli were presented on a black background with a gray fixation cross in the center of the circular hole. The subjects stimulated EEG signals by viewing the stimuli on the display screen in three phases. During the visual encoding phase, the screen randomly presented two moving white dots and two fixed red / yellow dots, each appearing for 800 ms with an 800 ms interval. After that, the memory maintenance phase began, with the screen in a black state for 4000 ms. During the memory remodeling phase, the screen randomly presented one moving white dot for 800 ms. The subjects had 2 s to judge whether the direction of the dot movement was the same as a certain direction of movement during the visual encoding phase. We instructed the subjects to remember the "movement" direction and ignore the color of the dot. The EEG acquisition time included the entire duration of the experimental paradigm. The experimental paradigm was repeated 10 times, and each subject received 10 EEG signal samples. Figure 2 shown.

[0043] In this embodiment, when collecting EEG signals in step S101, the reference electrode is Fpz, and 59 channels of dry electrodes including Fp1, Fp2, Fpz, AF3, AF4, AF7, AF8, Fz, F1, F2, F3, F4, F5, F6, F7, F8, FCz, FC1, FC2, FC3, FC4, FC5, FC6, FT7, FT8, Cz, C1, C2, C3, C4, C5, C6, T7, T8, CP1, CP2, CP3, CP4, CP5, CP6, TP7, TP8, Pz, P3, P4, P5, P6, P7, P8, POz, PO3, PO4, PO5, PO6, PO7, PO8, Oz, O1 and O2 are used for signal collection. In this embodiment, a 64-channel EEG amplifier that complies with the 10-20 method calibrated by the International Electroencephalography Association is used to collect and amplify the signals of each channel.

[0044] In this embodiment, when collecting the EEG signal in step S101 , the sampling frequency of the EEG signal is 1200 Hz.

[0045] In this embodiment, the pre-processing of the EEG signal in step S102 includes:

[0046] S201, extracting EEG signals for a specified time before the memory maintenance phase and for a specified time at the beginning of the memory maintenance phase; for example, as an optional implementation, in this embodiment, EEG signals for a time period of 1000 ms before the memory maintenance phase and EEG signals for a time period of 0-3500 ms during the memory maintenance phase are extracted;

[0047] S202, denoising the EEG signal and removing artifacts;

[0048] S203, performing time-frequency transformation on the subject's EEG signal using a wavelet with certain bandwidth parameters and center parameters to obtain a time-frequency graph, performing Z-score standardization and maximum value normalization on the time-frequency graph over multiple time segments in turn, and superimposing and averaging the resulting time-frequency graphs over multiple time segments to obtain an average standard time-frequency graph of the subject's working memory.

[0049] In this embodiment, the denoising in step S202 refers to performing bandpass filtering using a 1-50 Hz bandpass filter. Bandpass filtering is a filtering method. Since the EEG signal frequency does not exceed 60 Hz, a 1-50 Hz bandpass filter can be designed to perform preliminary denoising.

[0050] In this embodiment, artifact removal in step S202 refers to removing EEG signals exceeding a voltage threshold of + / - 50 μV as artifacts. Specifically, this method uses EEGLAB's AAR to remove EOG. Specifically, open the EEGLAB plugin for MATLAB, then click Tools > Artifact removal using AAR 1.3 > EOG removal > Using BSS. After removing EOG, EEG signals exceeding a voltage threshold of + / - 50 μV are removed as artifacts.

[0051] In this embodiment, 10 EEG data of each subject were wavelet transformed, and time-frequency transformation was performed using a wavelet with a bandwidth parameter of 1.5 and a center parameter of 2. Ten time-frequency graphs with a time horizontal coordinate of -1000-3500 ms and a frequency vertical coordinate of 1-40 Hz were obtained for each subject. The 10 time-frequency graphs were Z-score standardized and maximum value normalized in time, and the 10 time-frequency graphs were superimposed and averaged to obtain the average standard time-frequency graph of working memory for each subject.

