Psychological analysis electroencephalogram signal processing method, device, equipment and medium

Abstract

The method is mainly applied to the technical field of electroencephalogram signals. The invention discloses a psychoanalysis electroencephalogram signal processing method and device, equipment and a medium, and the method comprises the steps: obtaining electroencephalogram signals through a plurality of collection stages, and obtaining an electroencephalogram signal group corresponding to each collection stage; dividing each electroencephalogram signal group into wave band signals corresponding to each frequency band based on the frequency bands of the electroencephalogram signals; determining wave band signals related to the psychological state to be tested from the plurality of wave band signals so as to obtain a plurality of target wave band signals through screening; determining a difference value between every two target wave band signals; and based on the plurality of difference values, generating an evaluation result of the change process of the to-be-tested psychological state. According to the method, the accuracy of psychological assessment can be improved by identifying the electroencephalogram signal change, and the psychological recovery effect can be assessed more objectively.

Description

Technical Field

[0001] The present invention relates to the technical field of electroencephalogram signals, and specifically relates to a method, device, equipment and medium for processing electroencephalogram signals for psychological analysis. Background Art

[0002] With the rapid development of modern society, people are facing increasing psychological pressure. This pressure not only affects the physical and mental health of individuals, but also has a negative impact on their cognitive functions and work efficiency. Therefore, how to effectively evaluate and promote psychophysiological recovery has become an important research topic. Psychophysiological recovery refers to the process by which an individual returns to a normal psychological and physiological state through various means after experiencing stress or fatigue. This process involves multiple factors, including the environment, individual differences, recovery activities, etc.

[0003] In the research of psychological recovery, traditional technical solutions can only provide relatively rough evaluation results of psychological recovery, and it is difficult to accurately detect subtle changes under different conditions. In order to more comprehensively and objectively evaluate the effect of psychological recovery, new solutions are urgently needed. Summary of the Invention

[0004] The present invention provides a method, device, equipment and medium for processing electroencephalogram signals for psychological analysis, which improves the accuracy of psychological evaluation by identifying changes in electroencephalogram signals, and is conducive to more objectively evaluating the effect of psychological recovery.

[0005] The present invention provides a method for processing electroencephalogram signals for psychological analysis, and the method includes: Obtaining electroencephalogram signals through multiple acquisition stages to obtain an electroencephalogram signal group corresponding to each acquisition stage; Dividing each electroencephalogram signal group into band signals corresponding to each frequency band based on the frequency band of the electroencephalogram signals; Determining the band signals related to the psychological state to be measured from multiple band signals to screen out multiple target band signals; Determining the difference between every two target band signals; Generating an evaluation result of the change process of the psychological state to be measured based on multiple differences.

[0006] Further, the method for processing electroencephalogram signals for psychological analysis further includes: Determining the acquisition area for collecting the electroencephalogram signals based on the anatomical structure of the brain, and each acquisition area includes multiple acquisition points; Generating a signal data set corresponding to each acquisition point based on the electroencephalogram signals collected at each acquisition point, and each signal data set includes frequency band energy data; Based on the band energy data corresponding to each acquisition point within the same acquisition area, determine the band energy data corresponding to the acquisition area, and generate a band feature according to the band energy data; Determine the band of the electroencephalogram signal according to the band feature corresponding to each acquisition area.

[0007] Further, the obtaining the electroencephalogram signal through multiple acquisition stages to obtain an electroencephalogram signal group corresponding to each acquisition stage includes: Generate multiple groups of test objects, each group of test objects including at least one test object; Collect the electroencephalogram signals of each test object respectively; For each test object, collect the electroencephalogram signal through multiple acquisition stages to obtain an electroencephalogram signal group corresponding to each acquisition stage.

[0008] Further, the determining the difference between every two target band signals includes: Classify each target band signal based on the acquisition stage corresponding to the target band signal to obtain multiple band signal groups, each band signal group including at least one of the target band signals; Determine the average power value of each target band signal; Compare the average power values of the target band signals in the same frequency band in every two band signal groups to determine the power change difference of the electroencephalogram signals in the same frequency band at different acquisition stages.

[0009] Further, the electroencephalogram signal processing method for psychological analysis further includes: Determine the total average value corresponding to each band signal group, where the total average value is the average value of the sum of the average power values of all target band signals in the band signal group; When determining the degree of difference between the electroencephalogram signal power values corresponding to two different acquisition stages, compare the total average value corresponding to the first band signal group with the total average value corresponding to the second band signal group, and determine the degree of difference according to the comparison result.

[0010] Further, the when determining the degree of difference between the electroencephalogram signal power values corresponding to two different acquisition stages, compare the total average value corresponding to the first band signal group with the total average value corresponding to the second band signal group, and determine the degree of difference according to the comparison result includes: Obtain the average power value datasets of the target band signals corresponding to the first acquisition stage and the second acquisition stage respectively, each average power value dataset including a sample quantity value and a sample mean and sample standard deviation value calculated from the average power value and the sample quantity value; After determining the prediction comparison result, determine the probability value of the prediction comparison result according to the sample quantity value, the sample mean value, and the sample standard deviation value; If the probability value is less than a preset threshold, it is determined that the prediction comparison result does not hold and the actual comparison result is determined to have a significant difference. If the probability value is greater than or equal to the preset threshold, it is determined that the prediction comparison result holds and is equal to the actual comparison result, and the actual comparison result is determined to have no significant difference.

