WO2024092869A1 - 基于脑电特征的本能恐惧研究方法和装置 - Google Patents

基于脑电特征的本能恐惧研究方法和装置 Download PDF

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WO2024092869A1
WO2024092869A1 PCT/CN2022/131228 CN2022131228W WO2024092869A1 WO 2024092869 A1 WO2024092869 A1 WO 2024092869A1 CN 2022131228 W CN2022131228 W CN 2022131228W WO 2024092869 A1 WO2024092869 A1 WO 2024092869A1
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eeg
signal
fear
stimulation
subject
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PCT/CN2022/131228
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French (fr)
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韩传亮
王立平
蔚鹏飞
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中国科学院深圳先进技术研究院
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    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/16—Devices for psychotechnics; Testing reaction times ; Devices for evaluating the psychological state
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/24—Detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof
    • A61B5/316—Modalities, i.e. specific diagnostic methods
    • A61B5/369—Electroencephalography [EEG]
    • A61B5/372—Analysis of electroencephalograms
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/24—Detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof
    • A61B5/316—Modalities, i.e. specific diagnostic methods
    • A61B5/369—Electroencephalography [EEG]
    • A61B5/377—Electroencephalography [EEG] using evoked responses
    • A61B5/378—Visual stimuli
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/24—Detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof
    • A61B5/316—Modalities, i.e. specific diagnostic methods
    • A61B5/369—Electroencephalography [EEG]
    • A61B5/377—Electroencephalography [EEG] using evoked responses
    • A61B5/38—Acoustic or auditory stimuli
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/24—Detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof
    • A61B5/316—Modalities, i.e. specific diagnostic methods
    • A61B5/369—Electroencephalography [EEG]
    • A61B5/377—Electroencephalography [EEG] using evoked responses
    • A61B5/381—Olfactory or gustatory stimuli

Definitions

  • the present application relates to the field of psychology, and in particular, to a method and device for studying instinctive fear based on EEG characteristics.
  • Human fear is one of the most important and indispensable basic emotional manifestations in the process of species evolution and survival. External fear stimulation can stimulate the specific defensive behavior of individual organisms, thus playing an important role in their survival and reproduction. Fear includes conditioned fear and instinctive fear. Among them, instinctive fear is a behavior that can be produced without acquired learning.
  • 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 fear in experimental animals, observing the reactions of experimental animals to fear stimulation (for example, mice will run away when faced with fear stimulation), and then exploring and understanding the impact of fear on animals.
  • the embodiments of the present application provide a method, device, electronic device and storage medium for studying instinctive fear based on EEG characteristics, which can solve the problem of the lack of experimental paradigms for studying instinctive fear reactions of human subjects in related technologies.
  • the technical solution is as follows:
  • 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; acquiring original EEG signals of a 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 signals; 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 by 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.
  • 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; 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.
  • stimulation signals of different fear stimulus types are used to obtain the original EEG signals generated by the subjects under different stimulation signals, and the EEG features are extracted after processing the original EEG signals to explore the effects of different fear stimuli on brain activity, and then study the various changes produced in the human brain when in a state of instinctive fear, so as to understand the mechanism of human instinctive fear.
  • FIG1 is a schematic diagram of an implementation environment according to an embodiment of the present application.
  • FIG2 is a flow chart of a method for studying instinctive fear based on EEG features according to an exemplary embodiment
  • FIG3 is a flow chart of the steps following step 310 in the embodiment corresponding to FIG2 in one embodiment
  • FIG4 is a flow chart of the steps following step 430 in the embodiment corresponding to FIG3 in one embodiment
  • FIG5 is a flow chart of step 310 in one embodiment of the embodiment corresponding to FIG2;
  • FIG6 is a flow chart of step 330 in one embodiment of the embodiment corresponding to FIG5;
  • FIGS. 7 to 9 are schematic diagrams showing a specific implementation of a method for studying instinctive fear based on EEG features in an application scenario
  • Figures 10 to 12 are EEG information obtained by a method for studying instinctive fear based on EEG features in an application scenario
  • FIG13 is a structural block diagram of a device for studying instinctive fear based on EEG features according to an exemplary embodiment
  • Fig. 14 is a structural block diagram of an electronic device according to an exemplary embodiment.
  • fear stimuli may cause different instinctive fear reactions. For example, a car suddenly crashing or a dog suddenly rushing out will cause different degrees of intensity in the human instinctive fear reaction. Therefore, the choice of fear stimulus is also of great significance for the experimental design of instinctive fear.
  • the instinctive fear research method based on EEG features provided in the present application extracts human EEG features by designing an experimental paradigm of human subjects' instinctive fear reactions, thereby effectively studying human instinctive fear reactions.
  • the instinctive fear research method is suitable for an instinctive fear research device, and the instinctive fear research device can be deployed in an electronic device configured with a von Neumann architecture, for example, the server 130 in the implementation environment shown in Figure 1, and the electronic device can be a desktop computer, a laptop computer, a server, etc.
  • Figure 1 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 to the present invention and cannot be considered as providing any limitation on the scope of use of the present invention.
  • the implementation environment includes a collection end 110 and a service end 130 .
  • the acquisition end 110 may be an electronic device having the function of collecting EEG signals, which is not specifically limited herein.
  • the server 130 may be an electronic device with computing functions, such as a desktop computer, a laptop computer, a server, a computer device cluster consisting of multiple servers, a cloud computing center consisting of multiple servers, etc.
  • the server 130 stores a program of the visceral fear research method, which can be used to provide background services, such as, but not limited to, visceral fear research services, etc.
  • the server 130 and the acquisition terminal 110 establish a network communication connection in advance through wired or wireless means, and data transmission between the server 130 and the acquisition terminal 110 is realized through the network communication connection.
  • the transmitted data includes but is not limited to: original EEG signals and the like.
  • 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.
  • An embodiment of the present application provides a method for studying instinctive fear based on EEG features.
  • the method is applicable to an electronic device, which may be the server 130 in the implementation environment shown in FIG. 1 .
  • the method may include the following steps:
  • Step 310 output different stimulation signals.
  • the form of the stimulus signal includes but is not limited to visual form, sound form, and smell form.
  • the stimulus signal can be a video that can simulate dangerous stimulation.
  • different video contents can simulate different types of fear stimulation, such as fear approach stimulation, intensive fear stimulation, natural enemy fear stimulation, etc.
  • each type of fear stimulation contains different fear stimulations, which correspond to different fear stimulation intensities. For example, in intensive fear stimulation, the fear stimulation intensity of the stimulus signal with high density will be stronger than the fear stimulation intensity of the stimulus signal with low density.
  • the stimulation signal includes outputting a fear approaching and then moving away signal, a fear approaching signal, or a fear moving away signal.
  • the fear approaching signal may be a video of a ball rapidly expanding, 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 fear approaching and then moving away signal may be a rapidly expanding ball shifting 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.
  • step 310 the following steps are further included:
  • Step 410 Send a confirmation request to the subject.
  • Step 430 receiving the confirmation result returned by the subject.
  • Step 450 based on the confirmation result, it is determined whether the subject is concentrating on the experiment. If the subject is concentrating on the experiment, the original EEG signal is valid data, otherwise, it is invalid data.
  • the method of sending the confirmation request can be a voice broadcast or a method of using a display device to display a request box, which is not limited here.
  • 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 by touching the screen, or by using a button to return the confirmation result, which is also not limited here.
  • the confirmation request is used to request the subject to return a confirmation result, and the confirmation result is used to determine whether the subject is focused on the experiment.
  • step 430 the following steps may be included:
  • Step 610 comparing the confirmation result with the setting information.
  • 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.
  • 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.
  • 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.
  • a stimulation interval can be set to ensure that the instinctive fear response of the subject 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.
  • step 310 may include the following steps:
  • Step 510 determining the stimulation interval time.
