An electroencephalogram analysis method based on LPP

Abstract

The application discloses a brain electrical analysis method based on LPP, comprising the following steps: showing a resting state picture to a subject according to a preset relaxation time; showing a plurality of experimental picture groups to the subject in sequence according to a preset experiment time, receiving and recording an input negative emotion intensity evaluation value of the subject after each experimental picture group is shown, and showing a next experimental picture group after a black screen display according to a preset black screen duration; each experimental picture group comprises a resting state picture and a stimulus picture arranged in sequence, and the stimulus picture is a social exclusion picture or a physiological pain picture; collecting brain electrical signals of the subject in the preset relaxation time and the preset experiment time; dividing a plurality of sliding time windows within 300 milliseconds after the stimulus picture appears to the preset stimulus time according to the brain electrical signals, quantifying average amplitudes of LPP of each electrode channel of the subject in the sliding time windows, and comparing; sampling and comparing the brain electrical signals within the preset stimulus time after the stimulus picture appears; and LPP quantification is performed again.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of electroencephalogram analysis, and particularly relates to an electroencephalogram analysis method based on LPP. BACKGROUND

[0002] Major depressive disorder (MDD) is one of the most common mental disorders, and its core symptoms are significant and persistent low mood, reduced interest, and the like. Non-suicidal self-injury (NSSI) refers to intentionally damaging one's own body tissue without suicidal intent, and its common forms include cutting the skin or wrists, beating and burning oneself, and the like. Adolescence is a critical period for the occurrence of NSSI behavior, and recent studies have shown that about 18.6-44.8% of adolescents will participate in NSSI behavior every year, making NSSI an important public health problem. NSSI behavior is usually discussed in the context of mental illness disorders, such as major depressive disorder and borderline personality disorder. It is worth noting that about 41.6% of adolescents with NSSI are diagnosed with MDD, and the cognitive function of adolescents with MDD who have NSSI behavior is significantly impaired compared with adolescents with MDD who do not have NSSI behavior, mainly manifested as memory loss, lack of concentration, and decreased executive function. At the same time, NSSI is also a risk factor for future suicide attempts and completed suicides, and in some cases, NSSI is a better predictor of suicide attempts than past self-injurious behavior. Therefore, it is crucial to fully study adolescents with depression and self-injury in order to provide information for treatment and prevention work. However, the accuracy of the existing electroencephalogram research results of NSSI behavior of adolescents with depression and self-injury is low. SUMMARY

[0003] The technical problem to be solved by the present application is to provide an electroencephalogram analysis method based on LPP to improve the accuracy of electroencephalogram analysis of NSSI behavior.

[0004] In order to solve the above technical problems, the purpose of the present invention is to achieve the following technical solutions: to provide an LPP-based EEG analysis method, comprising the following steps: resting state picture display: displaying resting state pictures to the subject according to a preset relaxation time; experimental picture group display: displaying several experimental picture groups to the subject in sequence according to the preset experimental time, and prompting the subject to input a negative emotion intensity evaluation value after each experimental picture group is displayed, after receiving and recording the negative emotion intensity evaluation value input by the subject or after a preset evaluation time has passed, a black screen is displayed according to a preset black screen duration, and the next experimental picture group is displayed after the black screen is displayed for the preset black screen duration until all experimental picture groups have completed the preset display times; wherein each experimental picture group includes a resting state picture and a stimulation picture arranged in sequence, and the stimulation picture is a social rejection picture or a physical pain picture; the preset experimental time is the total time of the display time of all experimental picture groups, all preset evaluation times and all preset black screen durations; EEG signal acquisition : Collect the EEG signals of the subjects during the preset relaxation time and the preset experimental time; Stimulation picture LPP comparison: According to the EEG signals, several sliding time windows are divided within the time period from 300 milliseconds after the stimulation picture appears to the preset stimulation time, and the average amplitude of the LPP of each electrode channel of the subject within the sliding time window is quantified. The LPP corresponding to each subject watching the social exclusion pictures and the physical pain pictures are compared to obtain the stimulation picture LPP comparison result; Stimulation picture EEG signal amplitude point-by-point sampling comparison: The EEG signal is sampled according to the amplitude of the EEG signal within the preset stimulation time after the stimulation picture appears, and the amplitude of the EEG signal corresponding to each subject watching the social exclusion pictures and the physical pain pictures is compared according to each sampling point to obtain the stimulation picture EEG signal amplitude point-by-point sampling comparison result; LPP quantification again: According to the stimulation picture LPP comparison result and the stimulation picture EEG signal amplitude point-by-point sampling comparison result, the electrode channel cluster and time window are divided, and the average amplitude of the LPP on the electrode channel cluster within the time window is quantified.

