Method and device for studying instinctive fear based on EEG characteristics

Abstract

The present embodiment provides a method for studying instinctive fear based on EEG features, comprising: outputting different stimulation signals, wherein different stimulation signals correspond to different fear stimulus intensities; obtaining a subject's original EEG signal, performing noise reduction processing on the original EEG signal to obtain an EEG signal; performing time-frequency analysis on the EEG signal to obtain EEG information; extracting EEG features from the EEG information; and analyzing the EEG features to obtain results of the instinctive fear study. Also provided is a device for studying instinctive fear based on EEG features. The experimental design for studying human instinctive fear provided by the present embodiment can provide in-depth research into the mechanisms of human instinctive fear.

Description

Technical Field

[0001] The present application relates to the field of psychology, and more specifically, to a method and device for studying instinctive fear based on EEG characteristics. Background Art

[0002] Human fear is one of the most important and essential emotions in the evolution and survival of species. External fear stimuli can trigger specific defensive behaviors in an individual organism, thus playing a vital role in its survival and reproduction. Fear can be divided into conditioned fear and instinctive fear. Instinctive fear is a behavior that can be developed without learning.

[0003] At present, the research on instinctive fear response mainly focuses on the experimental design of instinctive fear behavior in animals (such as mice, insects, etc.). By stimulating experimental animals with fear, the reaction of experimental animals to fear stimulation is observed (for example, mice will run away when faced with fear stimulation), and then explore and understand the impact of fear on animals.

[0004] However, the study of humans' instinctive fear reactions is more complicated than the study of animals' instinctive fear reactions. Different types of fear stimuli have different effects on humans. When exposed to fear stimuli, different people will react differently. Some people's reactions to fear stimuli may be difficult to observe. Therefore, it is difficult to study humans' instinctive fear reactions from a behavioral perspective. In other words, experimental designs for animals' instinctive fear reactions are not suitable for studying humans' instinctive fear reactions.

[0005] Since there is no experimental design for human instinctive fear and a lack of reliable experimental data to study human instinctive fear reactions, various questions about instinctive fear, such as the relationship between fear stimuli and human instinctive fear, and the physical reactions of humans in a state of instinctive fear, remain unanswered. Summary of the Invention

[0006] The embodiments of this application provide a method, apparatus, electronic device, and storage medium for studying instinctive fear based on EEG characteristics, which can address the problem of the lack of experimental paradigms for studying instinctive fear responses in human subjects in the related art. The technical solution is as follows:

[0007] According to one aspect of an embodiment of the present application, a method for studying instinctive fear based on EEG features includes: outputting different stimulation signals, wherein different stimulation signals correspond to different fear stimulation intensities; obtaining the original EEG signal of the subject, and performing noise reduction processing on the original EEG signal to obtain an EEG signal; obtaining EEG information based on time-frequency analysis of the EEG signal; extracting EEG features from the EEG information, wherein the EEG features are used to indicate changes in EEG signals when the subject is in an instinctive fear state of different degrees under stimulation of different stimulation signals; and performing analysis based on the EEG features to obtain instinctive fear research results, wherein the instinctive fear research results are used to indicate changes in brain activity when the subject is in an instinctive fear state.

[0008] According to one aspect of an embodiment of the present application, a device for studying instinctive fear based on EEG features includes: a signal output module for outputting different stimulation signals, wherein different stimulation signals correspond to different fear stimulation intensities; a signal processing module for acquiring the original EEG signal of the subject, performing noise reduction processing on the original EEG signal, and obtaining the EEG signal; a time-frequency analysis module for obtaining EEG information based on time-frequency analysis of the EEG signal; a feature extraction module for extracting EEG features from the EEG information, wherein the EEG features are used to indicate changes in EEG signals when the subject is in an instinctive fear state under stimulation of different stimulation signals; and a feature analysis module for performing analysis based on the EEG features to obtain instinctive fear research results, wherein the instinctive fear research results are used to indicate changes in brain activity when the subject is in an instinctive fear state.

[0009] The beneficial effects of the technical solution provided by this application are:

[0010] In the above technical scheme, an experimental paradigm for studying the instinctive fear response of human subjects designed in this scheme is used, and stimulation signals of different fear stimulation types are used to obtain the original EEG signals generated by the subjects under different stimulation signals. The EEG features are extracted after processing the original EEG signals to explore the effects of different fear stimulations on brain activities, and then the various changes produced in the human brain when in a state of instinctive fear are studied, so as to understand the mechanism of human instinctive fear. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following briefly introduces the drawings required for describing the embodiments of the present application.

[0012] Figure 1 is a schematic diagram of an implementation environment involved in an embodiment of the present application;

[0013] Figure 2This is a flow chart showing a method for studying instinctive fear based on EEG features according to an exemplary embodiment;

[0014] Figure 3 yes Figure 2 The steps following step 310 in the corresponding embodiment are a flowchart of an embodiment;

[0015] Figure 4 yes Figure 3 The steps following step 430 in the corresponding embodiment are a flowchart of an embodiment;

[0016] Figure 5 yes Figure 2 A flowchart of an embodiment corresponding to step 310 in an embodiment;

[0017] Figure 6 yes Figure 5 A flowchart of step 330 in one embodiment corresponding to the embodiment;

[0018] Figures 7 to 9 This is a schematic diagram of a specific implementation of a method for studying instinctive fear based on EEG characteristics in an application scenario;

[0019] Figures 10 to 12 It is the EEG information obtained by a method of studying instinctive fear based on EEG characteristics in an application scenario;

[0020] Figure 13 This is a structural block diagram of a device for studying instinctive fear based on EEG features according to an exemplary embodiment;

[0021] Figure 14 The figure is a structural block diagram of an electronic device according to an exemplary embodiment. DETAILED DESCRIPTION

[0022] The following describes in detail embodiments of the present application, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present application, and are not to be construed as limiting the present application.