[0011] Further, the step of determining the probability value of the prediction comparison result according to the sample quantity value, the sample mean value, and the sample standard deviation value includes: Determine the statistic of the target test method according to the first functional relationship among the sample quantity value, the sample mean value, and the sample standard deviation value; Determine the degree of freedom of the target test method according to the second functional relationship among the sample quantity value, the sample mean value, and the sample standard deviation value; Based on the preset mapping relationship between the statistic and the degree of freedom, determine the probability value.

[0012] The present invention also provides a mental analysis electroencephalogram signal processing device, and the device includes: An acquisition module, configured to acquire electroencephalogram signals through multiple acquisition stages to obtain an electroencephalogram signal group corresponding to each acquisition stage; A frequency division module, configured to divide each electroencephalogram signal group into band signals corresponding to each frequency band based on the frequency band of the electroencephalogram signal; A screening module, configured to determine the band signals related to the mental state to be measured from multiple band signals to screen out multiple target band signals; A calculation module, configured to determine the difference between every two target band signals; An evaluation module, configured to generate an evaluation result of the change process of the mental state to be measured based on multiple differences.

[0013] The present invention also provides an electronic device, and the electronic device includes a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, the mental analysis electroencephalogram signal processing method described in any one of the above is implemented.

[0014] The present invention also provides a computer-readable storage medium, and the computer-readable storage medium stores a computer program. When the computer program is executed by a processor, the mental analysis electroencephalogram signal processing method described in any one of the above is implemented.

[0015] The present invention has at least the following beneficial effects: In the technical solution of this application, by collecting electroencephalogram signals in multiple stages, accurately dividing the band signals corresponding to frequency bands, and screening the band signals related to the psychological state to be measured; further, by calculating the difference between the target band signals and generating an evaluation result of the change in the psychological state, the problems of rough evaluation results and difficulty in detecting subtle changes in the traditional technical solution in psychological recovery research are solved. Through multi-stage collection and frequency band division, the resolution and pertinence of the signals are improved; by screening the target band signals, the information closely related to the psychological state is focused; by calculating the difference and generating an evaluation result of the change process, the dynamic monitoring of the psychological recovery effect is realized. The electroencephalogram signal processing method for psychological analysis in this application can more accurately identify the changes in electroencephalogram signals, thereby improving the accuracy of psychological evaluation and providing strong support for a more comprehensive and objective evaluation of the psychological recovery effect. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] The drawings are used to provide a further understanding of the technical solution of the present invention, and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the technical solution of the present invention and do not constitute a limitation to the technical solution of the present invention.

[0017] Figure 1 It is a flowchart of the steps of the electroencephalogram signal processing method for psychological analysis.

[0018] Figure 2 It is another flowchart of the steps of the electroencephalogram signal processing method for psychological analysis.

[0019] Figure 3 It is a flowchart of the steps of step S101 in the electroencephalogram signal processing method for psychological analysis.

[0020] Figure 4 It is a flowchart of the steps of step S104 in the electroencephalogram signal processing method for psychological analysis.

[0021] Figure 5 It is another flowchart of the steps of the electroencephalogram signal processing method for psychological analysis.

[0022] Figure 6 It is a flowchart of the steps of step S502 in the electroencephalogram signal processing method for psychological analysis.

[0023] Figure 7 It is a schematic structural diagram of the electroencephalogram signal processing device for psychological analysis.

[0024] Figure 8 It is a schematic structural diagram of an electronic device. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0025] In order to make the objectives, technical solutions, and advantages of the present invention more clear and understandable, the present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0026] It should be noted that electroencephalogram technology (EEG) is a non-invasive neurophysiological monitoring method that records the electrical activities of brain neurons by placing electrodes on the scalp and is used for applications such as studying brain functions, diagnosing brain diseases, and developing brain-computer interfaces.

[0027] Self-Attention Restoration Theory (ART): ART was proposed by the Kaplans. This theory holds that in a modern urban environment, continuous use of directed attention (requiring mental concentration) is likely to lead to mental fatigue. The natural environment can attract humans' involuntary attention, thereby restoring the orienting attention mechanism and improving people's mood and perceptual functions. ART proposes four factors for restorative experiences in the natural environment, namely being away, extent, fascination, and compatibility. These factors work together to make the natural environment an excellent resting environment, helping to restore attention and reduce mental fatigue.

[0028] Stress Restoration Theory (SRT): SRT was proposed by Ulrich. Based on the perspective of psychological evolution, this theory holds that humans have developed a dependence on certain natural features during the long process of evolution, and this evolutionary adaptation mechanism determines that people have an innate positive response to the open space of nature, specific vegetation structures, water bodies, etc. SRT believes that spending time in the natural environment can reduce stress, block negative thoughts by attracting an individual's attention, inhibit negative emotions, stimulate positive emotions, and restore the balance of the disturbed physiological operation, thereby promoting the restoration of cognitive and behavioral abilities.