[0052] In this embodiment, step S103 includes:

[0053] S301, the average standard time-frequency graph of the subject's working memory is moved with a window within the set time range and frequency range, the overlapping part of the window and the time-frequency graph is recorded as the window energy, and the window energy of the beta frequency band is extracted, such as Figure 3 As shown, the black frame in the figure is a movable window;

[0054] S302: Calculate the desynchronization energy value DEV for the beta frequency band window energy according to the following formula to serve as the evaluation result of the subject's visual cognitive ability:

[0055]

[0056] In the above formula, DEV represents the desynchronization energy value DEV, t1 is the horizontal coordinate of the left edge of the window, t2 is the horizontal coordinate of the right edge of the window, t is the horizontal coordinate of the average standard time-frequency diagram of working memory, f1 is the vertical coordinate of the lower edge of the window, f2 is the vertical coordinate of the upper edge of the window, f is the vertical coordinate of the average standard time-frequency diagram of working memory, E(t,f) is the time-frequency energy of the average standard time-frequency diagram of the subject's working memory at time t and frequency f, and C is the number of nodes in the window.

[0057] On this basis, in order to further verify the accuracy of the synchronization energy value DEV proposed by the method of this embodiment, this embodiment further includes the following steps after step S103:

[0058] S401, a visual motion psychophysical experiment is conducted on the subject. In the visual motion psychophysical experiment, the stimulation pattern uses a vertically oriented sinusoidal motion grating. The grating has two motion directions, left and right, and two sizes. Four different stimulus patterns are generated in combination. The subject's discrimination threshold for the large grating and the discrimination threshold for the small grating are obtained. The subject's peripheral inhibition index (SI) is calculated based on the subject's discrimination threshold for the large grating and the discrimination threshold for the small grating:

[0059] SI = log 10 (large threshold)-log 10 (small threshold),

[0060] In the above formula, large threshold represents the subject's discrimination threshold for large gratings, and small threshold represents the subject's discrimination threshold for small gratings;

[0061] S402, calculating the correlation coefficient between the subject's peripheral inhibition index SI and the desynchronization energy value DEV to evaluate the correlation between the desynchronization energy value DEV and the peripheral inhibition index SI.

[0062] It should be noted that the visuomotor psychophysics experiment is a well-known experiment used to measure individual differences in visual suppression. The stimulus pattern employed a vertical sinusoidal grating with 80% contrast, a spatial frequency of 1 cycle / degree, and a grating motion speed of 4 degrees / second. The grating edges were blurred using a cosine function (blur width of 0.3°). The gratings had two motion directions (left and right) and two sizes (2° and 10°), resulting in four different stimulus patterns. At the beginning of each trial, a cross appeared in the center of the screen, and participants were required to maintain their fixation on the cross. After 500 milliseconds, the cross disappeared, and a random stimulus pattern appeared in the center of the screen. The grating moved left or right, and participants were required to determine the direction of the grating's motion by pressing a key on a keyboard. If the judgment was incorrect, an audible feedback sound was heard. The experiment consisted of 160 trials. After 80 trials, a prompt appeared, allowing participants to take a short break and continue for the remaining 80 trials. For each participant, the accuracy of the judgment was calculated for each grating duration and at two grating sizes. These values are then fitted to a cumulative Gaussian function, and the corresponding grating presentation duration when the accuracy is the set value (75%) is used as the discrimination threshold. After obtaining each subject's discrimination threshold for large gratings (larger threshold) and small gratings (smaller threshold), the peripheral suppression index (SI) can be calculated based on the subject's discrimination threshold for large gratings and small gratings. In this embodiment, when the time window is 750ms-1250ms and the frequency window is 10-15Hz, the correlation coefficient between the subject's peripheral suppression index SI and the desynchronization energy value DEV is calculated as shown in Table 1.

[0063] Table 1: Evaluation results of the subjects' peripheral inhibition index SI and desynchronization energy value DEV

[0064] Data Sample Desynchronization energy value DEV Peripheral inhibition index SI Subject 1 -0.9602 0.3155 Subject 2 -0.8110 1.2503 Subject 3 -0.9144 0.8197 Participant 4 -0.9352 0.3454 Subject 5 -0.2942 1.0799 Subject 6 -0.7010 0.6470 Subject 7 -0.9292 0.6217 Subject 8 -0.9716 0.4605 Subject 9 -0.8679 0.5964 Subject 10 -0.5471 0.8151 Participant 11 -0.9002 0.6585 Subject 12 -0.8306 0.4582 Participant Thirteen -0.8358 0.3500 Subject 14 -0.9922 0.1635 Subject 15 -0.4801 1.5888 Subject 16 -0.9467 0.8151 Participant 17 -0.7247 0.2832 Subject 18 -0.8773 0.0437 Subject 19 -0.8508 0.4766 Twenty subjects -0.7230 0.4605

