Method for measuring degree of decline in driver's dangerous perception induced by negative emotion based on gamma electroencephalogram signal

Abstract

The application discloses a measurement method of negative emotion induced driver dangerous perception force decline degree based on gamma brain electrical signals, characterized by setting neutral emotion and negative emotion two kinds of dangerous signal perception test tasks, collecting brain electrical signals of the measured personnel in the two kinds of tasks; calculating the baseline normalized wave band power value corresponding to the gamma wave band in the time window 550-750 ms in the Fz channel of the measured personnel under different tasks; respectively calculating the index values under different tasks and conditions, and the statistical test of the difference between each other p If and, it indicates that the measurement is valid, and the negative emotion induced dangerous perception force decline degree of the measured personnel is, and the larger the value is, the greater the decline degree is; the method has the advantages of objectivity and direct evaluation of the decline degree of the dangerous perception force of the measured personnel under negative emotion.

Description

Technical Field

[0001] This invention relates to the field of electroencephalography (EEG) neurofunctional measurement technology, and in particular to a method for measuring the degree of decline in a driver's danger perception induced by negative emotions based on gamma EEG signals. Background Technology

[0002] Hazard perception refers to a driver's sensitivity to road hazard signals. In driving operations, a driver's level of hazard perception is crucial for driving safety. Existing research shows that emotions are a significant factor influencing driver decision-making, especially negative emotions, which can easily lead to distraction, reduced perceptual sensitivity, and delayed decision-making, resulting in a significant decline in a driver's road hazard perception. Furthermore, individual psychological differences among drivers mean that the degree to which negative emotions affect their perception varies considerably. Accurately identifying the extent to which negative emotions influence an individual driver's hazard perception is crucial for assessing their ability to withstand emotional interference, rationally scheduling driving tasks, dynamically regulating driver emotional levels, and avoiding potential driving hazards. However, currently, there is no accurate and objective assessment method to determine the degree to which negative emotions induce a decline in a driver's hazard perception. Summary of the Invention

[0003] The technical problem to be solved by the present invention is to provide a method for measuring the degree of decline in a driver’s danger perception induced by negative emotions based on gamma electroencephalogram (EEG) signals. This method can accurately and efficiently assess the degree of decline in an individual’s danger perception by analyzing the conditional differences in EEG signals.

[0004] The technical solution adopted by this invention to solve the above problems is: a method for measuring the degree of decline in a driver's danger perception induced by negative emotions based on gamma electroencephalogram (GEG) signals, comprising the following specific steps: (1) The subjects were fitted with multi-channel EEG measurement devices to record EEG signals; (2) In the simulated driving environment, two types of tasks were set up: a neutral emotion danger signal perception test and a negative emotion danger signal perception test. Each type of task included two types of stimulus conditions: with danger signal and without danger signal. In the two types of tasks, the EEG signals of the test subjects were collected for 10 minutes to obtain the raw EEG data of the test subjects. (3) Preprocess the raw EEG data, including signal amplification, segmentation, signal noise reduction, bandpass filtering and artifact removal. (4) Based on the preprocessed EEG data, calculate the baseline normalized band power value (unit: dB) of the γ band (31~45Hz part) within a time window of 550~750ms after the stimulus signal appears for the test subjects under different tasks and conditions. (5) Compare the baseline normalized power values ​​of the γ band when danger signals are perceived under neutral emotional conditions. P_ dB 中性.危险 Baseline normalized power value of the gamma band when danger signals are perceived under negative emotions P_dB 负性.危险 The difference between them, and the baseline normalized power value of the gamma band when no danger signal is perceived under neutral mood. P_dB 中性.安全 Baseline normalized power value of the gamma band when no danger signals are perceived under negative emotions P_dB 负性.安全 The difference between them was calculated. P_dB 中性.危险 and P_dB 负性.危险 Statistical tests of differences between p value The mean was calculated. mean ;Calculation obtained P_dB 中性.安全 and P_dB 负性.安全 Statistical tests of differences between p value Then, calculate the degree of decline in the test subject's danger perception: if and and This indicates that the measurement was valid, and the degree of decline in danger perception induced by the test subject's negative emotions was [value missing]. The larger the value, the greater the degree of decrease; if the condition and and If the result is not valid, then the measurement is invalid.