  • Step 530 output the current stimulation signal, and after the stimulation interval time, output the next stimulation signal.
  • the stimulation signals can be output in a fixed order, for example, fear approaching signal -> fear moving away signal -> fear approaching but moving away signal -> fear approaching signal...; the fear stimulation intensity can also be randomly selected to output the stimulation signal; multiple stimulation intervals can also be set. In the experiment, the set stimulation interval is randomly selected, and after the stimulation interval, the next stimulation signal is output. Furthermore, the fear stimulation intensity and stimulation interval can be randomly selected at the same time to output the stimulation signal. It can be understood that by increasing the randomness of the output stimulation signal, the subject cannot know in advance what the next stimulation signal will be and/or when the next stimulation signal will appear, so the subject's instinctive fear reaction is more obvious.
  • Step 330 obtaining the original EEG signal of the subject, performing noise reduction processing on the original EEG signal, and obtaining an EEG signal.
  • the subjects are volunteers participating in the instinctive fear research experiment.
  • the subjects cannot be the researchers of the experiment, because knowing the specific experimental process in advance will reduce the subjects' instinctive fear response, which will affect the experimental results.
  • 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.
  • 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.
  • EEG signals are highly random physiological signals with various rhythms. Various emotions and mentalities will affect the changes in brain waves. Therefore, EEG signals are highly sensitive to time variations and are easily contaminated by irrelevant noise. Therefore, the unprocessed raw EEG signals contain a series of noises, such as high-frequency noise, low-frequency noise, electrooculographic artifacts, electromyographic artifacts, electrocardiographic artifacts, etc. Therefore, before analyzing the raw EEG signals, the noise of the raw EEG signals needs to be processed to obtain clear EEG signals.
  • a low-pass filter can be used to filter high-frequency noise
  • a high-pass filter can be used to filter low-frequency noise
  • a finite-length unit impulse response filter FIR filter
  • the original EEG signal can be input into the FIR filter in sequence, and then the original EEG signal can be flipped back and forth in sequence, input into the FIR filter again, and finally the original EEG signal can be flipped back and forth in sequence again to ensure that the phase delay introduced by each filter is zero.
  • 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.
  • step 330 may include the following steps:
  • Step 331 performing separation processing on the original EEG signal based on independent component analysis to obtain at least one group of first EEG signals containing artifacts and second EEG signals without artifacts.
  • Step 333 removing artifacts from each first EEG signal, and reconstructing the first EEG signal and the second EEG signal from which the artifacts have been removed to obtain an EEG signal.
  • 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.
  • the second EEG signal refers to the EEG independent component without artifacts obtained by extracting the original EEG signal using independent component analysis.
  • the first EEG signal refers to the EEG independent component with artifacts obtained by extracting the original EEG signal using independent component analysis.
  • 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 after removing the electrooculogram artifacts, the independent component after removing the electromyography artifacts, the independent component after removing the electrocardiography artifacts and the EEG independent component, reconstruction is performed to obtain the EEG signal with various types of artifacts removed.
  • the independent components of electrooculogram artifacts, the independent components of electromyography artifacts, the independent components of electrocardiogram artifacts and the independent components of electroencephalogram are removed, and reconstructed using the back-projection method according to the weights of the above independent components in the original electroencephalogram signal to obtain an electroencephalogram signal with various types of artifacts removed.
  • Step 350 obtaining EEG information based on time-frequency analysis of the EEG signal.
  • EEG signals contain a large amount of physiological information, which can reflect the changing characteristics of brain activity.
  • physiological information contained in the EEG information By extracting the physiological information contained in the EEG information and analyzing this physiological information, we can study the part of the EEG signal related to the instinctive fear response, and then understand the characteristics of brain activity when in a state of instinctive fear.
  • Time domain analysis can quickly obtain the change in the amplitude of the EEG signal caused by the stimulation signal, but it cannot obtain the EEG information related to the frequency of the EEG signal; frequency domain analysis can obtain the energy value distribution on the frequency, but frequency domain analysis is only applicable to steady-state signals, while EEG signals are non-steady-state signals. Therefore, combining the characteristics of time domain analysis and frequency domain analysis, EEG signals can be analyzed in time and frequency to extract EEG information, which includes information on the relationship between time, frequency and energy of EEG signals under different stimulation signals.
  • EEG information of different frequency bands can reflect the changes of EEG signals when in an instinctive fear reaction. Therefore, based on the frequency of the EEG information, the EEG information can be divided into frequency bands according to the set rules to obtain the EEG information of each frequency band.
  • 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.
  • the EEG acquisition device can be used to collect brain waves from various brain regions of the subjects to obtain several groups of original EEG signals.
  • each group of original EEG signals has a corresponding brain region, such as left frontal, right frontal, right occipital, etc.
  • the EEG information obtained by processing the original EEG signals also has a corresponding brain region.
  • Step 370 extracting EEG features from the EEG information.
  • EEG information includes information about the relationship between time, frequency and energy of EEG signals under different stimulation signals, and EEG features are used to indicate changes in EEG information when the subjects are in different degrees of instinctive fear states under stimulation of different stimulation signals.
  • the EEG characteristics include but are not limited to troughs and peaks of EEG information.
  • the energy troughs generated at the EEG signal frequency of 8-14 Hz are deeper than the energy troughs generated in other frequency bands.
  • Step 390 analyzing based on EEG characteristics to obtain the results of the instinctive fear study.
  • 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, and so on.
  • EEG information of each frequency band is obtained, and EEG information has corresponding brain areas. Based on this, the results of the instinctive fear research can also reflect the changes in brain activities in different frequency bands and different brain areas.
  • the EEG characteristic is that under the stimulation of the fear approach signal, the energy trough generated by the Alpha segment is deeper than the energy troughs of 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.
  • the EEG characteristics are that the subjects are stimulated by the fear approaching and then moving away signal, the fear approaching signal, and the fear moving away signal.
  • the energy trough generated by the fear approaching signal is deeper than the energy trough generated by the fear approaching and then moving away signal and the fear moving away signal. Then, the result of the instinctive fear research is that under the stimulation of the fear approaching signal, the Alpha segment brain activity is most strongly inhibited.
  • the EEG characteristic is that the energy trough generated in the posterior brain region of the subject is deep, and therefore, the result of the instinctive fear study is that the brain activity in the posterior brain region of the subject is significantly inhibited.
  • FIG. 7 is a schematic diagram of a specific implementation of an instinctive fear research method based on EEG features in an application scenario.
  • Step 810 output a stimulation signal.
  • the subjects include a healthy person and a patient with autism spectrum disorder.
  • the healthy person refers to an ordinary person who does 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.
  • the subjects were first told about the experiment and asked to stay focused during the experiment, keep their eyes on the center of the screen, and count the total number of balls that appeared in the experiment. Then the subjects were guided to sit at a distance from the monitor.
  • the subject sits at a distance from the monitor.
  • the distance is determined according to the size of the monitor to ensure a clear field of vision for the subject. For example, when the size of the monitor is 34 cm by 61 cm (34 cm*61 cm), the subject sits 75 cm away from the monitor.
  • the stimulation signals include the ball moving away signal (fear moving away signal), the ball approaching signal (fear approaching signal) and the ball moving away signal after approaching (fear moving away signal after approaching).
  • the above three stimulation signals are randomly output.
  • the image of the ball is vaguely visible from a viewing angle of 1° to 15° within 0.3 seconds. In half of the experiments, the ball will randomly appear on the left side of the screen, and in the other half of the experiments, the ball will randomly appear on the right side of the screen.
  • the purpose of this design is to increase the randomness of the output stimulation signal and make the subjects' instinctive fear reaction more significant.
  • Step 830 collecting the original EEG signal of the subject.
  • 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.
  • 32 silver/silver chloride electrodes were used to collect the original EEG signals of the subjects.