[0005] The beneficial technical effects of the present invention are as follows: the LPP-based EEG analysis method of the present invention displays resting-state pictures to the subject according to a preset relaxation time; displays several experimental picture groups to the subject in sequence according to a preset experimental time; collects the EEG signals of the subject during the preset relaxation time and the preset experimental time; divides the time period from 300 milliseconds after the stimulation picture appears to the preset stimulation time into several sliding time windows, quantifies the average amplitude of the LPP of each electrode channel of the subject within the sliding time window, compares the LPP corresponding to each subject viewing social exclusion pictures and physical pain pictures, and obtains the stimulation picture LPP comparison result; samples the EEG signal according to the amplitude of the EEG signal within the preset stimulation time after the stimulation picture appears, compares the amplitude of the EEG signal corresponding to each subject viewing social exclusion pictures and physical pain pictures according to each sampling point, and obtains the stimulation picture EEG signal amplitude point-by-point sampling comparison result; divides the electrode channel cluster and the time window according to the stimulation picture LPP comparison result and the stimulation picture EEG signal amplitude point-by-point sampling comparison result, and quantifies the average amplitude of the LPP on the electrode channel cluster within the time window. Acquiring LPP can more accurately analyze the EEG signals of NSSI behavior and is beneficial to the study of emotion regulation and affective disorders. BRIEF DESCRIPTION OF THE DRAWINGS

[0006] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0007] Figure 1 A schematic diagram of the process of the LPP-based EEG analysis method provided in an embodiment of the present invention;

[0008] Figure 2 A schematic diagram of a sub-process of the LPP-based EEG analysis method provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0009] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0010] See also Figure 1 As shown, Figure 1 A schematic flow chart of an LPP-based EEG analysis method according to an embodiment of the present invention is provided. The LPP-based EEG analysis method includes the following steps:

[0011] Step S10, Resting State Image Display: The subject is presented with a resting state image based on a preset relaxation time. The preset relaxation time may be five minutes. The resting state image is an image that puts the subject in a resting state, which is a state when the brain is not performing specific cognitive tasks, remaining quiet, relaxed, and alert. The resting state image may be an image with a solid color background and a crosshair cursor in the center.

[0012] Step S20, displaying experimental image groups: Several experimental image groups are sequentially displayed to the subject according to a preset experimental time. After each experimental image group is displayed, the subject is prompted to enter a negative emotion intensity assessment value. After receiving and recording the negative emotion intensity assessment value entered by the subject, or after the preset assessment time has elapsed, a black screen is displayed according to a preset black screen duration. After the black screen has been displayed for the preset black screen duration, the next experimental image group is displayed until all experimental image groups have been displayed for the preset number of times. Each experimental image group includes a resting image and a stimulating image, which are sequentially arranged. The stimulating image refers to an image that stimulates the subject, and the stimulating image may be a social rejection image or a physical pain image. The preset experimental time is the sum of the display time of all experimental image groups, the preset assessment time, and the preset black screen duration. The preset assessment time refers to the preset time for waiting for the subject to enter a negative emotion intensity assessment value. The preset number of displays can be two. Of course, in some embodiments, the preset number of displays can be one, three, or more. The total number of experimental picture groups may be seventy, and the number of experimental picture groups whose stimulus pictures are social rejection pictures and the number of experimental picture groups whose stimulus pictures are physical pain pictures may be thirty-five respectively.