[0023] It will be understood by those skilled in the art that, unless expressly stated otherwise, the singular forms "a", "an", "said" and "the" used herein may also include the plural forms. It should be further understood that the term "comprising" used in the specification of the present application refers to the presence of the features, integers, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or groups thereof. It should be understood that when we refer to an element as being "connected" or "coupled" to another element, it may be directly connected or coupled to the other element, or there may be intermediate elements. In addition, "connected" or "coupled" as used herein may include wireless connections or wireless couplings. The term "and / or" used herein includes all or any units and all combinations of one or more associated listed items.

[0024] As mentioned above, there is currently a lack of experimental designs for human instinctive fear, and there is no reliable experimental data to study human instinctive fear responses.

[0025] Different people react differently to fear. When faced with the same fear stimulus, some people's reactions are easily observed, while others are very subtle and difficult to observe. Therefore, it is inappropriate to use people's reactions to fear stimuli as the basis for studying instinctive fear. In other words, the experimental indicators used to study instinctive fear should reduce the subjective influence of humans. The choice of experimental indicators as the research basis is of great significance to the study of instinctive fear.

[0026] Different types of fear stimuli may also cause different instinctive fear reactions. For example, the intensity of the human instinctive fear reaction caused by a car suddenly crashing or a dog suddenly rushing out may vary. Therefore, the choice of fear stimulus is also of great significance for the experimental design of instinctive fear.

[0027] To this end, the present application provides an instinctive fear research method based on EEG features. By designing an experimental paradigm for the instinctive fear reaction of human subjects, the EEG features of humans are extracted, thereby effectively studying the instinctive fear reaction of humans. Accordingly, the instinctive fear research method is applicable to an instinctive fear research device, which can be deployed in an electronic device configured with a von Neumann architecture, for example, Figure 1 The server 130 in the illustrated implementation environment may be an electronic device such as a desktop computer, a laptop computer, a server, or the like.

[0028] In order to make the objectives, technical solutions and advantages of this application clearer, the implementation methods of this application will be further described in detail below with reference to the accompanying drawings.

[0029] In the following method embodiments, for ease of description, the execution entity of each step of the method is taken as an example of a server, but this does not constitute a specific limitation.

[0030] Figure 1 This is a schematic diagram of an implementation environment involved in a method for studying instinctive fear based on EEG features. It should be noted that this implementation environment is only an example adapted for the present invention and should not be considered as providing any limitation on the scope of application of the present invention.

[0031] The implementation environment includes a collection end 110 and a service end 130 .

[0032] Specifically, the acquisition terminal 110 may be an electronic device having the function of collecting EEG signals, which is not specifically limited here.

[0033] Server 130 can be an electronic device with computing capabilities, such as a desktop computer, a laptop computer, a server, a computer cluster consisting of multiple servers, a cloud computing center consisting of multiple servers, etc. Server 130 stores a program for the visceral fear research method and can be used to provide background services, including but not limited to visceral fear research services.

[0034] A network communication connection is pre-established between the server 130 and the acquisition terminal 110 via a wired or wireless method, and data transmission between the server 130 and the acquisition terminal 110 is achieved via the network communication connection. The transmitted data includes but is not limited to: raw EEG signals, etc.

[0035] Through the interaction between the acquisition terminal 110 and the server terminal 130, the acquisition terminal 110 sends the original EEG signal to the server terminal 130, and the server terminal 130 processes the original EEG signal to obtain EEG information, and analyzes the EEG information to obtain EEG features.

[0036] See also Figure 2 The embodiment of the present application provides a method for studying instinctive fear based on EEG characteristics. The method is applicable to electronic devices, which may be Figure 1 The server 130 is shown in an implementation environment.

[0037] In the following method embodiment, for ease of description, the execution subject of each step of the method is taken as the electronic device 130 as an example for illustration, but this does not constitute a specific limitation.

[0038] like Figure 2 As shown, the method may include the following steps:

[0039] Step 310: output different stimulation signals.

[0040] First of all, it should be noted that designing different stimulus signals to simulate different dangerous situations can trigger different instinctive fear reactions, and these different instinctive fear reactions can serve as the basis for the study of instinctive fear.

[0041] The form of the stimulus signal includes, but is not limited to, visual, audible, and olfactory forms. For example, if the stimulus signal is visual, the stimulus signal can be a video that simulates a dangerous stimulus. Of course, different video content can simulate different types of fear stimuli, such as approaching fear stimuli, intensive fear stimuli, and fear of natural enemies. Furthermore, each type of fear stimulus contains different fear stimuli, corresponding to different fear stimulus intensities. For example, in intensive fear stimuli, the fear stimulus intensity of a high-density stimulus signal will be stronger than that of a low-density stimulus signal.