  • the 32 silver/silver chloride electrodes were arranged according to the international 10-20 system, and the reference electrodes were placed on the bilateral mastoids.
  • the ground electrode was placed at the center of the frontal pole FP1, the frontal pole FP2 and the frontal midline Fz.
  • the silver/silver chloride electrode was embedded in the elastic cap and worn on the head by the subject.
  • the silver/silver chloride electrode can collect the original EEG signal of a certain area of the subject's brain.
  • the silver/silver chloride electrode attached to the posterior brain area of the subject can collect the original EEG signal of the posterior brain area of the subject. Therefore, 32 silver/silver chloride electrodes are used to collect the original EEG signals of the subject, and 32 groups of original EEG signals can be obtained. These 32 groups of original EEG signals correspond to 32 locations of the subject's brain.
  • the original EEG signal is sampled at a frequency of 1000Hz.
  • the impedance between the electrodes is kept below 5k ohms.
  • the data is recorded in EEG format by the brain visual recorder software.
  • 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.
  • the subject 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.
  • 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.
  • the subject has completed the experimental task seriously, and the obtained experimental data (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, the total number of balls calculated by the subject is different from the total number of balls that actually appeared in the experiment.
  • the subject did not complete the experimental task seriously, the obtained experimental data is unreliable, and the current experiment is ended.
  • Step 870 Perform noise reduction processing on the original EEG signal to obtain an EEG signal.
  • the collected original EEG signals include a series of noises, such as high-frequency noise, low-frequency noise, various artifacts, etc.
  • the high-frequency noise above 40Hz of the original EEG signals is filtered out by a low-pass filter
  • the low-frequency noise below 0.5Hz of the original EEG signals is filtered out by a high-pass filter.
  • the artifacts are removed by independent component analysis, and then the back-projection method is used to reconstruct a clear EEG signal without artifacts.
  • Step 890 Perform time-frequency analysis on the EEG signal to obtain EEG information.
  • 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.
  • EEG information which includes information about the relationship between time, frequency and energy of EEG signals under different stimulation signals.
  • EEG signals 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.
  • the EEG information can be divided into multiple frequency bands based on its frequency, so as to observe the impact of the stimulation signal on the brain activity of 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.
  • Step 811 based on the EEG information, extract the EEG features, analyze the EEG features, and obtain the results of the instinctive fear study.
  • 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 the EEG information.
  • the Chronux toolbox can be used to calculate the time-frequency power spectrum using a moving window size of 1000 ms and a step size of 50 ms, from -2 seconds to 2 seconds relative to the start of the stimulus signal, with a frequency resolution of 1 Hz.
  • the relative power spectra of the Loom segment, Miss segment and Far segment of the EEG signal of healthy people can also be further calculated based on the time-frequency power spectrum to compare the effects of various types of stimulation signals on the EEG signal.
  • the relative power spectrum corresponding to each stimulation signal can be calculated by (power before stimulation signal - power after stimulation signal) / power before stimulation signal.
  • EEG characteristics of patients with autism spectrum disorder were significantly different from those of healthy people. Therefore, these EEG characteristics with significant differences can provide a diagnostic indicator for screening patients with autism spectrum disorder, and these diagnostic indicators are more objective.
  • patients with other mental disorders also have different EEG indicators when stimulated by stimulation signals. Accordingly, EEG characteristics containing these differences can also provide a diagnostic indicator for screening corresponding mental disorders.
  • a control experimental group is set up to obtain the original EEG signals of the two subjects.
  • the two groups of original EEG signals are processed to obtain the corresponding EEG information, and the EEG features in the two groups of EEG information are extracted.
  • the effects of different fear stimuli on brain activity are explored, and then the various changes in the human brain when in a state of instinctive fear are explored, so as to understand the mechanism of instinctive fear.
  • 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.
  • the method embodiment of the method for studying instinctive fear based on EEG features involved in 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.
  • An embodiment of the present application provides an instinctive fear research device 900 based on EEG features, 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.
  • the signal output module 910 is used to output different stimulation signals, wherein different stimulation signals correspond to different fear stimulation intensities.
  • the signal processing module 930 is used to obtain the original EEG signal of the subject, and perform noise reduction processing on the original EEG signal to obtain the EEG signal.
  • the time-frequency analysis module 950 is used to obtain EEG information based on time-frequency analysis of EEG signals.
  • 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 EEG signals when the subject is in an instinctive fear state under different stimulation signals;
  • the feature analysis module 990 is used to perform analysis based on EEG features to obtain the results of the visceral fear study.
  • the visceral fear study results are used to indicate changes in brain activity when the subject is in a state of visceral fear.
  • the device for studying instinctive fear based on EEG features only uses the division of the above-mentioned functional modules as an example when conducting a study on instinctive fear based on EEG features.
  • 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.
  • 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 way in which each module performs operations has been described in detail in the method embodiments and will not be repeated here.
  • An electronic device 4000 is provided in an embodiment of the present application.
  • the electronic device 4000 may include: a desktop computer, a laptop computer, a server, etc.
  • the electronic device 4000 includes at least one processor 4001 , at least one communication bus 4002 , and at least one memory 4003 .
  • the processor 4001 and the memory 4003 are connected, such as through a communication bus 4002.
  • the electronic device 4000 may also include a transceiver 4004, which may be used for data interaction between the electronic device and other electronic devices, such as data transmission and/or data reception.
  • the transceiver 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.
  • Processor 4001 can 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 can implement or execute various exemplary logic blocks, modules and circuits described in conjunction with the disclosure of this application. Processor 4001 can 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, etc.
  • 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, etc.
  • the communication bus 4002 may be divided into an address bus, a data bus, a control bus, etc.
  • FIG. 14 only uses one thick line, but it does not mean that there is only one bus or one type of bus.
  • the memory 4003 can 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 compressed optical disk, laser disk, optical disk, digital versatile disk, Blu-ray disk, etc.), a disk storage medium or other magnetic storage device, or any other medium that can be used to carry or store the desired program code in the form of instructions or data structures and can be accessed by a computer, but is not limited to these.
  • ROM Read Only Memory
  • RAM Random Access Memory
  • EEPROM Electrically Erasable Programmable Read Only Memory
  • CD-ROM Compact Disc Read Only Memory
  • optical disk storage including compressed optical disk, laser disk, optical disk, digital versatile disk, Blu-ray disk, etc.
  • a disk storage medium or other magnetic storage device or any
  • the memory 4003 stores a computer program
  • the processor 4001 reads the computer program stored in the memory 4003 through the communication bus 4002 .
  • a storage medium is provided in an embodiment of the present application, on which a computer program is stored.
  • the computer program is executed by a processor, the instinctive fear research method based on EEG characteristics in the above-mentioned embodiments is implemented.
  • a computer program product includes a computer program, the computer program is stored in a storage medium.
  • a processor of a computer device reads the computer program from the storage medium, and the processor executes the computer program, so that the computer device executes the instinctive fear research method based on EEG features in the above embodiments.
  • the experimental paradigm for studying the instinctive fear response of human subjects designed in this scheme outputs different stimulation signals, obtains the original EEG signals generated by the subjects under different stimulation signals, and extracts the 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 in the human brain when in a state of instinctive fear, so as to understand the mechanism of instinctive fear.