[0013] Step S30, EEG signal acquisition: EEG signals of the subject are collected during the preset relaxation time and the preset experimental time; wherein, EEG signal acquisition can be performed synchronously during the display of the resting-state pictures and the display of the experimental picture group, so as to collect the EEG signals of the subject in real time while viewing the resting-state pictures during the preset relaxation time and the experimental picture group during the preset experimental time.

[0014] Step S40, LPP comparison of stimulus images: Based on the EEG signals, a sliding time window is divided within the time period from 300 milliseconds after the stimulus image appears to the preset stimulus time, and the average amplitude of the LPP of each electrode channel of the subject within the sliding time window is quantified. The LPP corresponding to each subject viewing the social rejection image and the physical pain image is compared to obtain the stimulus image LPP comparison results;

[0015] The preset stimulation time is the duration of the stimulus image presentation, which can be 5 seconds. Dividing the time window into a number of sliding windows between 300 milliseconds after the stimulus image appears and the preset stimulation time refers to dividing the time window into a number of sliding windows between 300 milliseconds and 5 seconds after the stimulus image appears. The event-related potential (ERP) is a special type of brain-evoked potential and an important indicator of neuroelectrophysiological changes in the brain during emotion regulation. Event-related potential analysis uses scalp EEG to reflect neuroelectrophysiological changes in the cognitive process, using an average superposition method to time-lock the dynamic process of cognition. The late positive potential (LPP) is a component of the ERP that typically begins approximately 300 milliseconds after stimulus presentation and persists throughout the stimulus presentation process. It is a neurophysiological indicator for studying emotional responses and an event-related potential that is sensitive to the regulation of emotional responses. The LPP is primarily generated in the central and posterior regions of the scalp. It is a positive component that typically appears on the EEG as an increase in positive scalp potential. Compared with traditional ERP components such as N1, EPN or P3, LPP can reflect the brain's sustained and in-depth processing of significant stimuli.

[0016] Step S50, point-by-point sampling and comparison of the EEG signal amplitude of the stimulation picture: The EEG signal is sampled according to the amplitude of the EEG signal within the preset stimulation time after the stimulation picture appears, and the amplitude of the EEG signal corresponding to each subject viewing the social exclusion picture and the physical pain picture is compared according to each sampling point to obtain the point-by-point sampling and comparison result of the EEG signal amplitude of the stimulation picture; wherein the sampling frequency is 500 Hz.

[0017] Step S60, re-LPP quantification: divide the electrode channel clusters and time windows according to the LPP comparison results of the stimulation image and the point-by-point sampling comparison results of the EEG signal amplitude of the stimulation image, and quantify the average amplitude of the LPP on the electrode channel cluster within the time window.

[0018] The LPP-based EEG analysis method can more accurately analyze the EEG signals of NSSI behavior by acquiring LPP, and is beneficial to the research of emotion regulation and emotion disorders.

[0019] Preferably, the quantification in step S40 and step S60 is statistically analyzed using a two-factor mixed-effects analysis of variance method, wherein the subjects may include two groups: a depression-with-self-injury group and a depression-without-self-injury group, the group of subjects is a between-subject factor, the category of the stimulus picture is a within-subject factor, and the gender and age of the subjects are covariates.

[0020] Combine Figure 2 , the step S20 specifically includes:

[0021] Step S21: Displaying a resting state image according to a preset resting state display time; wherein the preset resting state display time may be 2 seconds;

[0022] Step S22: displaying a stimulation picture according to a preset stimulation time;

[0023] Step S23: After the stimulus image is displayed, the subject is prompted to enter a negative emotion intensity assessment value based on a preset assessment time. The negative emotion intensity assessment value ranges from 1 to 9, with 1 indicating no negative emotion and 9 indicating strong negative emotion. That is, the higher the negative emotion intensity assessment value, the stronger the negative emotion. The subject can enter the negative emotion intensity assessment value using a keyboard or by selecting from a screen displaying different negative emotion intensity assessment values.