[0042] In one embodiment, the stimulation signal includes outputting a fear approaching and then moving away signal, a fear approaching signal, or a fear moving away signal. Specifically, the fear approaching signal may be a video of a rapidly expanding ball, which simulates the sudden appearance of danger; the fear moving away signal may be a video of a large ball rapidly shrinking, which simulates the retreat of danger; the outputting the fear approaching and then moving away signal may be a rapidly expanding ball offset to the other side, which simulates the sudden appearance and then retreat of danger. The fear stimulation intensities of the above stimulation signals are, from large to small, fear approaching signal, fear approaching and then moving away, and fear moving away.

[0043] Scientific research requires ensuring that the EEG signals obtained from the experiment are valid and reliable, so that the analysis results obtained from the experiment are meaningful. Therefore, if the subjects do not conduct the experiment carefully and attentively as required, the EEG signals obtained will be invalid experimental data.

[0044] In one embodiment, Figure 3 As shown, after step 310, the following steps are also included:

[0045] Step 410: Send a confirmation request to the subject.

[0046] Step 430: Receive the confirmation result returned by the subject.

[0047] Step 450: Based on the confirmation result, it is determined whether the subject is focused on the experiment. If the subject is not focused on the experiment, the original EEG signal is valid data; otherwise, it is invalid data.

[0048] First of all, it should be noted that the method of sending the confirmation request can be a voice broadcast or a method of displaying a request box using a display device, which is not limited here. Correspondingly, the method of receiving the confirmation result returned by the subject can be a method in which the subject returns the confirmation result by voice, or a method in which the subject returns the confirmation result by touching the screen, or a method in which the subject uses a button to return the confirmation result, which is also not limited here.

[0049] The confirmation request is used to request the subject to return a confirmation result, which is used to determine whether the subject is focused on the experiment.

[0050] In one embodiment, Figure 4 As shown, after step 430, the following steps may be included:

[0051] Step 610: Compare the confirmation result with the setting information.

[0052] In step 630, if the confirmation result is the same as the setting information, the original EEG signal of the subject is valid data; otherwise, it is invalid data.

[0053] Regarding the setting information, the setting information is the number of stimulation signal outputs set for the experiment, in other words, the number of times the stimulation signal actually appears during the experiment.

[0054] It can be understood that if the confirmation result is the same as the actual result, it means that the subject has completed the experimental task seriously, the experimental data (original EEG signal) obtained is credible, and the next step of data processing can be carried out based on the experimental data; if the confirmation result is different from the actual result, it means that the subject has not completed the experimental task seriously, the experimental data obtained is unreliable, then the current experiment is ended.

[0055] It is further explained that a stimulation interval can be set between outputting different stimulation signals to ensure that the subject's instinctive fear response produced by the current stimulation signal will not continue to the time when the next stimulation signal is output, thereby affecting the instinctive fear response of the next stimulation signal.

[0056] In one embodiment, Figure 5 As shown, step 310 may include the following steps:

[0057] Step 510: Determine the stimulation interval.

[0058] Step 530: output the current stimulation signal, and after the stimulation interval, output the next stimulation signal.

[0059] It should be noted that when outputting different stimulus signals, the stimulus signals can be output in a fixed sequence, for example, fear approach signal -> fear retreat signal -> fear approach but retreat signal -> fear approach signal...; the fear stimulus intensity can also be randomly selected and the stimulus signal output can be randomly selected; and multiple stimulus intervals can also be set. In the experiment, the set stimulus interval is randomly selected, and after the stimulus interval, the next stimulus signal is output. Furthermore, the fear stimulus intensity and stimulus interval can be randomly selected simultaneously and the stimulus signal output can be randomly selected. It can be understood that increasing the randomness of the output stimulus signal makes it impossible for the subject to know in advance what the next stimulus signal will be and / or when the next stimulus signal will appear, thereby making the subject's instinctive fear reaction more obvious.

[0060] Step 330: Obtain the original EEG signal of the subject, perform noise reduction processing on the original EEG signal, and obtain an EEG signal.

[0061] Among them, the subjects are volunteers participating in the instinctive fear research experiment. In order to ensure the experimental effect, the subjects cannot be the researchers of the experiment, because knowing the specific experimental process in advance will reduce the subjects' instinctive fear reaction, which in turn affects the experimental results.

[0062] First of all, it should be noted that when humans face fear stimuli, the EEG signals in the cerebral cortex will change. EEG signals are an objective biological indicator. The experimental paradigm designed in this program uses the EEG signals of the subjects' instinctive fear state as the research basis to study instinctive fear.

[0063] The original EEG signals of the subjects can be obtained by non-invasive measurement methods, for example, by using silver / silver chloride electrodes, which is not limited here.

[0064] It's important to note that EEG signals are highly random physiological signals with diverse rhythms. Various emotions and mental states can affect brainwave fluctuations. Consequently, EEG signals are highly sensitive to time variations and easily contaminated by irrelevant noise. Consequently, unprocessed raw EEG signals contain a range of noise, including high-frequency noise, low-frequency noise, oculomotor artifacts, myoelectric artifacts, and electrocardiographic artifacts. Therefore, before analyzing raw EEG signals, noise removal is necessary to obtain a clear signal.