[0029] In the research on psychological restoration, traditional technical solutions can only provide relatively rough evaluation results of psychological restoration and are difficult to accurately detect subtle changes under different conditions. In order to more comprehensively and objectively evaluate the effect of psychological restoration, new solutions are urgently needed.

[0030] To solve the above problems, the present invention provides a method, device, equipment, and medium for processing electroencephalogram signals for psychological analysis. By identifying changes in electroencephalogram signals, the accuracy of psychological evaluation is improved, which is conducive to more objectively evaluating the effect of psychological restoration. The following are embodiments of the technical solutions of this application.

[0031] Please refer to Figure 1 , Figure 1 which is a flowchart of the steps of the method for processing electroencephalogram signals for psychological analysis.

[0032] This embodiment provides a method for processing electroencephalogram (EEG) signals for psychological analysis, including: S101. Obtain EEG signals through multiple acquisition stages to obtain an EEG signal group corresponding to each acquisition stage.

[0033] S102. Based on the frequency bands of the EEG signals, divide each EEG signal group into band signals corresponding to each frequency band.

[0034] S103. Determine the band signals related to the psychological state to be measured from multiple band signals, so as to screen out multiple target band signals.

[0035] S104. Determine the difference between every two target band signals.

[0036] S105. Generate an evaluation result of the change process of the psychological state to be measured based on multiple differences.

[0037] In step S101 of some embodiments, EEG signals are collected respectively according to different stages of psychological state tests (such as before, during, and after stress tests). For example, in psychological recovery research, EEG signals can be collected every 30 minutes after the subject experiences a stress event to form an EEG signal group corresponding to multiple acquisition stages.

[0038] In some embodiments, a portable wearable EEG measurement and recording system (BitBrain, 256Hz) is used for data acquisition, with a common mode rejection ratio (CMRR) of -115 dB and a 10–20 electrode system.

[0039] In step S102 of some embodiments, the EEG signal data is processed by the ErgoLAB EEG analysis module, using high and low pass filtering (0.1~49Hz) and frequency domain index extraction. The EEG signals are divided into different frequency bands, such as theta waves (4 - 8 Hz), alpha waves (8 - 13 Hz), and beta waves (13 - 30 Hz), and each frequency band corresponds to a different EEG activity state.

[0040] It can be understood that when determining the band signals related to the psychological state to be measured in step S103, by analyzing the correlation between different frequency band signals and the psychological state, the band signals related to the psychological state to be measured (such as anxiety, relaxation) are screened out. For example, the state of concentrating attention is usually related to the band signals of beta waves and theta waves, and the band signals of alpha waves, beta waves, and theta waves are usually related to the relaxation state.

[0041] It is understandable that the power or amplitude difference of the target band signal in adjacent acquisition stages is calculated. For example, the power difference of the alpha band signal before and after a stress test is calculated to reflect the change in mental state. By continuously calculating the differences in multiple stages, a dynamic trajectory of the mental state change is formed, providing data support for subsequent evaluation.

[0042] Please refer to Figure 2 , Figure 2 which is another step flowchart of the electroencephalogram signal processing method for psychoanalysis.

[0043] Optionally, in some embodiments, the electroencephalogram signal processing method for psychoanalysis further includes: S201. Based on the anatomical structure of the brain, determine the acquisition areas for collecting electroencephalogram signals, and each acquisition area includes multiple acquisition points.

[0044] S202. Based on the electroencephalogram signals collected at each acquisition point, generate a signal data set corresponding to each acquisition point, and each signal data set includes band energy data.

[0045] S203. Based on the band energy data corresponding to each acquisition point within the same acquisition area, determine the band energy data corresponding to the acquisition area, and generate band features according to the band energy data.

[0046] S204. Determine the frequency band of the electroencephalogram signal according to the band features corresponding to each acquisition area.

[0047] In step S201 of some embodiments, the divided acquisition areas have different numbers of acquisition points. For example, the acquisition areas can be divided as follows: the prefrontal acquisition area, and the acquisition points include FPZ; the frontal acquisition area, and the acquisition points include FZ, F3, and F4; the occipital acquisition area, and the acquisition points include O1 and O2; the parietal acquisition area, and the acquisition points include P3 and P4. Electroencephalogram signals are obtained at the acquisition points through electrodes.

[0048] In some embodiments, a multi-channel electroencephalogram acquisition device (such as a 64-channel Neuroscan system) is used to arrange multiple acquisition points in specific acquisition areas to ensure comprehensive signal coverage.

[0049] It is understandable that the short-time Fourier transform (STFT) is used to convert the electroencephalogram signal from the time domain to the time-frequency domain signal, so as to extract the frequency domain features of the signal. Specifically, according to the band energy data, the band features of this area are generated, such as calculating the mean, variance, or power spectral density of the band energy, etc.