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Abstract

一种基于脑电特征的本能恐惧研究方法，包括：输出不同的刺激信号（310），其中，不同的刺激信号对应不同恐惧刺激强度；获取被试者的原始脑电信号，对原始脑电信号进行降噪处理，得到脑电信号（330）；基于对脑电信号进行时频分析，得到脑电信息（390）；提取脑电信息中的脑电特征（370）；基于脑电特征进行分析，得到本能恐惧研究结果（350）。同时还提供了一种基于脑电特征的本能恐惧研究装置，针对人类本能恐惧研究的实验设计，能够深入研究人类的本能恐惧机制。

Description

基于脑电特征的本能恐惧研究方法和装置 技术领域
本申请涉及心理学领域，具体而言，本申请涉及一种基于脑电特征的本能恐惧研究方法和装置。
背景技术
人类的恐惧情感是物种进化与生存过程中最重要和最不可或缺的基本情绪表征之一，外界的恐惧刺激可以激发生物个体特定的防御行为产生，从而在其生存和繁衍中起到重要作用。恐惧包括条件性恐惧和本能恐惧，其中，本能恐惧是一种不需要后天学习就能产生的行为。
目前，对本能恐惧反应的研究，主要针对动物(如小鼠、昆虫等)进行本能恐惧行为的实验设计，通过恐惧刺激实验动物，观察实验动物面对恐惧刺激的反应(例如小鼠在面对恐惧刺激时会逃跑)，进而探索了解恐惧对动物造成的影响。
然而，人类的本能反应恐惧研究相较于动物的本能恐惧反应研究更为复杂，不同类型的恐惧刺激对人类造成的影响也不同，当受到恐惧刺激时，不同的人会做出不同的反应动作，有些人面对恐惧刺激的反应可能难以观测，因此，难以从行为学角度研究人类的本能恐惧反应，也就是说，针对动物的本能恐惧反应的实验设计，不适用于研究人类的本能恐惧反应。
由于没有针对人类的本能恐惧实验设计，缺乏可靠的实验数据以研究人类的本能恐惧反应，因此，对于恐惧刺激与人类的本能恐惧之间的关系、本能恐惧状态时人类产生的身体反应等各种有关本能恐惧的问题，目前仍未解答。
发明内容
本申请各实施例提供了一种基于脑电特征的本能恐惧研究方法、装置、电子设备及存储介质，可以解决相关技术中存在的缺少人类被试本能恐惧反应研究的实验范式的问题。所述技术方案如下：
根据本申请实施例的一个方面，一种基于脑电特征的本能恐惧研究方法，包括：输出不同的刺激信号，其中，不同的刺激信号对应不同恐惧刺激强度；获取被试者的原始脑电信号，对所述原始脑电信号进行降噪处理，得到脑电信号；基于对所述脑电信号进行时频分析，得到脑电信息；提取所述脑电信息中的脑电特征，其中，所述脑电特征用于指示在不同的所述刺激信号刺激下，所述被试者处于不同程度的本能恐惧状态时脑电信号产生的变化；基于所述脑电特征进行分析，得到本能恐惧研究结果，所述本能恐惧研究结果用于指示所述被试者处于本能恐惧状态时大脑活动产生的变化。
根据本申请实施例的一个方面，一种基于脑电特征的本能恐惧研究装置，包括：信号 输出模块，用于输出不同的刺激信号，其中，不同的刺激信号对应不同恐惧刺激强度；信号处理模块，用于获取被试者的原始脑电信号，对所述原始脑电信号进行降噪处理，得到脑电信号；时频分析模块，用于基于对所述脑电信号进行时频分析，得到脑电信息；特征提取模块，用于提取所述脑电信息中的脑电特征，其中，所述脑电特征用于指示在不同的所述刺激信号刺激下，所述被试者处于本能恐惧状态时脑电信号产生的变化；特征分析模块，用于基于所述脑电特征进行分析，得到本能恐惧研究结果，所述本能恐惧研究结果用于指示所述被试者处于本能恐惧状态时大脑活动产生的变化。
本申请提供的技术方案带来的有益效果是：
在上述技术方案中，通过本方案设计的人类被试本能恐惧反应研究的实验范式，不同恐惧刺激类型的刺激信号，获取被试者在不同刺激信号下产生的原始脑电信号，通过对原始脑电信号进行处理后提取得到脑电特征，探究不同恐惧刺激对大脑活动造成的影响，进而研究处于本能恐惧状态时，人类大脑产生的各种变化，从而了解人类本能恐惧的机制。
附图说明
为了更清楚地说明本申请实施例中的技术方案，下面将对本申请实施例描述中所需要使用的附图作简单地介绍。
图1是根据本申请实施例所涉及的实施环境的示意图；
图2是根据一示例性实施例示出的一种基于脑电特征的本能恐惧研究方法的流程图；
图3是图2对应实施例中步骤310之后的步骤在一个实施例的流程图；
图4是图3对应实施例中步骤430之后的步骤在一个实施例的流程图；
图5是图2对应实施例中步骤310在一个实施例的流程图；
图6是图5对应实施例中步骤330在一个实施例的流程图；
图7至图9是一应用场景中一种基于脑电特征的本能恐惧研究方法的具体实现示意图；
图10至图12是一应用场景中一种基于脑电特征的本能恐惧研究方法得到的脑电信息；
图13是根据一示例性实施例示出的一种基于脑电特征的本能恐惧研究装置的结构框图；
图14是根据一示例性实施例示出的一种电子设备的结构框图。
具体实施方式
下面详细描述本申请的实施例，所述实施例的示例在附图中示出，其中自始至终相同或类似的标号表示相同或类似的元件或具有相同或类似功能的元件。下面通过参考附图描述的实施例是示例性的，仅用于解释本申请，而不能解释为对本申请的限制。
本技术领域技术人员可以理解，除非特意声明，这里使用的单数形式“一”、“一个”、“所述”和“该”也可包括复数形式。应该进一步理解的是，本申请的说明书中使用的措辞“包括”是指存在所述特征、整数、步骤、操作、元件和/或组件，但是并不排除存在 或添加一个或多个其他特征、整数、步骤、操作、元件、组件和/或它们的组。应该理解，当我们称元件被“连接”或“耦接”到另一元件时，它可以直接连接或耦接到其他元件，或者也可以存在中间元件。此外，这里使用的“连接”或“耦接”可以包括无线连接或无线耦接。这里使用的措辞“和/或”包括一个或更多个相关联的列出项的全部或任一单元和全部组合。
如前所述，目前缺乏针对人类的本能恐惧实验设计，没有可靠的实验数据以研究人类的本能恐惧反应。
不同人对于恐惧的做出的反应是有差异的，当面对同一个恐惧刺激时，有些人的反应动作很容易被观察到，而有一些人的反应动作十分细微，难以观察，因此，以人们面对恐惧刺激做出的动作作为本能恐惧的研究依据是不合适的。也就是说，用于研究本能恐惧的实验指标，应当减少人类的主观影响，选用何种实验指标作为研究依据对于本能恐惧的研究意义重大。
不同类型的恐惧刺激，所引起的本能恐惧反应也可能有所不同，例如，一辆车突然撞上来或一只狗突然冲出来，所引起的人类本能恐惧反应的剧烈程度有所差异，因此，选择何种恐惧刺激对于本能恐惧的实验设计也意义非凡。
为此，本申请提供的基于脑电特征的本能恐惧研究方法，通过设计人类被试本能恐惧反应的实验范式，提取人类的脑电特征，进而有效地研究人类的本能恐惧反应，相应地，该本能恐惧研究方法适用于本能恐惧研究装置、该本能恐惧研究装置可部署于配置冯诺依曼体系结构的电子设备，例如，图1所示实施环境中的服务端130，该电子设备可以是台式电脑、笔记本电脑、服务器等等。
为使本申请的目的、技术方案和优点更加清楚，下面将结合附图对本申请实施方式作进一步地详细描述。
在下述方法实施例中，为了便于描述，以该方法各步骤的执行主体为服务器为例进行说明，但是并非对此构成具体限定。