[0065] Regarding processing high-frequency noise and low-frequency noise in the original EEG signal, a low-pass filter can be used to filter out high-frequency noise, and a high-pass filter can be used to filter out low-frequency noise. In addition, a finite-length impulse response filter (FIR filter) can be used to filter the noise of the original EEG signal forward and backward. Specifically, the original EEG signal can be input into the FIR filter in sequence, then flipped back and forth in sequence, input into the FIR filter again, and finally flipped back and forth again in sequence to ensure that the phase delay introduced by each filter is zero.

[0066] In one embodiment, a low-pass filter is used to filter out high-frequency noise above 40 Hz in the original EEG signal, and a high-pass filter is used to filter out low-frequency noise below 0.5 Hz in the original EEG signal.

[0067] Regarding the processing of various artifacts in the original EEG signal, such as Figure 6 As shown, step 330 may include the following steps:

[0068] Step 331 : Separate and process the original EEG signals based on independent component analysis to obtain at least one set of first EEG signals containing artifacts and second EEG signals without artifacts.

[0069] Step 333 : removing artifacts from each first EEG signal, and reconstructing the first EEG signal and the second EEG signal based on the artifact-removed signals to obtain an EEG signal.

[0070] As mentioned above, the original EEG signal includes artifacts such as electrooculographic artifacts, electromyographic artifacts, and electrocardiographic artifacts. These artifacts do not belong to the electrical signals generated by brain activity. Therefore, it is necessary to remove the artifacts and extract pure EEG signals.

[0071] The second EEG signal refers to the EEG independent component without artifacts obtained by extracting the original EEG signal using independent component analysis. Conversely, the first EEG signal refers to the EEG independent component with artifacts obtained by extracting the original EEG signal using independent component analysis.

[0072] Specifically, independent component analysis is performed on the original EEG signal to determine the EEG independent component, the independent component containing electrooculogram artifacts, the independent component containing electromyography artifacts and the independent component containing electrocardiography artifacts; for the independent component containing electrooculogram artifacts, the electrooculogram artifacts are removed, for the independent component containing electromyography artifacts, the electromyography artifacts are removed, and for the independent component containing electrocardiography artifacts, the electrocardiography artifacts are removed; based on the independent component without electrooculogram artifacts, the independent component without electromyography artifacts, the independent component without electrocardiography artifacts and the EEG independent component, reconstruction is performed to obtain the EEG signal with various types of artifacts removed.

[0073] In one embodiment, the independent components of electrooculogram (EOG) artifacts, the independent components of electromyography (EMG) artifacts, the independent components of electrocardiogram (ECG) artifacts, and the independent components of EEG are removed and reconstructed using the back-projection method according to the weights of the above independent components in the original EEG signal to obtain an EEG signal with various types of artifacts removed.

[0074] Step 350: Obtain EEG information based on time-frequency analysis of the EEG signal.

[0075] Among them, EEG signals contain a large amount of physiological information, which can reflect the changing characteristics of brain activity. By extracting the physiological information contained in EEG information and analyzing this physiological information, we can study the part of EEG signals related to instinctive fear reactions and then understand the characteristics of brain activity when in a state of instinctive fear.

[0076] There are many ways to analyze EEG signals, such as time domain analysis and frequency domain analysis. Time domain analysis can quickly determine changes in EEG signal amplitude caused by stimulation signals, but it cannot obtain EEG information related to the frequency of the EEG signal. Frequency domain analysis can obtain the energy distribution over frequency, but frequency domain analysis is only applicable to steady-state signals, while EEG signals are non-steady-state. Therefore, combining the characteristics of time domain analysis and frequency domain analysis, it is possible to perform time-frequency analysis on EEG signals and extract EEG information, which includes information about the relationship between time, frequency, and energy of EEG signals under different stimulation signals.

[0077] Of course, EEG information in different frequency bands can reflect changes in EEG signals during instinctive fear reactions. Therefore, based on the frequency of the EEG information, the EEG information can be divided into frequency bands according to set rules to obtain EEG information in each frequency band.

[0078] In one embodiment, the EEG information is divided into Alpha segment (8-13 Hz), Beta segment (14-30 Hz), Theta segment (5-7 Hz), Delta segment (1-4 Hz) and Gamma segment (>30 Hz) according to set rules.

[0079] It should be noted that during the experiment, the EEG acquisition device can collect brain waves from various brain regions of the subjects, obtaining several sets of raw EEG signals. Correspondingly, each set of raw EEG signals has a corresponding brain region, such as left frontal, right frontal, right occipital, etc. Based on this, the EEG information obtained by processing the raw EEG signals also has a corresponding brain region.

[0080] Step 370: extracting EEG features from the EEG information.

[0081] As mentioned above, EEG information includes information about the relationship between time, frequency and energy of EEG signals under different stimulation signals, and EEG characteristics are used to indicate the changes in EEG information when the subjects are in different degrees of instinctive fear under stimulation of different stimulation signals.

[0082] The EEG characteristics include but are not limited to the troughs and peaks of EEG information. For example, when stimulated by a fear approach signal, the subject is in an instinctive fear state, and the energy trough generated at the EEG signal frequency of 8-14 Hz is deeper than the energy troughs generated in other frequency bands.

[0083] Step 390: Analyze based on EEG characteristics to obtain the results of the instinctive fear study.

[0084] Among them, the research results of instinctive fear refer to the changes in brain activity when humans are in a state of instinctive fear, for example, brain activity becomes active, brain activity is inhibited, etc.