[0065] In addition, the correlation coefficient between the subjects' peripheral inhibition index SI (SI value) and desynchronization energy value DEV (DEV value) is calculated as shown in the figure below: Figure 4 As shown, the correlation is (r=0.5894, p=0.0062), and the two are significantly correlated, which verifies the feasibility of using the EEG beta wave desynchronization assessment index to measure visual cognitive ability.

[0066] In summary, there are many different ways to judge cognitive ability, but visual cognitive ability cannot be well evaluated. The method in this embodiment analyzes the desynchronization phenomenon of the beta frequency band (10-15Hz) of the EEG during working memory, and evaluates the visual cognitive ability of the subject by calculating the desynchronization energy value DEV used to evaluate visual cognitive ability. Here, we measure individual differences in perceptual inhibition in a simple visual discrimination task, and measure individual differences in desynchronization phenomenon in a visual cognition task based on EEG. We found that there is a strong positive correlation between individual visual inhibition values and desynchronization energy values DEV, which proves that the ability to inhibit irrelevant information and quickly process relevant information fundamentally restricts sensory discrimination and working memory capabilities, and determines the feasibility of using the desynchronization energy value DEV in the beta frequency band to measure visual cognitive ability.

[0067] In addition, this embodiment also provides an assessment system for visual cognition ability based on EEG signals, comprising a microprocessor and a memory connected to each other, wherein the microprocessor is programmed or configured to execute the aforementioned method for assessing visual cognition ability based on EEG signals. In addition, this embodiment also provides a computer-readable storage medium storing a computer program for being programmed or configured by the microprocessor to execute the aforementioned method for assessing visual cognition ability based on EEG signals.

[0068] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present application may take the form of a computer program product implemented on one or more computer-readable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code. The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, may be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the steps in the process. Figure 1 a process or multiple processes and / or boxes Figure 1These computer program instructions can also be stored in a computer-readable memory that can guide a computer or other programmable data processing device to work in a specific way, so that the instructions stored in the computer-readable memory produce a product including the instruction device, which implements the function specified in the process. Figure 1 a process or multiple processes and / or boxes Figure 1 These computer program instructions can also be loaded onto a computer or other programmable data processing device, so that a series of operation steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process in the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0069] The above description is merely a preferred embodiment of the present invention. The scope of protection of the present invention is not limited to the above embodiment. All technical solutions based on the concept of the present invention are within the scope of protection of the present invention. It should be noted that for those skilled in the art, various improvements and modifications that do not depart from the principles of the present invention should also be considered within the scope of protection of the present invention.

Claims

1. A method for evaluating visual cognitive ability based on EEG signals, characterized in that: include: S101, collecting EEG signals of subjects performing visual cognition task paradigm trials under the specified working memory paradigm; S102, preprocessing the EEG signal to extract the average standard time-frequency graph of the subject's working memory; S103, for the average standard time-frequency graph of working memory, a moving window is used to extract the window energy of the beta frequency band, and the window energy of the beta frequency band is averaged to obtain a desynchronization energy value DEV as an evaluation result of the subject's visual cognitive ability.

2. The method for evaluating visual cognitive ability based on EEG signals according to claim 1, characterized in that: The working memory paradigm specified in step S101 includes three stages: visual encoding stage, memory maintenance stage and memory awakening stage. In the three stages, the subjects stimulate EEG signals by watching the screen. The visual cognitive task paradigm experiment includes randomly presenting two moving white scattered dots and two fixed red and yellow scattered dots on the screen in the visual encoding stage, the screen is black in the memory maintenance stage, and randomly presenting one moving white scattered dots on the screen in the memory awakening stage. The subjects judge whether the movement direction of the scattered dots is the same as a certain movement direction in the visual encoding stage, and the subjects are instructed to remember the movement direction and ignore the color.