[0050] Please refer to Figure 3 , Figure 3 which is the step flowchart of step S101 in the electroencephalogram signal processing method for psychoanalysis.

[0051] Optionally, in some embodiments, step S101 includes: S301. Generate multiple groups of test objects, where each group of test objects includes at least one test object.

[0052] S302. Collect electroencephalogram (EEG) signals for each test object respectively.

[0053] S303. For each test object, collect EEG signals through multiple acquisition stages to obtain an EEG signal group corresponding to each acquisition stage.

[0054] It can be understood that when dividing the test object groups, in the first test object group, an EEG signal is collected once after the subject has rested for 10 minutes after experiencing a stress event; in the second test object group, an EEG signal is collected once after the subject has rested for 5 minutes after experiencing a stress event; in the third test object group, an EEG signal is collected once without the subject resting after experiencing a stress event.

[0055] Please refer to Figure 4 , Figure 4 which is the flowchart of step S104 in the EEG signal processing method for psychological analysis.

[0056] Optionally, in some embodiments, step S104 includes: S401. Classify each target band signal based on the acquisition stage corresponding to the target band signal to obtain multiple band signal groups, where each band signal group includes at least one target band signal.

[0057] S402. Determine the average power value of each target band signal.

[0058] S403. Compare the average power values of the target band signals in the same frequency band in every two band signal groups to determine the power change difference of the EEG signals in the same frequency band at different acquisition stages.

[0059] In some embodiments, it includes 3 acquisition stages, namely pre - test, rest stage, and environmental exposure stage. By comparing the changes in EEG signals at different acquisition stages, especially the average power changes in each band, the changes in the psychophysiological state of the subject are evaluated. Further, the "difference" analysis method is adopted, that is, calculating the difference in EEG signals at different stages to more precisely detect the change differences in the brain waves of the subjects under different exposure durations.

[0060] Specifically, let the "average power value of the subject's EEG in the preparation stage" be , that is, in the preparation stage, the average power value of the subject's EEG signal.

[0061] Let the "average power value of the subject's EEG after stress" be , that is, during the pressure application phase, the average power value of the electroencephalogram (EEG) signal of the subject.

[0062] Let "the average EEG power value of the subjects in the 5-minute rest group" be , that is, in the 5-minute rest group, the average power value of the electroencephalogram (EEG) signal of the subject.

[0063] Let "the average EEG power value of the subjects in the 10-minute rest group" be , that is, in the 10-minute rest group, the average power value of the electroencephalogram (EEG) signal of the subject.

[0064] The "difference" analysis method is a method of analyzing data by calculating the differences between adjacent data points, and the differential method is used to calculate the change differences between the average EEG power values in each phase.

[0065] The power change difference from the preparation phase to the pressure application phase:

[0066] Among them, represents the difference in the average EEG power value between the preparation phase and the pressure application phase.

[0067] The power change difference from the pressure application phase to the 5-minute rest group:

[0068] Among them, represents the difference in the average EEG power value between the pressure application phase and the 5-minute rest group.

[0069] The power change difference from the pressure application phase to the 10-minute rest group:

[0070] Among them, represents the difference in the average EEG power value between the pressure application phase and the 10-minute rest group.

[0071] The power change difference from the preparation phase to the 5-minute rest group:

[0072] Among them, represents the difference in the average EEG power value between the preparation phase and the 5-minute rest group.

[0073] The power change difference from the preparation phase to the 10-minute rest group:

[0074] Among them, represents the difference in the average EEG power value between the preparation phase and the 10-minute rest group.

[0075] Power change difference from the 5 - minute rest group to the 10 - minute rest group:

[0076] Among them, represents the difference in the average EEG power values between the 5 - minute rest group and the 10 - minute rest group.

[0077] Please refer to Figure 5 , Figure 5 which is another flowchart of the steps of the psycho - analytical EEG signal processing method.

[0078] Optionally, in some embodiments, the psycho - analytical EEG signal processing method further includes: S501. Determine the total average value corresponding to each band signal group, where the total average value is the average of the sum of the average power values of all target band signals in the band signal group.

[0079] S502. When determining the degree of difference between the EEG signal power values corresponding to two different acquisition stages, compare the total average value corresponding to the first band signal group with the total average value corresponding to the second band signal group, and determine the degree of difference according to the comparison result.

[0080] In some embodiments, when determining the degree of difference between the EEG signal power values corresponding to two different acquisition stages, a comparative analysis is used to evaluate the significance of the EEG signal power change between different acquisition stages.

[0081] Specifically, the t - test method is used to compare whether the difference between different groups is significant, using to represent that the difference in the EEG signal power between the two acquisition stages is zero, to represent that there is a significant difference in the EEG signal power between the two acquisition stages.

[0082] Compare the differences between the preparation stage and the pressure - applying stage:

[0083] Compare the differences between the pressure - applying stage and the 5 - minute rest group:

[0084] Compare the differences between the pressure - applying stage and the 10 - minute rest group:

[0085] Compare the differences between the preparation stage and the 5 - minute rest group:

[0086] Compare the differences between the preparation stage and the 10 - minute rest group:

[0087] Compare the differences between the 5-minute rest group and the 10-minute rest group:

[0088] Through the above embodiments, the variation differences of the brain waves of the subjects under different exposure durations can be accurately detected. These analyses can help reveal the recovery effects of different environmental exposure times on the psycho-physiological states of the subjects, as well as their impacts on cognitive function and emotional recovery.