[0024] Step S24: After receiving and recording the negative emotion intensity evaluation value input by the subject or after a preset evaluation time has passed, a black screen is displayed according to a preset black screen duration;

[0025] Step S25: After the black screen is displayed for a preset black screen duration, it is determined whether there is an experimental image group that has not been displayed for a preset number of times;

[0026] Step S26: If there is no experimental image group that has not been displayed for the preset number of times, all experimental image groups are displayed for the preset number of times and the display ends.

[0027] Specifically, after step S25, the method further includes:

[0028] If there are experimental image groups that have not been displayed for the preset number of times, steps S21 to S25 are repeated until all experimental image groups have been displayed for the preset number of times.

[0029] Specifically, before step S10, the method further includes:

[0030] Social exclusion pictures and physical pain pictures are selected from a database, and the selected social exclusion pictures and physical pain pictures are stored as stimulus pictures.

[0031] Preferably, the step S30 is specifically as follows:

[0032] An electrode cap with 64 silver or silver chloride channels was used to collect and record the subjects' EEG signals during the preset relaxation time and the preset experimental time through the BrainAmp amplifier and BrainVisionRecorder software; the sampling frequency was 500 Hz.

[0033] Specifically, after step S30, the method further includes:

[0034] Perform offline preprocessing on the collected EEG signals to obtain preprocessed EEG signals. Preprocessing the raw EEG signals improves signal quality and more accurately reflects brain activity and status. Letswave can be used for offline preprocessing of EEG signals.

[0035] Preferably, the step of performing offline preprocessing on the collected EEG signal to obtain the preprocessed EEG signal specifically includes:

[0036] The eye movement channel of the collected EEG signal is deleted, and the bad channel is interpolated to obtain the interpolated EEG signal;

[0037] The interpolated EEG signal is filtered using a bandpass filter and a notch filter; the bandpass frequency of the bandpass filter is 1-45 Hz, and the notch frequency of the notch filter is 50 Hz.

[0038] The interpolated EEG signals before and after filtering are respectively extracted from the time period 0.5 seconds before the start of the experimental picture group display to 0.5 seconds after the end of the experimental picture group display for each experimental picture group as the pre-filtered stimulation EEG signal segment and the post-filtered stimulation EEG signal segment; wherein, the experimental picture group display includes the resting state picture display and the stimulation picture display, then 0.5 seconds before the start of the experimental picture group display is 0.5 seconds before the start of the preset resting state display time, and 0.5 seconds after the end of the experimental picture group display is 0.5 seconds after the end of the stimulation picture display. Extracting the signal segments from the time period 0.5 seconds before the start of the experimental picture group display to 0.5 seconds after the end of the experimental picture group display for each experimental picture group can ensure that the extracted signal segments are relatively complete for the stimulation process. The interpolated EEG signals before and after filtering refer to the interpolated EEG signals before filtering and the interpolated EEG signals after filtering. The pre-filtered stimulation EEG signal segment is extracted from the pre-filtered interpolated EEG signals, and the post-filtered stimulation EEG signal segment is extracted from the post-filtered interpolated EEG signals.

[0039] An independent component analysis algorithm is used to process the obtained filtered stimulation EEG signal segments to obtain an independent component analysis matrix of the filtered stimulation EEG signal segments;

[0040] Assigning the obtained independent component analysis matrix of the filtered stimulation EEG signal segment to the pre-filter stimulation EEG signal segment to obtain a comprehensive stimulation EEG signal segment;

[0041] Visually inspect the integrated stimulation EEG signal segments to remove artifacts caused by eye and muscle activity to obtain artifact-free stimulation EEG signal segments;

[0042] All the artifact-free EEG signal segments were subjected to bandpass filtering and notch filtering to obtain artifact-free filtered EEG signal segments; wherein the bandpass frequency of the bandpass filter was 0.01-45 Hz, and the notch frequency of the notch filter was 50 Hz.