[0085] As mentioned above, EEG information of each frequency band is obtained based on the frequency division of EEG information, and EEG information has corresponding brain areas. Based on this, the results of the instinctive fear study can also reflect the changes in brain activities in different frequency bands and different brain areas.

[0086] In one embodiment, the EEG characteristic is that under the stimulation of a fear approach signal, the energy trough generated by the Alpha segment is deeper than the energy troughs in other frequency bands. Therefore, the result of the instinctive fear study is that the suppression of brain activity in the Alpha segment is stronger than the suppression of brain activity in other frequency bands.

[0087] In one embodiment, the EEG characteristics are that the subject is stimulated by a fear approach followed by a distance signal, a fear approach signal, and a fear distance signal. The energy trough generated by the fear approach signal is deeper than the energy trough generated by the fear approach followed by a distance signal and a fear distance signal. Therefore, the result of the instinctive fear study is that under the stimulation of the fear approach signal, the Alpha segment brain activity is most strongly inhibited.

[0088] In one embodiment, the EEG characteristic is that the energy trough generated in the posterior brain region of the subject is deep. Therefore, the result of the instinctive fear study is that the brain activity in the posterior brain region of the subject is greatly inhibited.

[0089] Through the above process, different stimulation signals are designed to obtain the original EEG signals generated by the subjects under different stimulation signals. The EEG features are extracted after processing the original EEG signals to explore the impact of different types of fear stimulation on brain activity, and then explore the various changes that occur in the human brain when in a state of instinctive fear, so as to understand the mechanism of human instinctive fear.

[0090] Figure 7This is a schematic diagram of the specific implementation of the instinctive fear research method based on EEG characteristics in an application scenario.

[0091] Step 810: output a stimulation signal.

[0092] Among them, in this application scenario, the subjects include a healthy person and a patient with autism spectrum disorder. Healthy people refer to ordinary people who do not suffer from mental disorders. It can be understood that the instinctive fear reactions of the two subjects selected in this experiment when facing fear stimuli are different. By comparing the EEG signals of the two subjects under the stimulation signal, we can better observe and analyze the impact of the stimulation signal on the human instinctive fear reaction.

[0093] Before the test, the subjects were first informed of the experiment and asked to remain focused, keep their eyes fixed on the center of the screen, and count the number of balls that appeared during the experiment. They were then instructed to sit at a distance from the monitor.

[0094] like Figure 8 As shown, the subject sits at a distance from the monitor. Specifically, the distance is determined according to the size of the monitor to ensure a clear field of view for the subject. For example, when the size of the monitor is 34 cm by 61 cm (34 cm*61 cm), the subject sits at a distance of 75 cm from the monitor.

[0095] In this experiment, the stimulation signals included the ball moving away signal (fear moving away signal), the ball approaching signal (fear approaching signal), and the ball moving away after approaching (fear moving away after approaching signal). During the experiment, the above three stimulation signals were randomly output. Among them, there were 24 experiments for each type of stimulation signal. The image of the ball was vaguely visible from a viewing angle of 1° to 15° within 0.3 seconds. In half of the experiments, the ball appeared randomly on the left side of the screen, and in the other half of the experiments, the ball appeared randomly on the right side of the screen. The purpose of this design is to increase the randomness of the output stimulation signals and make the subjects' instinctive fear response more significant.

[0096] Step 830: collecting the original EEG signal of the subject.

[0097] Regarding the collection of raw EEG signals, during the experiment, the EEG signals of the subjects can be collected through silver / silver chloride electrodes. The number of silver / silver chloride electrodes can be 32, 64 or 128, which is not limited here. In this experiment, 32 silver / silver chloride electrodes are used to collect the raw EEG signals of the subjects, such as Figure 9 As shown, 32 silver / silver chloride electrodes were arranged according to the international 10-20 system, with reference electrodes placed on both mastoid processes. Ground electrodes were placed at the center of the frontal poles FP1 and FP2 and the forehead midline Fz. The silver / silver chloride electrodes were embedded in an elastic cap and worn on the head by the subjects.

[0098] It is understood that silver / silver chloride electrodes can collect raw EEG signals from a specific area of ​​the subject's brain. For example, a silver / silver chloride electrode attached to the back of the subject's brain will collect raw EEG signals from the back of the subject's brain. Therefore, using 32 silver / silver chloride electrodes to collect the subject's raw EEG signals can produce 32 sets of raw EEG signals, corresponding to 32 locations in the subject's brain. The raw EEG signals are sampled at a frequency of 1000 Hz. The inter-electrode impedance is maintained below 5k ohms. The data is recorded in EEG format using the Brain Vision Recorder software.

[0099] Step 850: Receive the confirmation value returned by the subject. If the confirmation value is the same as the actual value, the experiment continues; otherwise, the current experiment ends.

[0100] After the experiment is over, the subject returns the total number of balls counted during the experiment by pressing a key to confirm the number, which indicates the total number of balls calculated by the subject.

[0101] If the confirmed value is the same as the actual value, that is, the total number of balls calculated by the subject is the same as the total number of balls that actually appeared in the experiment, then the subject has completed the experimental task carefully, the experimental data obtained (original EEG signal) is credible, and the next step of data processing can be based on the experimental data; if the confirmed value is different from the actual value, then the total number of balls calculated by the subject is different from the total number of balls that actually appeared in the experiment, then the subject has not completed the experimental task carefully, the experimental data obtained is unreliable, and the current experiment is ended.