图1为一种基于脑电特征的本能恐惧研究方法所涉及的一种实施环境的示意图。需要说明的是，该种实施环境只是一个适配于本发明的示例，不能认为是提供了对本发明的使用范围的任何限制。
该实施环境包括采集端110和服务端130。
具体地，采集端110，可以是具有采集脑电信号功能的电子设备，在此不构成具体限定。
服务端130，可以是具有计算功能的电子设备，例如，台式电脑、笔记本电脑、服务器、由多台服务器构成的计算机设备集群、多台服务器构成的云计算中心等等。服务端 130内存储有本能恐惧研究方法的程序，可以用于提供后台服务，例如，后台服务包括但不限于本能恐惧研究服务等等。
服务端130与采集端110之间通过有线或者无线等方式预先建立网络通信连接，并通过该网络通信连接实现服务端130与采集端110之间的数据传输。传输的数据包括但不限于：原始脑电信号等等。
通过采集端110与服务端130的交互，采集端110将原始脑电信号发送给服务端130，服务端130对原始脑电信号进行处理得到脑电信息，对脑电信息进行分析，得到脑电特征。
请参阅图2，本申请实施例提供了一种基于脑电特征的本能恐惧研究方法，该方法适用于电子设备，该电子设备可以是图1所示出实施环境中的服务器130。
在下述方法实施例中，为了便于描述，以该方法各步骤的执行主体为电子设备130为例进行说明，但是并非对此构成具体限定。
如图2所示，该方法可以包括以下步骤：
步骤310，输出不同的刺激信号。
首先说明的是，设计不同的刺激信号，以模拟不同危险情形，可以引发不同的本能恐惧反应，而这些不同的本能恐惧反应可以成为本能恐惧的研究依据。
其中，刺激信号的形式包括但不限于是视觉形式、声音形式、气味形式。以刺激信号的输出形式是视觉形式为例，刺激信号可以是一段视频，该视频可以模拟危险刺激，当然，不同的视频内容可以模拟不同恐惧刺激种类，例如恐惧逼近刺激、密集恐惧刺激、天敌恐惧刺激等等。进一步地，每个种类的恐惧刺激包含不同的恐惧刺激，其对应了不同恐惧刺激强度，例如，密集恐惧刺激中，密集度高的刺激信号的恐惧刺激强度，会比密集度低的刺激信号的恐惧刺激强度强。
在一个实施例中，刺激信号包括输出恐惧逼近后远离信号、恐惧逼近信号或恐惧远离信号，具体地，恐惧逼近信号可以是一个球快速膨胀的视频，该视频模拟危险突然出现；恐惧远离信号可以是一个大球快速变小的视频，模拟危险退去；输出恐惧逼近后远离可以是一个快速膨胀的球偏移到另一侧，模拟危险突然出现然后退去，上述刺激信号的恐惧刺激强度由大到小依次是恐惧逼近信号、恐惧逼近后远离、恐惧远离。
进行科学研究要保证实验得到的脑电信号是有效、可信的，实验得到的分析结果才有意义。因此，若被试者没有按照要求认真、专注地进行实验，则得到的脑电信号是无效的实验数据。
在一个实施例中，如图3所示，步骤310之后，还包括以下步骤：
步骤410，发送确认请求至被试者。
步骤430，接收被试者返回的确认结果。
步骤450，基于确认结果判断被试者是否专注于实验，若被试者专注于实验，则原始脑电信号为有效数据，否则，则为无效数据。
首先说明的是，发送确认请求的方式可以是语音播报的方式，也可以是使用显示设备显示请求框的方式，在此不作限定，相应地，接收被试者返回的确认结果的方式，可以是被试者通过语音返回确认结果的方式，也可以是被试者通过触摸屏幕方式返回确认结果，也可以被试者使用按键的方式返回确认结果，在此亦不作限定。
其中，确认请求用于请求被试者返回确认结果，该确认结果用于判断被试者是否专注于实验。
在一个实施例中，如图4所示，步骤430之后，可以包括以下步骤：
步骤610，将确认结果与设定信息作对比。
步骤630，若确认结果与设定信息相同，则被试者的原始脑电信号为有效数据；否则，则为无效数据。
关于设定信息，设定信息是为实验设定的刺激信号输出次数，换而言之，即在实验过程中，刺激信号实际出现的次数。
可以理解，若确认结果与实际结果相同，说明被试者认真完成实验任务，获取的实验数据(原始脑电信号)可信，可以基于该实验数据进行下一步数据处理；若确认结果与实际结果不同，说明被试者未认真完实验任务，获取的实验数据不可信，那么，结束当前实验。
进一步说明的是，在输出不同的刺激信号之间，可以设定刺激间隔时间，确保被试者在当前刺激信号的刺激下产生的本能恐惧反应，不会延续到后一次刺激信号输出的时间，导致影响在后一次刺激信号的本能恐惧反应。
在一个实施例中，如图5所示，步骤310可以包括以下步骤：
步骤510，确定刺激间隔时间。
步骤530，输出当前刺激信号，经过刺激间隔时间，输出后一次刺激信号。
补充说明的是，在输出不同刺激信号时，可以按照固定顺序输出刺激信号，例如，恐惧逼近信号-》恐惧远离信号-》恐惧逼近却远离信号-》恐惧逼近信号……；也可以随机选取恐惧刺激强度，输出刺激信号；还可以可以设定多个刺激间隔时间，在实验中，随机选取设定的刺激间隔时间，经过该刺激间隔时间后，输出后一次刺激信号，进一步地，可以同时随机选取恐惧刺激强度和刺激间隔时间，输出刺激信号。可以理解，增加输出刺激信号的随机性，被试者无法预先知道下一个出现的刺激信号是什么和/或下一次什么时候出现刺激信号，因此，被试者的本能恐惧反应更为明显。
步骤330，获取被试者的原始脑电信号，对原始脑电信号进行降噪处理，得到脑电信号。
其中，被试者是参加本能恐惧研究实验的志愿者，为了保证实验效果，被试者不能是 该实验的研究人员，因为提前得知具体的实验过程，会减轻被试者的本能恐惧反应，进而影响实验结果。
首先说明的是，人类在面对恐惧刺激时，大脑皮层的脑电信号会发生变化，脑电信号是一种客观的生物学指标，本方案设计的实验范式，将被试者本能恐惧状态的脑电信号作为研究依据，用于研究本能恐惧。
关于被试者原始脑电信号的获取，可以通过非侵入式的测量方法采集得到，例如，利用银/氯化银电极采集，在此不作限定。
需要说明的是，脑电信号是一种随机性很强的生理信号，节律种类多样，各种不同的情绪、心态都会影响脑电波的变化。因此，脑电信号具有高度时变敏感性，其信号极易被无关噪声污染。因此，未经处理的原始脑电信号包含了一系列的噪音，例如：高频噪音、低频噪音、眼电伪迹、肌电伪迹、心电伪迹等等。因此，在分析原始脑电信号前，需要对原始脑电信号的噪音进行处理，以获得清晰的脑电信号。
关于处理原始脑电信号中的高频噪音和低频噪音，可以利用低通滤波过滤高频噪音、利用高通滤波过滤低频噪音。并且，可以利用有限长单位冲激响应滤波器(FIR滤波器)向前和向后过滤原始脑电信号的噪音，具体地，可以将原始脑电信号按照顺序输入FIR滤波器，然后按照顺序将原始脑电信号前后翻转，再次输入FIR滤波器，最后再按照顺序将原始脑电信号前后翻转一次，以确保每个滤波器引入的相位延迟为零。
在一个实施例中，使用低通滤波器过滤原始脑电信号在40Hz以上的高频噪音，使用高通滤波器过滤原始脑电信号在0.5Hz以下的低频噪音。
关于处理原始脑电信号中各种伪迹，如图6所示，步骤330可以包括以下步骤：
步骤331，基于独立成分分析对原始脑电信号进行分离处理，得到至少一组含伪迹的第一脑电信号以及不含伪迹的第二脑电信号。
步骤333，去除各第一脑电信号中的伪迹，基于去除伪迹的各第一脑电信号和第二脑电信号进行重构，得到脑电信号。
如前所述，原始脑电信号中包括眼电伪迹、肌电伪迹、心电伪迹等伪迹，这些伪迹不属于大脑活动产生的电信号，因此，需要去除各伪迹，提取纯净的脑电信号。