[0043] Re-reference the anti-aliased filtered stimulation EEG signal segment based on the average amplitude of the left mastoid electrode and the right mastoid electrode to obtain the anti-aliased filtered reference stimulation EEG signal segment;

[0044] The anti-aliased filtered reference stimulation EEG signal segment within 0.5 seconds before the stimulation picture is displayed is used as the baseline. The anti-aliased filtered reference stimulation EEG signal segment is baseline-corrected to obtain the preprocessed EEG signal.

[0045] Specifically, in step S20, if the stimulus images of the adjacent experimental image groups are different in category, then the stimulus images of one of the adjacent experimental image groups are social rejection images, while the stimulus images of the other experimental image group are physical pain images. The adjacent experimental image groups refer to the experimental image groups corresponding to two adjacent presentations of the sequentially presented experimental image groups.

[0046] In summary, the LPP-based EEG analysis method of the present invention is as follows: showing a resting-state picture to the subject according to a preset relaxation time; showing several experimental picture groups to the subject in sequence according to a preset experimental time; collecting the EEG signals of the subject during the preset relaxation time and the preset experimental time; dividing the time period from 300 milliseconds after the stimulation picture appears to the preset stimulation time into several sliding time windows according to the EEG signals, quantifying the average amplitude of the LPP of each electrode channel of the subject within the sliding time window, comparing the LPP corresponding to the social exclusion pictures and the physical pain pictures viewed by each subject, and obtaining the stimulation picture LPP comparison result; sampling the EEG signals according to the amplitude of the EEG signals within the preset stimulation time after the stimulation picture appears, comparing the amplitude of the EEG signals corresponding to the social exclusion pictures and the physical pain pictures viewed by each subject according to each sampling point, and obtaining the stimulation picture EEG signal amplitude point-by-point sampling comparison result; dividing the electrode channel clusters and time windows according to the stimulation picture LPP comparison result and the stimulation picture EEG signal amplitude point-by-point sampling comparison result, and quantifying the average amplitude of the LPP on the electrode channel clusters within the time window. Acquiring LPP can more accurately analyze the EEG signals of NSSI behavior and is beneficial to the study of emotion regulation and affective disorders.

[0047] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in the present invention, and such modifications or substitutions are intended to be within the scope of protection of the present invention. Therefore, the scope of protection of the present invention shall be subject to the scope of protection of the claims.