[0102] Step 870: Perform noise reduction processing on the original EEG signal to obtain an EEG signal.

[0103] The raw EEG signals collected contain a range of noise, including high-frequency noise, low-frequency noise, and various artifacts. In this experiment, high-frequency noise above 40 Hz was filtered out using a low-pass filter, and low-frequency noise below 0.5 Hz was filtered out using a high-pass filter. For various artifacts, independent component analysis was used to remove them, and then back-projection was used to reconstruct a clear, artifact-free EEG signal.

[0104] Step 890: Perform time-frequency analysis on the EEG signal to obtain EEG information.

[0105] First of all, it should be noted that the EEG signal can reflect the changes in the subject's brain waves under the stimulation signal. Different stimulation signals have different effects on the subject's brain waves. Therefore, the EEG signal can be segmented based on the stimulation signal type and duration of the stimulation signal. It can be understood that the segmented EEG signal can more intuitively reflect the effects of different stimulation signals on brain waves. For example, the ball approaching signal, the ball moving away signal after approaching, and the ball moving away signal are output to the screen in front of the subject in sequence. The duration of each stimulation signal is 0.3s. Therefore, based on the above three stimulation signals, the EEG signal is divided into three segments, each of which is 0.3s, namely the Loom segment, the Miss segment, and the Far segment.

[0106] Then, based on the divided EEG signal segments, time-frequency analysis is performed to obtain EEG information, which includes information about the relationship between time, frequency, and energy of EEG signals under different stimulation signals. It can be seen that under different stimulation signals, the EEG signals of healthy people and patients with autism spectrum disorders change. Specifically, the change in energy in EEG information can reflect the brain activity. It can be understood that the stronger the energy in the EEG information, the more intense the brain activity, and vice versa. The weaker the brain activity. Of course, in order to further observe the impact of the stimulation signal on the brain activity of different subjects, the EEG information can be divided into multiple frequency bands based on its frequency to observe the impact of the stimulation signal on brain activity in different frequency bands. Then, the EEG signal is divided into Alpha segment (8-13Hz), Beta segment (14-30Hz), Theta segment (5-7Hz), Delta segment (1-4Hz) and Gamma segment (>30Hz) according to the frequency.

[0107] Step 811: Based on the EEG information, EEG features are extracted and analyzed to obtain the results of the instinctive fear research.

[0108] like Figure 10 The following figure shows the time-frequency plots of the EEG signals of two subjects under the stimulation of a ball approaching signal. It can be seen that under the stimulation of the ball approaching signal, the alpha segment oscillation activity in the posterior brain of healthy subjects is greatly suppressed, while the alpha segment oscillation activity in the posterior brain of patients with autism spectrum disorder remains largely unchanged.

[0109] Regarding the time-frequency power spectrum, in order to further confirm that the significant differences between the Alaph segment EEG signals of healthy people and those of patients with autism spectrum disorder are caused by the spherical approximation signal, the time-frequency power spectra of the Loom segment, Miss segment, and Far segment of the EEG signals of healthy people, as well as the time-frequency power spectra of the Loom segment, Miss segment, and Far segment of the EEG signals of patients with autism spectrum disorder, were calculated based on EEG information.

[0110] like Figure 11As shown in the figure, it can be seen that for healthy people, the inhibition of Alpha segment oscillation activity under ball approaching signal stimulation is significantly stronger than that of the other two stimulation signals, while for patients with autism spectrum disorder, there is no significant difference in the effects of the three stimulation signals on Alpha segment oscillation activity. Furthermore, under the stimulation of the ball approaching signal, the Alpha segment of the EEG signal of healthy people will be greatly inhibited, while the Alpha segment of patients with autism spectrum disorder will basically remain unchanged.

[0111] Among them, the time-frequency power spectrum can be calculated using the Chronux toolbox using a moving window size of 1000 ms and a step size of 50 ms, from -2 seconds to 2 seconds relative to the onset of the stimulus signal, with a frequency resolution of 1 Hz.

[0112] Of course, we can also further calculate the relative power spectra of the Loom, Miss, and Far segments of the EEG signals of healthy individuals based on the time-frequency power spectrum to compare the effects of various types of stimulation signals on the EEG signals. The relative power spectrum corresponding to each stimulation signal can be calculated as (power before stimulation signal - power after stimulation signal) / power before stimulation signal.

[0113] like Figure 12 Figure 2 shows the relative power spectrum of the EEG signals in the Alhpa segment of healthy subjects. It can be seen that the ball approaching signal significantly suppresses the EEG signals in the Alhpa segment of healthy subjects more than the other two stimulation signals.

[0114] During this experiment, we found that when stimulated by stimulation signals, the EEG characteristics of patients with autism spectrum disorder differed significantly from those of healthy controls. These significantly different EEG characteristics can provide a more objective diagnostic indicator for screening patients with autism spectrum disorder. Of course, patients with other mental disorders also generate different EEG indicators when stimulated by stimulation signals. Accordingly, EEG characteristics containing these differences can also provide a diagnostic indicator for screening for corresponding mental disorders.