其中，第二脑电信号是指使用独立成分分析对原始脑电信号进行提取，得到的不含伪迹的脑电独立分量，相反地，第一脑电信号是指使用独立成分分析对原始脑电信号进行提取，得到的含伪迹的脑电独立分量
具体地，对原始脑电信号进行独立成分分析，确定脑电独立分量、含有眼电伪迹的独立分量、含有肌电伪迹的独立分量和含有心电伪迹的独立分量；对含有眼电伪迹的独立分量，去除眼电伪迹，对含有肌电伪迹的独立分量，去除肌电伪迹，对含有心电伪迹的独立分量，去除心电伪迹；基于去除眼电伪迹的独立分量、去除肌电伪迹的独立分量、去除心电伪迹的独立分量和脑电独立分量进行重构，得到去除各类型伪迹的脑电信号。
在一个实施例中，将去除眼电伪迹的独立分量、去除肌电伪迹的独立分量、去除心电伪迹的独立分量和脑电独立分量，按照以上各独立分量在原始脑电信号的权重，利用反投影法进行重构，得到去除各类型伪迹的脑电信号。
步骤350，基于对脑电信号进行时频分析，得到脑电信息。
其中，脑电信号包含了大量的生理信息，可以反映大脑活动的变化特点，通过提取脑电信息包含的生理信息，对这些生理信息进行分析，研究脑电信号中与本能恐惧反应有关的部分，进而了解处于本能恐惧状态时大脑活动的特点。
其中，对于脑电信号的分析方式有很多种，例如，时域分析、频域分析，时域分析能快速得到刺激信号所引起的脑电信号幅值的变化，但是无法得到与脑电信号的频率有关的脑电信息；频域分析可以得到频率上的能量值分布，但是频域分析只适用于稳态信号，而脑电信号是非稳态信号。因此，结合时域分析和频域分析的特点，可以对脑电信号进行时频分析，提取得到脑电信息，该脑电信息是包括在不同刺激信号下，脑电信号的时间、频率和能量关系的信息。
当然，不同频段的脑电信息可以反映在处于本能恐惧反应时，脑电信号的变化情况。因此，可以基于脑电信息的频率，根据设定规则对脑电信息进行频段划分，得到各频段的脑电信息。
在一个实施例中，将脑电信息根据设定规则划分为Alpha段(8-13Hz)、Beta段(14-30Hz)、Theta段(5-7Hz)、Delta段(1-4Hz)和Gamma段(>30Hz)。
补充说明的是，实验时，可以通过脑电采集装置采集被试者各脑区的脑电波，得到若干组原始脑电信号，相应地，每组原始脑电信号都有与之对应的脑区，例如：左额、右额、右枕等。基于此，由原始脑电信号处理得到的脑电信息，也有与之对应的脑区。
步骤370，提取脑电信息中的脑电特征。
如前所述，脑电信息是包括在不同刺激信号下，脑电信号的时间、频率和能量关系的信息，而脑电特征用于指示在不同的刺激信号刺激下，被试者处于不同程度的本能恐惧状态时脑电信息产生的变化。
该脑电特征包括但不限于是脑电信息的波谷、波峰，例如，在恐惧逼近信号的刺激下，被试者在本能恐惧状态时，脑电信号频率为8-14Hz处生成的能量波谷比其他频段生成的的能量波谷深。
步骤390，基于脑电特征进行分析，得到本能恐惧研究结果。
其中，本能恐惧研究结果是指人类处于本能恐惧状态时大脑活动产生的变化，例如，大脑活动变得活跃、大脑活动受到抑制等等。
如前所述，基于脑电信息的频率划分得到了各频段的脑电信息，并且，脑电信息有与之对应的脑区，基于此，本能恐惧研究结果也可以反映不同频段、不同脑区的大脑活动产生的变化。
在一个实施例中，脑电特征为在恐惧逼近信号的刺激下，Alpha段生成的能量波谷比其他频段的能量波谷深，那么，本能恐惧研究结果是Alpha段大脑活动受到的抑制，比其它频段的大脑活动受到的抑制强。
在一个实施例中，脑电特征为被试者受到了恐惧逼近后远离信号、恐惧逼近信号，和恐惧远离信号的刺激，在恐惧逼近信号生成的能量波谷，比恐惧逼近后远离信号、恐惧远离信号生成的能量波谷深，那么，本能恐惧研究结果是在恐惧逼近信号的刺激下，Alpha段大脑活动受到的抑制最强。
在一个实施例中，脑电特征为被试者后脑区生成的能量波谷深，因此，本能恐惧研究结果是被试者后脑区的大脑活动受到较大抑制。
通过上述过程，设计不同的刺激信号，获取被试者在不同刺激信号下产生的原始脑电信号，通过对原始脑电信号进行处理后提取得到脑电特征，探究不同类型的恐惧刺激对大脑活动造成的影响，进而探究处于本能恐惧状态时，人类大脑产生的各种变化，从而了解人类本能恐惧的机制。
图7是一应用场景中基于脑电特征的本能恐惧研究方法的具体实现示意图。
步骤810，输出刺激信号。
其中，本应用场景中，被试者包括一个健康人和一个孤独症谱系障碍患者，健康人是指未患有精神障碍的普通人，可以理解，本实验所选取两个被试者面对恐惧刺激时的本能恐惧反应有所差异，通过对比在刺激信号下两个被试者的脑电信号，可以更好地观察和分析刺激信号对人类本能恐惧反应造成的影响。
在进行测试之前，首先向被试者说明实验内容，要求被试者在实验过程中保持专注，全程目光位于屏幕中心部分，并计算在实验中出现的球总数。然后引导被试者坐在离显示器一段距离的位置。
如图8所示，被试者坐在离显示器一段距离的位置，具体地，根据显示器大小确定该距离长短，以确保被试者的视野清晰，例如，当显示器的大小为34厘米乘61厘米(34cm*61cm)时，被试者坐在距离显示器75厘米的位置。
本实验中，刺激信号包括球远离信号(恐惧远离信号)、球逼近信号(恐惧逼近信号)和球逼近后远离信号(恐惧逼近后远离信号)，在实验时，随机输出以上三种刺激信号，其中，每种类型的刺激信号共有24次实验，球的图像在0.3秒内从1°到15°的视角隐约可见，在一半实验中球会随机出现在屏幕左侧，另一半实验中球会随机出现在屏幕右侧，这样设计的目的是为了增加输出刺激信号的随机性，使得被试者的本能恐惧反应更显著。
步骤830，采集被试者的原始脑电信号。
关于原始脑电信号的采集，在实验进行时，可以通过银/氯化银电极，采集得到被试者的脑电信号。其中，银/氯化银电极的数量可以是32个、64个或128个，在此不作限定。本实验中，采用32个银/氯化银电极采集被试者的原始脑电信号，如图9所示，32 个银/氯化银电极按照国际10-20系统排列，参考电极安放于双侧乳突。接地电极安放于额极FP1、额极FP2和额中线Fz的中心，银/氯化银电极嵌入弹性帽中，并由被试者佩戴在头上。
可以理解，银/氯化银电极可以采集被试者大脑一定区域的原始脑电信号，例如，贴在被试者后脑区的银/氯化银电极，采集得到的是被试者后脑区的原始脑电信号。因此，采用32个银/氯化银电极采集被试者的原始脑电信号，可以得到32组原始脑电信号，这32组原始脑电信号对应被试者大脑的32块位置。原始脑电信号以1000Hz的频率采样。电极间阻抗保持在5k欧姆以下。数据由脑视觉记录仪软件记录到脑电图格式。
步骤850，接收被试者返回的确认数值。若确认数值与实际数值相同，则继续实验，反之，则结束当前实验。
在实验结束后，被试者将实验过程中计数得到的球总数，通过按键返回确认数值，该确认数值指示被试者计算得到的球总数。
若确认数值与实际数值相同，也就是说，被试者计算得到的球总数与实际出现在实验中的球总数相同，此时，被试者认真完成实验任务，获得的实验数据(原始脑电信号)可信，可以基于该实验数据进行下一步数据处理；若该确认数值与实际数值不同，则被试者计算得到的球总数与实际出现在实验中的球总数不同，此时，被试者未认真完成实验任务，获得的实验数据不可信，结束当前实验。