[0005] Furthermore, in step (2), the test task is a danger signal judgment task. The two conditions in the task are two stimulus conditions: one with danger signal and one without danger signal. The test extracts the EEG data of the test subject within 2000ms from the appearance of the stimulus. The test task under each condition is ensured to be performed 20 times or more.

[0006] Furthermore, in step (3), during the preprocessing of the raw EEG data, the raw EEG data is subjected to bandpass filtering of 0.5 to 45 Hz, and independent component analysis and data reconstruction are performed using the FastICA algorithm based on the principle of maximum negative entropy, so as to effectively remove artifact interference; and the preprocessed EEG data retains the γ band as the target data for subsequent analysis.

[0007] Furthermore, in step (4), the method for calculating the baseline-normalized band power value is as follows: (4-1) For each type of task, the test time is 10 minutes. Wavelet transform is performed on the preprocessed EEG data to obtain the band power value of the γ band in the Fz electrode channel in the interval of 550-750ms after the stimulus signal appears. (4-2) For the four scenarios of perceiving danger with neutral emotions, perceiving danger with negative emotions, not perceiving danger with neutral emotions, and not perceiving danger with negative emotions, the band power is calculated separately. The baseline is set at -300ms to -100ms before the stimulus, and the baseline-normalized band power value is obtained. The calculation relationship is as follows: , , , , , , , , Where: symbol P This represents the γ-band power value under a single stimulus condition. k This is the frequency value, ranging from 31 to 45 Hz; symbol This represents the γ-band power value (unit: dB) obtained after baseline normalization under single stimulus conditions, with the symbol... This represents the gamma band power value during the baseline period from -300ms to -100ms before stimulation.

[0008] Furthermore, in step (5), the mean mean The calculation formula is as follows: , , Where: symbol This represents the average value under multiple stimulus conditions.

[0009] Compared with the prior art, the advantages of the present invention are: (1) This method objectively and directly assesses the degree of decline in the risk perception of test subjects under negative emotions by analyzing the task-specific changes of the γ index in the EEG signal data. It has important reference value for measuring the driver's ability to resist emotional interference in risk perception, rationally scheduling driving tasks, dynamically regulating the driver's emotional level, and avoiding potential driving hazards. (2) This method uses baseline-normalized γ-band power to extract neural activity features in the critical time window of 550-750ms in the Fz electrode channel. It is simple, sensitive and efficient for recognizing the brain’s danger perception ability under negative emotions. Attached Figure Description

[0010] Figure 1 This is a flowchart of the present invention. Detailed Implementation

[0011] The present invention will be further described in detail below with reference to the accompanying drawings and embodiments.