[0115] In this application scenario, by setting up a control experimental group, we obtain the original EEG signals of the two subjects, process the two sets of original EEG signals to obtain the corresponding EEG information, extract the EEG features in the two sets of EEG information, and compare the differences between the EEG features to explore the effects of different fear stimuli on brain activity, and then explore the various changes in the human brain when in a state of instinctive fear, so as to understand the mechanism of instinctive fear.

[0116] The following is an embodiment of the device of the present application, which can be used to implement the method for studying instinctive fear based on EEG features involved in the present application. For details not disclosed in the embodiment of the device of the present application, please refer to the method embodiment of the method for studying instinctive fear based on EEG features involved in the present application.

[0117] See also Figure 13 In an embodiment of the present application, a device 900 for studying instinctive fear based on EEG features is provided, including but not limited to: a signal output module 910, a signal processing module 930, a time-frequency analysis module 950, a feature extraction module 970, and a feature analysis module 990.

[0118] The signal output module 910 is configured to output different stimulation signals, wherein different stimulation signals correspond to different fear stimulation intensities.

[0119] The signal processing module 930 is used to obtain the original EEG signal of the subject, perform noise reduction processing on the original EEG signal, and obtain the EEG signal.

[0120] The time-frequency analysis module 950 is used to obtain EEG information based on time-frequency analysis of EEG signals.

[0121] A feature extraction module 970 is used to extract EEG features from the EEG information, wherein the EEG features are used to indicate changes in the EEG signals when the subject is in an instinctive fear state under different stimulation signals;

[0122] The feature analysis module 990 is used to analyze the EEG features to obtain the results of the instinctive fear study. The results of the instinctive fear study are used to indicate the changes in brain activity when the subject is in a state of instinctive fear.

[0123] It should be noted that the device for studying instinctive fear based on EEG features provided in the above embodiment only uses the division of the above-mentioned functional modules as an example when conducting research on instinctive fear based on EEG features. In actual applications, the above-mentioned functions can be assigned to different functional modules as needed, that is, the internal structure of the device for studying instinctive fear based on EEG features will be divided into different functional modules to complete all or part of the functions described above.

[0124] In addition, the device for studying instinctive fear based on EEG features and the method for studying instinctive fear based on EEG features provided in the above embodiments belong to the same concept, and the specific manner in which each module performs operations has been described in detail in the method embodiments and will not be repeated here.

[0125] See also Figure 14 In an embodiment of the present application, an electronic device 4000 is provided. The electronic device 4000 may include: a desktop computer, a laptop computer, a server, etc.

[0126] exist Figure 14In the embodiment, the electronic device 4000 includes at least one processor 4001, at least one communication bus 4002 and at least one memory 4003.

[0127] The processor 4001 and the memory 4003 are connected, for example, via a communication bus 4002. Optionally, the electronic device 4000 may further include a transceiver 4004, which may be used for data exchange between the electronic device and other electronic devices, such as data transmission and / or data reception. It should be noted that in actual applications, the number of transceivers 4004 is not limited to one, and the structure of the electronic device 4000 does not constitute a limitation on the embodiments of the present application.

[0128] Processor 4001 may be a CPU (Central Processing Unit), a general-purpose processor, a DSP (Digital Signal Processor), an ASIC (Application Specific Integrated Circuit), an FPGA (Field Programmable Gate Array), or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof. It may implement or execute the various exemplary logic blocks, modules, and circuits described in conjunction with the disclosure of this application. Processor 4001 may also be a combination that implements computing functions, such as a combination of one or more microprocessors, a combination of a DSP and a microprocessor, and the like.

[0129] The communication bus 4002 may include a path for transmitting information between the above components. The communication bus 4002 may be a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus. The communication bus 4002 may be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 14 Only one thick line is used in the diagram, but this does not mean that there is only one bus or one type of bus.

[0130] The memory 4003 may be a ROM (Read Only Memory) or other types of static storage devices that can store static information and instructions, a RAM (Random Access Memory) or other types of dynamic storage devices that can store information and instructions, or an EEPROM (Electrically Erasable Programmable Read Only Memory), a CD-ROM (Compact Disc Read Only Memory) or other optical disk storage, optical disk storage (including compact discs, laser discs, optical discs, digital versatile discs, Blu-ray discs, etc.), a magnetic disk storage medium or other magnetic storage device, or any other medium that can be used to carry or store desired program code in the form of instructions or data structures and can be accessed by a computer, but is not limited to these.

[0131] The memory 4003 stores a computer program, and the processor 4001 reads the computer program stored in the memory 4003 through the communication bus 4002 .

[0132] When the computer program is executed by the processor 4001, the method for studying instinctive fear based on EEG characteristics in the above-mentioned embodiments is implemented.

[0133] In addition, an embodiment of the present application provides a storage medium on which a computer program is stored. When the computer program is executed by a processor, the instinctive fear research method based on EEG characteristics in the above-mentioned embodiments is implemented.

[0134] In one embodiment of the present application, a computer program product is provided. The computer program product includes a computer program stored in a storage medium. A processor of a computer device reads the computer program from the storage medium and executes the computer program, causing the computer device to perform the method for studying instinctive fear based on EEG features described in each of the above embodiments.