[0089] Please refer to Figure 6 , Figure 6 which is the flowchart of step S502 in the psychoanalysis electroencephalogram signal processing method.

[0090] Optionally, in some embodiments, step S502 includes: S601. Obtain the average power value datasets corresponding to the first acquisition stage and the second acquisition stage respectively. Each average power value dataset includes a sample quantity value and the sample mean and sample standard deviation values calculated from the average power value and the sample quantity value.

[0091] S602. After determining the prediction comparison result, determine the probability value of the prediction comparison result according to the sample quantity value, the sample mean, and the sample standard deviation value.

[0092] S603. If the probability value is less than the preset threshold, it is determined that the prediction comparison result is not valid and the actual comparison result is determined to have a significant difference. If the probability value is greater than or equal to the preset threshold, it is determined that the prediction comparison result is valid and equal to the actual comparison result, and the actual comparison result is determined to have no significant difference.

[0093] Optionally, in some embodiments, step S602 includes: Determine the statistic of the target test method according to the first functional relationship among the sample quantity value, the sample mean, and the sample standard deviation value; determine the degrees of freedom of the target test method according to the second functional relationship among the sample quantity value, the sample mean, and the sample standard deviation value; and determine the probability value based on the preset mapping relationship between the statistic and the degrees of freedom.

[0094] To illustrate the above embodiments of the specification, in a specific embodiment, the difference degree between the 5-minute rest group after pressure application and the 10-minute rest group after pressure application is compared by means of a T-test.

[0095] According to the obtained average power value dataset, the sample quantity value of the 5-minute rest group after pressure application is , and the sample quantity value of the 10-minute rest group after pressure application is ; The sample mean of the group with a 5-minute rest after pressure application is , and the sample mean of the group with a 10-minute rest after pressure application is ; The sample standard deviation of the group with a 5-minute rest after pressure application is , and the sample standard deviation of the group with a 10-minute rest after pressure application is .

[0096] The first prediction result : There is no significant difference in the EEG signal power values between the group with a 5-minute rest and the group with a 10-minute rest after pressure application, that is:

[0097] Among them, and are the total averages of the group with a 5-minute rest and the group with a 10-minute rest after pressure application, respectively.

[0098] The second prediction result : There is a significant difference in the EEG signal power values between the group with a 5-minute rest and the group with a 10-minute rest after pressure application, that is:

[0099] Calculate the statistic of the t-test to determine whether the null hypothesis holds. The formula is as follows:

[0100] Among them, and are the sample means of the two groups, and are the sample sizes of the two groups, is the combined sample standard deviation of the two groups, and the calculation formula is as follows:

[0101] Among them, and are the sample standard deviations of the two groups, respectively.

[0102] The degrees of freedom of the t-test is:

[0103]

[0104] The statistic obtained by calculation and the degrees of freedom , and obtain the probability value p of the corresponding prediction comparison result by looking up the t-distribution table. Then, according to the comparison between the probability value p of the prediction comparison result and the preset threshold of the significance level (for example, ), make a conclusion: If , then reject , believing that there is a significant difference in EEG power values between the two groups. The actual comparison results are as .

[0105] If , then it is impossible to reject , believing that the difference in EEG power values between the two groups is not significant. The actual comparison results are as .

[0106] Through the above specific embodiments, the power difference of electroencephalogram signals between the group resting for 5 minutes and the group resting for 10 minutes after applying pressure is quantitatively compared.

[0107] In one embodiment, the technical solution of the present application is applied to evaluate the cognitive function and psychophysiological recovery effect of students with respect to the exposure time in the campus environment.

[0108] First, the stages of this evaluation process are as follows:[[]]END]] (1) Preparation stage: Require participants to fill out a short demographic questionnaire and perform a 3 - minute eyes - closed rest to complete psychological adjustment.

[0109] (2) Stress - application stage: Use a computer in the laboratory to perform the Stroop task to apply stress.

[0110] (3) Pre - test stage of cognitive ability: First complete the Spatial Memory Span Test (SMST) as a pre - test before walking in the classroom corridor.

[0111] (4) Environmental exposure stage: Participants are randomly selected to walk on the classroom balcony. Participants wear electroencephalogram (EEG) devices at the same time and are divided into two groups, being exposed for 5 minutes or 10 minutes respectively.

[0112] (5) Post - test stage of cognitive ability: Second, complete the SMST as a post - test after walking in the classroom corridor.

[0113] (6) Environmental evaluation stage: Conduct spatial preference evaluation and other recovery benefit evaluations through questionnaires to obtain the subjective responses of participants. The stages for which electroencephalogram signal acquisition is required are (2), (3), (4), and (5).