3. The method for evaluating visual cognitive ability based on EEG signals according to claim 2, wherein: When collecting EEG signals in step S101, the reference electrode is Fpz, and signals are collected using 59 channels of dry electrodes including Fp1, Fp2, Fpz, AF3, AF4, AF7, AF8, Fz, F1, F2, F3, F4, F5, F6, F7, F8, FCz, FC1, FC2, FC3, FC4, FC5, FC6, FT7, FT8, Cz, C1, C2, C3, C4, C5, C6, T7, T8, CP1, CP2, CP3, CP4, CP5, CP6, TP7, TP8, Pz, P3, P4, P5, P6, P7, P8, POz, PO3, PO4, PO5, PO6, PO7, PO8, Oz, O1 and O2.

4. The method for evaluating visual cognitive ability based on EEG signals according to claim 3, wherein: When collecting the EEG signal in step S101 , the sampling frequency of the EEG signal is 1200 Hz.

5. The method for evaluating visual cognitive ability based on EEG signals according to claim 4, wherein: The pre-processing of the EEG signal in step S102 includes: S201, extracting EEG signals for a specified time before the memory maintenance phase and a specified time at the beginning of the memory maintenance phase; S202, denoising the EEG signal and removing artifacts; S203, performing time-frequency transformation on the subject's EEG signal using a wavelet with certain bandwidth parameters and center parameters to obtain a time-frequency graph, performing Z-score standardization and maximum value normalization on the time-frequency graph over multiple time segments in turn, and superimposing and averaging the resulting time-frequency graphs over multiple time segments to obtain an average standard time-frequency graph of the subject's working memory.

6. The method for evaluating visual cognitive ability based on EEG signals according to claim 5, characterized in that: The denoising in step S202 refers to performing band-pass filtering with a 1-50 Hz band-pass filter, and the artifact removal refers to removing the EEG signals exceeding a voltage threshold of + / - 50 μV as artifacts.

7. The method for evaluating visual cognitive ability based on EEG signals according to claim 6, wherein: Step S103 includes: S301, moving the window of the subject's working memory average standard time-frequency graph within the set time range and frequency range, recording the overlapping part of the window and the time-frequency graph as the window energy, and extracting the window energy of the beta frequency band; S302: Calculate the desynchronization energy value DEV for the beta frequency band window energy according to the following formula to serve as the evaluation result of the subject's visual cognitive ability: In the above formula, DEV represents the desynchronization energy value DEV, t1 is the horizontal coordinate of the left edge of the window, t2 is the horizontal coordinate of the right edge of the window, t is the horizontal coordinate of the average standard time-frequency diagram of working memory, f1 is the vertical coordinate of the lower edge of the window, f2 is the vertical coordinate of the upper edge of the window, f is the vertical coordinate of the average standard time-frequency diagram of working memory, E(t,f) is the time-frequency energy of the average standard time-frequency diagram of the subject's working memory at time t and frequency f, and C is the number of nodes in the window.

8. The method for evaluating visual cognitive ability based on EEG signals according to claim 1, wherein: After step S103, the following steps are also included: S401, a visual motion psychophysical experiment is conducted on the subject. In the visual motion psychophysical experiment, the stimulation pattern uses a vertically oriented sinusoidal motion grating. The grating has two motion directions, left and right, and two sizes. Four different stimulus patterns are generated in combination. The subject's discrimination threshold for the large grating and the discrimination threshold for the small grating are obtained. The subject's peripheral inhibition index (SI) is calculated based on the subject's discrimination threshold for the large grating and the discrimination threshold for the small grating: SI=log 10 (large threshold)-log 10 (small threshold), In the above formula, large threshold represents the subject's discrimination threshold for large gratings, and small threshold represents the subject's discrimination threshold for small gratings; S402, calculating the correlation coefficient between the subject's peripheral inhibition index SI and the desynchronization energy value DEV to evaluate the correlation between the desynchronization energy value DEV and the peripheral inhibition index SI.

9. A visual cognitive ability assessment system based on electroencephalogram signals, comprising a microprocessor and a memory connected to each other, characterized in that: The microprocessor is programmed or configured to execute the method for evaluating visual cognitive ability based on EEG signals according to any one of claims 1 to 8.

10. A computer-readable storage medium storing a computer program, wherein: The computer program is used to be programmed or configured by a microprocessor to execute the method for evaluating visual cognitive ability based on EEG signals according to any one of claims 1 to 8.

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