[0135] Compared with related technologies, the experimental paradigm designed in this scheme to study the instinctive fear response of human subjects outputs different stimulation signals, obtains the original EEG signals generated by the subjects under different stimulation signals, and extracts EEG features after processing the original EEG signals to explore the impact of different fear stimuli on brain activity, and then explores the various changes produced in the human brain when in a state of instinctive fear, so as to understand the mechanism of instinctive fear.

[0136] It should be understood that although the steps in the flowcharts of the accompanying drawings are shown in sequence as indicated by the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some of the steps in the flowcharts of the accompanying drawings may include multiple sub-steps or multiple stages, and these sub-steps or stages are not necessarily executed at the same time, but can be executed at different times, and their execution order is not necessarily sequential, but can be executed in turn or alternately with other steps or at least a portion of the sub-steps or stages of other steps.

[0137] The above description is only part of the implementation methods of the present application. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present application. These improvements and modifications should also be regarded as the scope of protection of the present application.

Claims

1. A method for studying instinctive fear based on EEG characteristics, characterized in that: include: Outputting different stimulation signals by randomly selecting the fear stimulation intensity and / or randomly selecting the stimulation interval for the stimulation signal, wherein different stimulation signals correspond to different fear stimulation intensities; Obtaining original EEG signals from the subject, and performing noise reduction processing on the original EEG signals to obtain EEG signals; Obtaining EEG information based on time-frequency analysis of the EEG signal, including segmenting the EEG signal based on a stimulus signal type and duration, determining different segments in the EEG signal, and dividing the EEG signal into multiple frequency bands based on a frequency of the EEG signal; Extracting EEG features from the EEG information, wherein the EEG features are used to indicate changes in the EEG information when the subject is in a state of instinctive fear to different degrees under stimulation by different stimulation signals; Based on the EEG characteristics, analysis is performed to obtain the results of the instinctive fear study, which are used to indicate the changes in brain activity when the subject is in a state of instinctive fear, including generating a time-frequency diagram corresponding to the EEG signal and a time-frequency power spectrum of different segments in the corresponding EEG signal based on the EEG information, and obtaining the instinctive fear results based on the time-frequency diagram and the time-frequency power spectrum.

2. The method according to claim 1, wherein After obtaining EEG information based on time-frequency analysis of the EEG signal, the method further includes: Based on the frequency of the EEG information, the EEG information is divided into frequency bands according to a set rule to obtain the EEG information in several frequency bands.

3. The method according to claim 2, wherein The method further comprises: Dividing the EEG information into Alpha, Beta, Theta, Delta and Gamma segments according to set rules; The EEG information of each frequency band is extracted to obtain the EEG features.

4. The method according to claim 1, wherein The method further comprises: Obtaining original EEG signals of several brain regions of the subject; Based on the original EEG signals of the respective brain regions, obtaining EEG information corresponding to the respective brain regions; Feature extraction is performed based on the EEG information of each of the brain regions to obtain the EEG features.

5. The method according to claim 1, wherein The outputting of different stimulation signals includes: outputting a fear approaching and then moving away signal, a fear approaching signal or a fear moving away signal.

6. The method according to claim 1, wherein The method further comprises: outputting different stimulation signals; Setting a stimulation interval, wherein the stimulation interval is used to indicate the interval between outputs of the stimulation signal; Output the current stimulation signal, and after the stimulation interval, output the next stimulation signal.

7. The method according to any one of claims 1 to 6, wherein: After obtaining the original EEG signal of the subject, the method further includes: Sending a confirmation request to the subject, wherein the confirmation request is used to request the subject to return a confirmation result; receiving the confirmation result returned by the subject; Based on the confirmation result, it is determined whether the subject is focused on the experiment. If the subject is not focused on the experiment, the original EEG signal is valid data; otherwise, it is invalid data.

8. The method according to claim 7, wherein After receiving the confirmation result returned by the subject, the method further includes: Comparing the confirmation result with setting information, wherein the confirmation result is a result of counting the number of times the subject outputs the stimulation signal during the experiment, and the setting information is the number of times the stimulation signal is output set for the experiment; If the confirmation result is the same as the set information, the original EEG signal of the subject is valid data; otherwise, it is invalid data.

9. A device for studying instinctive fear based on EEG characteristics, characterized in that: include: a signal output module, configured to output different stimulation signals by randomly selecting a fear stimulation intensity and / or randomly selecting a stimulation interval for the stimulation signal, wherein different stimulation signals correspond to different fear stimulation intensities; A signal processing module is used to obtain the original EEG signal of the subject and perform noise reduction processing on the original EEG signal to obtain an EEG signal; a time-frequency analysis module for performing time-frequency analysis on the EEG signal to obtain EEG information, including segmenting the EEG signal based on the stimulation signal type and duration of the stimulation signal, determining different segments in the EEG signal, and dividing the EEG signal into multiple frequency bands based on the frequency of the EEG signal; a feature extraction module, configured to extract EEG features from the EEG information, wherein the EEG features are used to indicate changes in EEG signals when the subject is in an instinctive fear state under stimulation by different stimulation signals; A feature analysis module is used to perform analysis based on the EEG features to obtain the results of the instinctive fear study, which are used to indicate the changes in brain activity when the subject is in a state of instinctive fear, including generating a time-frequency diagram corresponding to the EEG signal and a time-frequency power spectrum of different segments in the corresponding EEG signal based on the EEG information, and obtaining the instinctive fear results based on the time-frequency diagram and the time-frequency power spectrum.

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