[0114] In stage (3), the Spatial Memory Span Test (SMST) is adopted. This test is used to evaluate a person's short-term memory, attention, and the ability to manipulate information in the mind. The performance of the memory span test can provide insights into cognitive abilities and can be used in clinical assessments, educational settings, or research. The capacity of short-term memory is called the memory span, and its capacity is approximately 7 ± 2 chunks of information. The Spatial Memory Span Test (SMST) refers to the length of a series of spatial positions after presenting stimuli in a fixed order. Participants are shown a series of spatial positions or patterns and are required to reproduce the sequence in the same order or in reverse order. The Spatial Memory Span Test (SMST) is of great significance in practice and can be used as an indicator for professional ability assessment. Among them, the retrieval of these spatial positions is consistent with their original presentation.

[0115] In stage (4), the mental states of stress reduction, relaxation, and attention are focused on. A portable wearable electroencephalogram measurement recording system (BitBrain, 256Hz) is used for data collection. The common mode rejection ratio (CMRR) is -115 dB, and a 10–20 electrode system is adopted. The acquisition points for setting the measurement electrodes include FPZ in the prefrontal acquisition area, FZ, F3, and F4 in the prefrontal acquisition area, O1 and O2 in the occipital acquisition area, and P3 and P4 in the parietal acquisition area. Data processing is based on the design module of ErgoLAB 3.0.

[0116] The collected electroencephalogram signal dataset is processed by the ErgoLAB EEG analysis module, using high and low pass filtering (0.1~49Hz) and frequency domain index extraction. Among them, frequency bands including θ (4 - 8Hz), α (8 - 12Hz), and β (12 - 30Hz) are used.

[0117] Specifically, by detecting the change process of the α wave in the pre-test and rest stages, it is tested whether the Stroop task oppression is successful. The changes in the average correct response time and average response time before and after the Spatial Memory Span Test (SMST) are checked to determine whether the cognitive ability of the subject has improved. The changes in the α wave, β wave, and θ wave in the CVET stage compared with the pre-test stage are analyzed to test the relaxation effect of the mental state in the semi-outdoor space, and the changes in the β wave and θ wave in the CVET period compared with the post-test stage are analyzed to test the change in the mental state of the participants' attention. That is, in the electroencephalogram signal processing of this technical solution, in the relevant signal processing of the mental state of concentrating attention, the difference in the change of the β wave and the difference in the change of the θ wave before and after the SMST; in the relevant signal processing of the relaxed mental state, the difference in the change of the α wave, the difference in the change of the β wave, and the difference in the change of the θ wave in the "environmental exposure" stage compared with the pre-test stage of the SMST are used to explain the change in the "attention" band of the subjects, and based on this, a comparative analysis with the T-test as the target test method is carried out for different "exposure duration" groups.

[0118] It can be understood that any of the above embodiments can collect electroencephalogram signals through multiple stages, accurately divide the band signals corresponding to the frequency bands, and screen the band signals related to the psychological state to be measured; further, by calculating the difference between the target band signals and generating an evaluation result of the change in the psychological state based on the difference, the problems of rough evaluation results and difficulty in detecting subtle changes in the traditional technical solutions in psychological recovery research are solved. Through multi-stage collection and frequency band division, the resolution and pertinence of the signals are improved; by screening the target band signals, the information closely related to the psychological state is focused; by calculating the difference and generating an evaluation result of the change process, the dynamic monitoring of the psychological recovery effect is realized. The electroencephalogram signal processing method for psychological analysis of the present application can more accurately identify the changes in electroencephalogram signals, thereby improving the accuracy of psychological evaluation and providing strong support for a more comprehensive and objective evaluation of the psychological recovery effect.

[0119] Please refer to Figure 7 , Figure 7 which is a schematic structural diagram of an electroencephalogram signal processing device for psychological analysis.

[0120] This embodiment also provides an electroencephalogram signal processing device for psychological analysis, and the device includes: An acquisition module 701, configured to obtain electroencephalogram signals through multiple acquisition stages, and obtain an electroencephalogram signal group corresponding to each acquisition stage.

[0121] A frequency division module 702, configured to divide each electroencephalogram signal group into band signals corresponding to each frequency band based on the frequency band of the electroencephalogram signals.

[0122] A screening module 703, configured to determine band signals related to the psychological state to be measured from multiple band signals, so as to screen out multiple target band signals.

[0123] A calculation module 704, configured to determine the difference between every two target band signals.

[0124] An evaluation module 705, configured to generate an evaluation result of the change process of the psychological state to be measured based on multiple differences.

[0125] Those of ordinary skill in the art will understand that all or some of the steps and devices disclosed in the above methods can be implemented as software, firmware, hardware, and their appropriate combinations. Some physical components or all physical components can be implemented as software executed by a processor, such as a central processing unit, a digital signal processor, or a microprocessor, or implemented as hardware, or implemented as an integrated circuit, such as an application-specific integrated circuit. Such software can be distributed on a computer-readable medium, which can include a computer storage medium (or non-transitory medium) and a communication medium (or transitory medium). As is well known to those of ordinary skill in the art, the term computer storage medium includes volatile and non-volatile, removable and non-removable media implemented in any method or technology for storing information, such as computer-readable instructions, data structures, program modules, or other data. Computer storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technologies, CD-ROM, digital versatile disk (DVD) or other optical disk storage, magnetic cassettes, tapes, magnetic disk storage or other magnetic storage devices, or any other medium that can be used to store the desired information and can be accessed by a computer. It is well known to those of ordinary skill in the art that a communication medium typically contains computer-readable instructions, data structures, program modules, or other data in a modulated data signal such as a carrier wave or other transmission mechanism, and can include any information delivery medium.