Claims

1. An electroencephalogram analysis method based on LPP, characterized in that: The following steps are involved: Resting state picture display: resting state pictures are displayed to the subjects according to the preset relaxation time; Experimental picture group display: several experimental picture groups are displayed to the subject in sequence according to the preset experimental time, and after each experimental picture group is displayed, the subject is prompted to input a negative emotion intensity assessment value, after receiving and recording the negative emotion intensity assessment value input by the subject or after the preset assessment time has passed, a black screen is displayed according to the preset black screen duration, and after the black screen is displayed for the preset black screen duration, the next experimental picture group is displayed until all experimental picture groups have been displayed for the preset number of times; wherein each experimental picture group includes a resting state picture and a stimulation picture arranged in sequence, and the stimulation picture is a social rejection picture or a physical pain picture; the preset experimental time is the total time of the display time of all experimental picture groups, all preset assessment times and all preset black screen durations; EEG signal acquisition: collect EEG signals of subjects during preset relaxation time and preset experimental time; Comparison of LPPs for stimulus images: Based on the EEG signals, several sliding time windows were divided from 300 milliseconds after the stimulus image appeared to the preset stimulus time. The average amplitude of the LPPs for each electrode channel in the whole brain of the subject within the sliding time windows was quantified. The LPPs corresponding to the social exclusion images and the physical pain images of each subject were compared to obtain the LPP comparison results for the stimulus images; Point-by-point sampling and comparison of EEG signal amplitudes of stimulus images: The EEG signal is sampled according to its amplitude within the preset stimulation time after the stimulus image appears. The EEG signal amplitudes corresponding to each subject viewing the social rejection images and the physical pain images are compared at each sampling point to obtain the point-by-point sampling and comparison results of the EEG signal amplitudes of the stimulus images; LPP re-quantification: The electrode channel clusters and time windows are divided according to the LPP comparison results of the stimulation image and the point-by-point sampling comparison results of the EEG signal amplitude of the stimulation image, and the average amplitude of the LPP on the electrode channel cluster within the time window is quantified; The steps shown in the experimental diagram group specifically include: Display the resting state picture according to the preset resting state display time; Display stimulus pictures according to the preset stimulus time; After the stimulus picture is displayed, the subject is prompted to enter the negative emotion intensity assessment value according to the preset assessment time; After receiving and recording the negative emotion intensity evaluation value input by the subject or after the preset evaluation time has passed, the screen is displayed in black according to the preset black screen duration; After the black screen is displayed for a preset black screen duration, it is determined whether there is an experimental image group that has not been displayed for a preset number of times; If there is no experimental image group that has not been displayed for the preset number of times, all experimental image groups will be displayed for the preset number of times and the display will end; After the step of collecting the EEG signal, the method further includes: Performing offline preprocessing on the collected EEG signals to obtain preprocessed EEG signals; The step of performing offline preprocessing on the collected EEG signal to obtain the preprocessed EEG signal comprises: Delete the eye movement channel of the acquired EEG signal and interpolate the bad channel; The interpolated EEG signal is filtered using a bandpass filter and a notch filter; Extract the signal segments from the interpolated EEG signals before and after filtering, which are from 0.5 seconds before the start of the experimental image group display to 0.5 seconds after the end of the experimental image group display, as the EEG signal segments before and after filtering and the EEG signal segments after filtering; An independent component analysis algorithm is used to process the obtained filtered stimulation EEG signal segments to obtain an independent component analysis matrix of the filtered stimulation EEG signal segments; Assigning the obtained independent component analysis matrix of the filtered stimulation EEG signal segment to the pre-filter stimulation EEG signal segment to obtain a comprehensive stimulation EEG signal segment; Visually inspect the integrated stimulation EEG signal segments to remove artifacts caused by eye and muscle activity to obtain artifact-free stimulation EEG signal segments; Performing bandpass filtering and notch filtering on all the artifact-free EEG signal segments to obtain artifact-free filtered EEG signal segments; Re-reference the anti-aliased filtered stimulation EEG signal segment based on the average amplitude of the left mastoid electrode and the right mastoid electrode to obtain the anti-aliased filtered reference stimulation EEG signal segment; The anti-aliased filtered reference stimulation EEG signal segment within 0.5 seconds before the stimulation picture is displayed is used as the baseline. The anti-aliased filtered reference stimulation EEG signal segment is baseline-corrected to obtain the preprocessed EEG signal.

2. The LPP-based EEG analysis method according to claim 1, characterized in that: In the step of filtering the interpolated EEG signal using a bandpass filter and a notch filter, the bandpass frequency range of the bandpass filter is 1-45 Hz, and the notch frequency of the notch filter is 50 Hz.

3. The LPP-based EEG analysis method according to claim 1, characterized in that: In the step of performing bandpass filtering and notch filtering on all the removed pseudo-stimulation EEG signal segments to obtain the removed pseudo-filtered stimulation EEG signal segments, the bandpass frequency range of the bandpass filtering is 0.01-45 Hz, and the notch frequency of the notch filtering is 50 Hz.

4. The LPP-based EEG analysis method according to claim 1, characterized in that: In the step of displaying the experimental image groups, the categories of the stimulus images of the adjacent experimental image groups are different.

5. The LPP-based EEG analysis method according to claim 1, characterized in that: The step of displaying the resting state image also includes: Social exclusion pictures and physical pain pictures are selected from a database, and the selected social exclusion pictures and physical pain pictures are stored as stimulus pictures.

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