步骤870，对原始脑电信号进行降噪处理，得到脑电信号。
采集得到的原始脑电信号包括一系列的噪声，例如高频噪音、低频噪音、各种伪迹等，本实验中，通过低通滤波器过滤原始脑电信号在40Hz以上的高频噪音，通过高通滤波器过滤原始脑电信号在0.5Hz以下的低频噪音。对于各种伪迹，则通过独立成分分析去除伪迹后，再利用反投影法重构得到清晰的不含伪迹的脑电信号。
步骤890，对脑电信号进行时频分析，得到脑电信息。
首先说明的是，脑电信号可以反映在刺激信号下受试者脑电波的变化。不同刺激信号对受试者脑电波的影响不同，因此，可以基于刺激信号的刺激信号类型和持续时间，对脑电信号分段，可以理解，分段后的脑电信号能更直观反应不同的刺激信号对脑电波的影响。例如，依次输出球逼近信号、球逼近后远离信号和球远离信号至被试者面前的屏幕，每个刺激信号持续时间为0.3s，因此，基于上述3个刺激信号将脑电信号划分为3段，每段脑电信号为0.3s，分别是Loom段、Miss段和Far段。
然后，基于划分的脑电信号段进行时频分析，得到脑电信息，该脑电信息是包括在不同刺激信号下，脑电信号的时间、频率和能量关系的信息，进而可知在不同刺激信号下，健康人与孤独症谱系障碍患者的脑电信号变化，具体地，脑电信息中能量的变化可以反映大脑活动情况，可以理解，脑电信息中能量越强，则表示大脑活动越剧烈，反之，大脑活动越微弱。当然，为了进一步观察刺激信号对不同被试者的大脑活动造成的影响，可以基于脑电信息的频率将其分成多个频段，以此观察刺激信号对不同频段的大脑活动造成的影 响。那么，将脑电信号根据频率划分为Alpha段(8-13Hz)、Beta段(14-30Hz)、Theta段(5-7Hz)、Delta段(1-4Hz)和Gamma段(>30Hz)。
步骤811，基于脑电信息，提取得到脑电特征，分析脑电特征，得到本能恐惧研究结果。
如图10所示，为球逼近信号的刺激下，两个被试者脑电信号对应的时频图。可以发现，在球逼近信号的刺激下，健康人大脑后部的Alpha段振荡活动受到了极大抑制，而孤独症谱系障碍患者大脑后部的Alpha段振荡活动变化不大。
关于时频功率谱，为了进一步确定健康人Alaph段脑电信号与孤独症谱系障碍患者Alaph段脑电信号之间的显著差异由球逼近信号引起的，基于脑电信息分别计算健康人脑电信号的Loom段、Miss段和Far段的时频功率谱，以及孤独症谱系障碍患者脑电信号的Loom段、Miss段和Far段的时频功率谱。
如图11所示，可知，对于健康人，在球逼近信号刺激下对Alpha段振荡活动的抑制显著强于其他两种刺激信号，而对于孤独症谱系障碍患者，三种刺激信号对Alpha段振荡活动的影响没有显著差异，进而，在球逼近信号刺激下，健康人脑电信号的Alpha段会受到较大抑制，而孤独症谱系障碍患者的Alpha段基本没有变化。
其中，可以利用Chronux toolbox，使用大小1000ms和步长为50ms的移动窗口计算时频功率谱，相对于刺激信号的开始，从-2秒到2秒，频率分辨率为1Hz。
当然，也可以基于时频功率谱，进一步计算健康人脑电信号的Loom段、Miss段和Far段的相对功率谱，以此对比各类型刺激信号对脑电信号的影响。其中，可以通过(刺激信号前功率-刺激信号后功率)/刺激信号前功率，计算得到各刺激信号对应的相对功率谱。
如图12所示，为健康人的Alhpa段脑电信号对应的相对功率谱。可知，球逼近信号的刺激对健康人Alhpa段的脑电信号的抑制显著强于其他两种刺激信号。
本实验过程中发现，在刺激信号的刺激下，孤独症谱系障碍患者的脑电特征与健康人的脑电特征有显著差异，那么，这些含有显著差异的脑电特征，可以为筛查孤独症谱系障碍患者提供一个诊断指标，这些诊断指标更加客观。当然，患有其他精神障碍的患者受到刺激信号刺激，生成的脑电指标也有所差异，相应地，包含这些差异的脑电特征，也可以为筛查对应精神障碍提供一个诊断指标。
在本应用场景中，通过设置对照实验组，获取两个被试者的原始脑电信号，对两组原始脑电信号进行处理得到对应的脑电信息，提取两组脑电信息中的脑电特征，通过对比脑电特征之间的差异，探究不同恐惧刺激对大脑活动造成的影响，进而探究处于本能恐惧状态时，人类大脑产生的各种变化，从而了解本能恐惧的机制。
下述为本申请装置实施例，可以用于执行本申请所涉及的基于脑电特征的本能恐惧研究方法。对于本申请装置实施例中未披露的细节，请参照本申请所涉及的基于脑电特征的 本能恐惧研究方法的方法实施例。
请参阅图13，本申请实施例中提供了一种基于脑电特征的本能恐惧研究装置900，包括但不限于：信号输出模块910、信号处理模块930、时频分析模块950、特征提取模块970以及特征分析模块990。
其中，信号输出模块910，用于输出不同的刺激信号，其中，不同的刺激信号对应不同恐惧刺激强度。
信号处理模块930，用于获取被试者的原始脑电信号，对原始脑电信号进行降噪处理，得到脑电信号。
时频分析模块950，用于基于对脑电信号进行时频分析，得到脑电信息。
特征提取模块970，用于提取脑电信息中的脑电特征，其中，脑电特征用于指示在不同的刺激信号刺激下，被试者处于本能恐惧状态时脑电信号产生的变化；
特征分析模块990，用于基于脑电特征进行分析，得到本能恐惧研究结果，本能恐惧研究结果用于指示被试者处于本能恐惧状态时大脑活动产生的变化。
需要说明的是，上述实施例所提供的基于脑电特征的本能恐惧研究装置在进行基于脑电特征的本能恐惧研究时，仅以上述各功能模块的划分进行举例说明，实际应用中，可以根据需要而将上述功能分配由不同的功能模块完成，即基于脑电特征的本能恐惧研究装置的内部结构将划分为不同的功能模块，以完成以上描述的全部或者部分功能。
另外，上述实施例所提供的基于脑电特征的本能恐惧研究装置与基于脑电特征的本能恐惧研究方法的实施例属于同一构思，其中各个模块执行操作的具体方式已经在方法实施例中进行了详细描述，此处不再赘述。
请参阅图14，本申请实施例中提供了一种电子设备4000，该电子设备4000可以包括：台式电脑、笔记本电脑、服务器等。
在图14中，该电子设备4000包括至少一个处理器4001、至少一条通信总线4002以及至少一个存储器4003。
其中，处理器4001和存储器4003相连，如通过通信总线4002相连。可选地，电子设备4000还可以包括收发器4004，收发器4004可以用于该电子设备与其他电子设备之间的数据交互，如数据的发送和/或数据的接收等。需要说明的是，实际应用中收发器4004不限于一个，该电子设备4000的结构并不构成对本申请实施例的限定。
处理器4001可以是CPU(Central Processing Unit，中央处理器)，通用处理器，DSP(Digital Signal Processor，数据信号处理器)，ASIC(Application Specific Integrated Circuit，专用集成电路)，FPGA(Field Programmable Gate Array，现场可编程门阵列)或者其他可编程逻辑器件、晶体管逻辑器件、硬件部件或者其任意组合。其可以实现或执行结合本申请公开内容所描述的各种示例性的逻辑方框，模块和电路。处理器4001也可以是实现计算功能的组合，例如包含一个或多个微处理器组合，DSP和微处理器的组合等。