[0126] It can be understood that the content in the above method embodiments is applicable to the device embodiments of the present application. The functions specifically implemented by the device embodiments of the present application are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those of the above method embodiments.

[0127] The embodiments of the present application further provide an electronic device, which includes a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, it implements the psychological analysis electroencephalogram signal processing method as described in any one of the above.

[0128] Reference Figure 8 , Figure 8 schematically shows the hardware structure of an electronic device in another embodiment. The electronic device includes: A processor 801, which can be implemented in a general-purpose CPU (Central Processing Unit), a microprocessor, an application-specific integrated circuit (ASIC), or one or more integrated circuits, etc., and is used to execute relevant programs to implement the technical solutions provided by the embodiments of the present application; The memory 802 can be implemented in the form of a read-only memory (ROM), a static storage device, a dynamic storage device, or a random access memory (RAM), etc. The memory 802 can store an operating device and other application programs. When implementing the technical solutions provided in the embodiments of this specification through software or firmware, the relevant program codes are stored in the memory 802, and the processor 801 is used to call and execute the psychoanalysis electroencephalogram signal processing method of the embodiments of this application; The input / output interface 803 is used to implement information input and output; The communication interface 804 is used to implement communication and interaction between this device and other devices. Communication can be achieved through wired means (such as USB, network cable, etc.) or wireless means (such as mobile network, WIFI, Bluetooth, etc.); The bus 805 transmits information between the various components of the device (such as the processor 801, the memory 802, the input / output interface 803, and the communication interface 804); Among them, the processor 801, the memory 802, the input / output interface 803, and the communication interface 804 achieve communication connections with each other inside the device through the bus 805.

[0129] It can be understood that the content in the above method embodiments is applicable to the embodiments of this electronic device. The functions specifically implemented by the embodiments of this electronic device are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those of the above method embodiments.

[0130] The embodiments of this application also provide a computer-readable storage medium, in which there is a program executable by a processor. When the program executable by the processor is executed by the processor, it is used to implement the psychoanalysis electroencephalogram signal processing method described in any one of the above specific embodiments.

[0131] The embodiments of this application also disclose a computer program product, including a computer program or computer instructions. The computer program or computer instructions are stored in a computer-readable storage medium. The processor of the computer device reads the computer program or computer instructions from the computer-readable storage medium, and the processor executes the computer program or computer instructions, so that the computer device executes the psychoanalysis electroencephalogram signal processing method described in any of the previous embodiments.

[0132] It can be understood that the content in the above method embodiments is applicable to the embodiments of this storage medium. The functions specifically implemented by the embodiments of this storage medium are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those of the above method embodiments.

[0133] In the description of the present application and the above-mentioned accompanying drawings, terms such as "first", "second", "third", "fourth", etc. (if any) are used to distinguish similar objects and do not necessarily describe a specific order or sequence. It should be understood that the data used in this way can be interchanged under appropriate circumstances so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, device, product, or apparatus that includes a series of steps or units does not necessarily have to be limited to those steps or units clearly listed, but may include other steps or units not clearly listed or inherent to these processes, methods, products, or apparatuses. It should be understood that in the present application, "at least one (item)" means one or more, and "a plurality" means two or more.

[0134] In several embodiments provided by the present application, it should be understood that the disclosed devices, apparatuses, and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the units is only a logical function division, and there may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another device, or some features can be ignored or not executed. Another point is that the displayed or discussed coupling, direct coupling, or communication connection between each other can be an indirect coupling or communication connection through some interfaces, devices, or units, and can be in electrical, mechanical, or other forms.

[0135] The units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place or distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0136] In addition, the functional units in each embodiment of the present application can be integrated into one processing unit, or each unit can exist physically alone, or two or more units can be integrated into one unit. The above-mentioned integrated units can be implemented in the form of hardware or in the form of software functional units.

[0137] When the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in various embodiments of this application. The aforementioned storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memories (ROM for short), random access memories (RAM for short), magnetic disks, or optical discs that can store program codes.

[0138] Although the description of this application has been quite detailed and has particularly described several of the described embodiments, it is not intended to be limited to any of these details or embodiments or any particular embodiment, but rather should be regarded as effectively covering the intended scope of this application by considering the prior art to provide a broad interpretation of these claims. In addition, the above description of this application with embodiments foreseeable by the inventors is for the purpose of providing a useful description, and those non-substantive modifications to this application that are not currently foreseeable may still represent equivalent modifications of this application.

Claims

1. A method for processing electroencephalogram signals for psychoanalysis, characterized in that, The method includes: Obtaining electroencephalogram (EEG) signals through multiple acquisition stages, and obtaining an EEG signal group corresponding to each of the acquisition stages; Based on the frequency bands of the EEG signals, dividing each of the EEG signal groups into band signals corresponding to each of the frequency bands; Determining the band signals related to the psychological state to be measured from the multiple band signals, so as to screen out multiple target band signals; Determining the difference between every two of the target band signals; Generating an evaluation result of the change process of the psychological state to be measured based on the multiple differences.

2. The method for processing electroencephalogram signals for psychological analysis according to claim 1, characterized in that, The method further includes: Based on the anatomical structure of the brain, determining the acquisition regions for acquiring the EEG signals, and each of the acquisition regions includes multiple acquisition points; Generating a signal data set corresponding to each of the acquisition points based on the EEG signals acquired at each of the acquisition points, and each of the signal data sets includes frequency band energy data; Based on the frequency band energy data corresponding to each of the acquisition points within the same acquisition region, determining the frequency band energy data corresponding to the acquisition region, and generating frequency band features according to the frequency band energy data; Determining the frequency bands of the EEG signals according to the frequency band features corresponding to each of the acquisition regions.

3. A method for processing electroencephalogram signals for psychological analysis according to claim 1, characterized in that, The step of obtaining EEG signals through multiple acquisition stages and obtaining an EEG signal group corresponding to each of the acquisition stages includes: Generating multiple groups of test objects, and each of the groups of test objects includes at least one test object; Respectively acquiring EEG signals for each of the test objects; For each of the test objects, acquiring the EEG signals through multiple acquisition stages to obtain an EEG signal group corresponding to each of the acquisition stages.

4. A method for processing electroencephalogram signals for psychological analysis according to claim 1, characterized in that, The step of determining the difference between every two of the target band signals includes: Classifying each of the target band signals based on the acquisition stage corresponding to the target band signal to obtain multiple band signal groups, and each of the band signal groups includes at least one of the target band signals; Determining the average power value of each of the target band signals; Comparing the average power values of the target band signals in the same frequency band in every two of the band signal groups to determine the power change difference of the EEG signals in the same frequency band at different acquisition stages.

5. A method for processing electroencephalogram signals for psychological analysis according to claim 4, characterized in that The method further includes: Determining the total average value corresponding to each of the band signal groups, where the total average value is the average value of the sum of the average power values of all the target band signals in the band signal group; When determining the degree of difference between the EEG signal power values corresponding to two different acquisition stages, comparing the total average value corresponding to the first band signal group with the total average value corresponding to the second band signal group, and determining the degree of difference according to the comparison result.

6. The method for processing electroencephalogram signals for psychological analysis according to claim 5, characterized in that, The step of when determining the degree of difference between the EEG signal power values corresponding to two different acquisition stages, comparing the total average value corresponding to the first band signal group with the total average value corresponding to the second band signal group, and determining the degree of difference according to the comparison result includes: Obtain the average power value datasets corresponding to the first acquisition stage and the second acquisition stage respectively. Each of the average power value datasets includes a sample quantity value, and a sample mean value and a sample standard deviation value calculated from the average power value and the sample quantity value. After determining the prediction comparison result, determine the probability value of the prediction comparison result according to the sample quantity value, the sample mean value, and the sample standard deviation value. If the probability value is less than the preset threshold, it is determined that the prediction comparison result is not established and the actual comparison result is determined to have a significant difference. If the probability value is greater than or equal to the preset threshold, it is determined that the prediction comparison result is established and equal to the actual comparison result, and the actual comparison result is determined to have no significant difference.

7. The psychoanalysis electroencephalogram signal processing method according to claim 6, characterized in that The step of determining the probability value of the prediction comparison result according to the sample quantity value, the sample mean value, and the sample standard deviation value includes: Determine the statistic of the target test method according to the first functional relationship among the sample quantity value, the sample mean value, and the sample standard deviation value. Determine the degree of freedom of the target test method according to the second functional relationship among the sample quantity value, the sample mean value, and the sample standard deviation value. Based on the preset mapping relationship between the statistic and the degree of freedom, determine the probability value.

8. A psychological analysis electroencephalogram signal processing device, characterized in that, The device includes: An acquisition module, configured to obtain electroencephalogram signals through multiple acquisition stages, and obtain an electroencephalogram signal group corresponding to each acquisition stage. A frequency division module, configured to divide each electroencephalogram signal group into band signals corresponding to each frequency band based on the frequency band of the electroencephalogram signals. A screening module, configured to determine the band signals related to the psychological state to be measured from multiple band signals, so as to screen out multiple target band signals. A calculation module, configured to determine the difference between every two of the target band signals. An evaluation module, configured to generate an evaluation result of the change process of the psychological state to be measured based on multiple differences.

9. An electronic device, characterized in that, The electronic device includes a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, it implements the method for processing electroencephalogram signals for psychological analysis according to any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the method for processing electroencephalogram signals for psychological analysis according to any one of claims 1 to 7.

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