通信总线4002可包括一通路，在上述组件之间传送信息。通信总线4002可以是PCI(Peripheral Component Interconnect，外设部件互连标准)总线或EISA(Extended Industry Standard Architecture，扩展工业标准结构)总线等。通信总线4002可以分为地址总线、数据总线、控制总线等。为便于表示，图14中仅用一条粗线表示，但并不表示仅有一根总线或一种类型的总线。
存储器4003可以是ROM(Read Only Memory，只读存储器)或可存储静态信息和指令的其他类型的静态存储设备，RAM(Random Access Memory，随机存取存储器)或者可存储信息和指令的其他类型的动态存储设备，也可以是EEPROM(Electrically Erasable Programmable Read Only Memory，电可擦可编程只读存储器)、CD-ROM(Compact Disc Read Only Memory，只读光盘)或其他光盘存储、光碟存储(包括压缩光碟、激光碟、光碟、数字通用光碟、蓝光光碟等)、磁盘存储介质或者其他磁存储设备、或者能够用于携带或存储具有指令或数据结构形式的期望的程序代码并能够由计算机存取的任何其他介质，但不限于此。
存储器4003上存储有计算机程序，处理器4001通过通信总线4002读取存储器4003中存储的计算机程序。
该计算机程序被处理器4001执行时实现上述各实施例中的基于脑电特征的本能恐惧研究方法。
此外，本申请实施例中提供了一种存储介质，该存储介质上存储有计算机程序，该计算机程序被处理器执行时实现上述各实施例中的基于脑电特征的本能恐惧研究方法。
本申请实施例中提供了一种计算机程序产品，该计算机程序产品包括计算机程序，该计算机程序存储在存储介质中。计算机设备的处理器从存储介质读取该计算机程序，处理器执行该计算机程序，使得该计算机设备执行上述各实施例中的基于脑电特征的本能恐惧研究方法。
与相关技术相比，通过本方案设计的人类被试本能恐惧反应研究的实验范式，输出不同的刺激信号，获取被试者在不同刺激信号下产生的原始脑电信号，通过对原始脑电信号进行处理后提取得到脑电特征，探究不同恐惧刺激对大脑活动造成的影响，进而探究处于本能恐惧状态时，人类大脑产生的各种变化，从而了解本能恐惧的机制。
应该理解的是，虽然附图的流程图中的各个步骤按照箭头的指示依次显示，但是这些步骤并不是必然按照箭头指示的顺序依次执行。除非本文中有明确的说明，这些步骤的执行并没有严格的顺序限制，其可以以其他的顺序执行。而且，附图的流程图中的至少一部分步骤可以包括多个子步骤或者多个阶段，这些子步骤或者阶段并不必然是在同一时刻执行完成，而是可以在不同的时刻执行，其执行顺序也不必然是依次进行，而是可以与其他步骤或者其他步骤的子步骤或者阶段的至少一部分轮流或者交替地执行。
以上所述仅是本申请的部分实施方式，应当指出，对于本技术领域的普通技术人员来 说，在不脱离本申请原理的前提下，还可以做出若干改进和润饰，这些改进和润饰也应视为本申请的保护范围。

Claims (10)

  1. 一种基于脑电特征的本能恐惧研究方法，其特征在于，包括：
    输出不同的刺激信号，其中，不同的刺激信号对应不同恐惧刺激强度；
    获取被试者的原始脑电信号，对所述原始脑电信号进行降噪处理，得到脑电信号；
    基于对所述脑电信号进行时频分析，得到脑电信息；
    提取所述脑电信息中的脑电特征，其中，所述脑电特征用于指示在不同的所述刺激信号刺激下，所述被试者处于不同程度的本能恐惧状态时脑电信息产生的变化；
    基于所述脑电特征进行分析，得到本能恐惧研究结果，所述本能恐惧研究结果用于指示所述被试者处于本能恐惧状态时大脑活动产生的变化。
  2. 如权利要求1所述的方法，其特征在于，所述基于对所述脑电信号进行时频分析，得到脑电信息之后，所述方法还包括：
    基于所述脑电信息的频率，根据设定规则对所述脑电信息进行频段划分，得到若干个频段的所述脑电信息。
  3. 如权利要求2所述的方法，其特征在于，所述方法还包括：
    根据设定规则将所述脑电信息划分为Alpha段、Beta段、Theta段、Delta段和Gamma段；
    基于各所述频段的所述脑电信息进行提取，得到所述脑电特征。
  4. 如权利要求1所述的方法，其特征在于，所述方法还包括：
    获取所述被试者若干个脑区的原始脑电信号；
    基于各所述脑区的所述原始脑电信号，得到对应于各所述脑区的脑电信息；
    基于各所述脑区的所述脑电信息进行特征提取，得到所述脑电特征。
  5. 如权利要求1所述的方法，其特征在于，所述输出不同的刺激信号，包括：输出恐惧逼近后远离信号、恐惧逼近信号或恐惧远离信号。
  6. 如权利要求1所述的方法，其特征在于，所述输出不同的刺激信号，所述方法还包括：
    设定刺激间隔时间，所述刺激间隔时间用于指示所述刺激信号输出的间隔时间；
    输出当前刺激信号，经过刺激间隔时间，输出后一次刺激信号。
  7. 如权利要求6所述的方法，其特征在于，所述输出不同的刺激信号，包括：
    随机选取所述恐惧刺激强度，输出所述刺激信号，和/或，随机选取所述刺激间隔时间，输出所述刺激信号。
  8. 如权利要求1至7任一项所述的方法，其特征在于，所述获取被试者的原始脑电信号之后，还包括：
    发送确认请求至所述被试者，所述确认请求用于请求所述被试者返回确认结果；
    接收所述被试者返回的所述确认结果；
    基于所述确认结果判断所述被试者是否专注于实验，若所述被试者未专注于实验，则 所述原始脑电信号为有效数据，否则，则为无效数据。
  9. 如权利要求8所述的方法，其特征在于，接收所述被试者返回的所述确认结果之后，所述方法还包括：
    将所述确认结果与设定信息作对比，其中，所述确认结果是所述被试者在实验过程中对所述刺激信号输出次数的计数结果，所述设定信息是为实验设定的刺激信号输出次数；
    若确认结果与设定信息相同，则所述被试者的所述原始脑电信号为有效数据；否则，则为无效数据。
  10. 一种基于脑电特征的本能恐惧研究装置，其特征在于，包括：
    信号输出模块，用于输出不同的刺激信号，其中，不同的刺激信号对应不同恐惧刺激强度；
    信号处理模块，用于获取被试者的原始脑电信号，对所述原始脑电信号进行降噪处理，得到脑电信号；
    时频分析模块，用于基于对所述脑电信号进行时频分析，得到脑电信息；
    特征提取模块，用于提取所述脑电信息中的脑电特征，其中，所述脑电特征用于指示在不同的所述刺激信号刺激下，所述被试者处于本能恐惧状态时脑电信号产生的变化；
    特征分析模块，用于基于所述脑电特征进行分析，得到本能恐惧研究结果，所述本能恐惧研究结果用于指示所述被试者处于本能恐惧状态时大脑活动产生的变化。
PCT/CN2022/131228 2022-11-04 2022-11-10 基于脑电特征的本能恐惧研究方法和装置 WO2024092869A1 (zh)

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