[0012] As shown in the figure, the method for measuring the degree of decline in a driver's danger perception induced by negative emotions based on gamma electroencephalogram (GEG) signals includes the following specific steps: (1) The subject wears a multi-channel EEG measurement device to record EEG signals; the multi-channel EEG measurement device can use an existing EEG acquisition system, such as the EMOTIV EPOC FlexSaline Sensor Kit EEG acquisition system. (2) In a simulated driving environment, set up neutral emotion danger signal perception test tasks and negative emotion danger signal perception test tasks: In the task, neutral emotion pictures and negative emotion pictures are selected from the Chinese emotion picture library to regulate the emotions (neutral and negative) of the test subjects. Since emotions have a retention effect, it is necessary to conduct the neutral emotion test task first and then the negative emotion test task. Then, use the stimulus pictures (or videos) of the driving scene to conduct the danger perception judgment task: the pedestrian on the side of the lane may be preparing to turn to cross the road (danger signal) or is in a straight state (no danger signal). The test subjects are asked to judge whether it is dangerous and press the button to provide feedback. Therefore, each type of task includes two stimulus conditions: with danger signal and without danger signal. In the two types of tasks, the test subjects' EEG signals are collected for 10 minutes. The EEG data of the test subjects within 2000ms from the appearance of the stimulus are extracted. The test tasks for each type of condition of each task are ensured to be 20 times or more. (3) Preprocessing of raw EEG data includes signal amplification, segmentation, signal denoising, bandpass filtering, and artifact removal. Specifically, bandpass filtering and artifact removal are performed on the raw EEG data with a bandpass filter of 0.5 to 45 Hz, and independent component analysis and data reconstruction are performed using the FastICA algorithm based on the principle of maximum negative entropy to effectively remove interference from electrooculography, electromyography, and other artifacts. Furthermore, the preprocessed EEG data retains γ (31 to 45 Hz) as the target data for subsequent analysis, providing a clear and reliable basic signal for danger perception analysis. (4) Based on the preprocessed EEG data, calculate the baseline-normalized band power values ​​of the γ band (31-45Hz portion) within a time window of 550-750ms after the stimulus signal appears for the subjects under different tasks and conditions; specifically: (4-1) For each type of task, the test time is 10 minutes. Wavelet transform is performed on the preprocessed EEG data to obtain the band power value of the γ band in the Fz electrode channel in the interval of 550-750ms after the stimulus signal appears. (4-2) For the four scenarios of perceiving danger with neutral emotions, perceiving danger with negative emotions, not perceiving danger with neutral emotions, and not perceiving danger with negative emotions, the band power is calculated separately. The baseline is set at -300ms to -100ms before the stimulus, and the baseline-normalized band power value is obtained. The calculation relationship is as follows: , , , , , , , , Where: symbol P This represents the γ-band power value under a single stimulus condition. k These are frequency values, ranging from 31 to 45 Hz; [symbol] This represents the γ-band power value (unit: dB) obtained after baseline normalization under single stimulus conditions, with the symbol... This represents the gamma band power value during the baseline period from -300ms to -100ms before stimulation. (5) Compare the baseline normalized index of the γ band when danger signals are perceived under neutral emotional conditions. P_dB 中性.危险 Baseline normalized index corresponding to the gamma band when danger signals are perceived under negative emotions P_dB 负性.危险 The differences between them, and the baseline normalized index corresponding to the gamma band when no danger signals are perceived under neutral mood. P_dB 中性.安全 Baseline normalized index corresponding to the gamma band when no danger signals are perceived under negative emotions P_dB 负性.安全 The difference between them was calculated. P_dB 中性.危险 and P_ dB 负性.危险 Statistical tests of differences between p value The mean was calculated. mean ;Calculation obtained P_dB 中性.安全 and P_dB 负性.安全 Statistical tests of differences between p value Then, calculate the degree of decline in the test subject's danger perception: if and and This indicates that the measurement was valid, and the degree of decline in danger perception induced by the test subject's negative emotions was [value missing]. The larger the value, the greater the degree of decrease; if the condition and and If the condition is not met, then the measurement is invalid. mean mean The calculation formula is as follows: , , Where: symbol This represents the average value under multiple stimulus conditions.

[0013] The scope of protection of this invention includes, but is not limited to, the above embodiments. The scope of protection is defined by the claims. Any substitutions, modifications, or improvements to this technology that are easily conceived by those skilled in the art fall within the scope of protection of this invention.

Claims

1. A method for measuring the degree of decline in a driver's danger perception induced by negative emotions based on gamma electroencephalogram (GEG) signals, characterized in that... The specific steps include the following: (1) The subjects were fitted with multi-channel EEG measurement devices to record EEG signals; (2) In the simulated driving environment, two types of tasks were set up: a neutral emotion danger signal perception test and a negative emotion danger signal perception test. Each type of task included two types of stimulus conditions: with danger signal and without danger signal. In the two types of tasks, the EEG signals of the test subjects were collected for 10 minutes to obtain the raw EEG data of the test subjects. (3) Preprocess the raw EEG data, including signal amplification, segmentation, signal noise reduction, bandpass filtering and artifact removal. (4) Based on the preprocessed EEG data, calculate the baseline normalized band power value of the γ band within a time window of 550-750ms after the appearance of the stimulus signal for the subject under different tasks and conditions. (5) Compare the baseline normalized power values ​​of the γ band when danger signals are perceived under neutral emotional conditions. P_dB 中性.危险 Baseline normalized power value of the gamma band when danger signals are perceived under negative emotions P_dB 负性.危险 The difference between them, and the baseline normalized power value of the γ band when no danger signal is perceived under neutral mood. P_dB 中性.安全 Baseline normalized power value of the gamma band when no danger signals are perceived under negative emotions P_dB 负性.安全 The difference between them was calculated. P_dB 中性.危险 and P_dB 负性.危险 Statistical tests of differences between p value The mean was calculated. mean ;Calculation obtained P_dB 中性.安全 and P_dB 负性.安全 Statistical tests of differences between p value Then, calculate the degree of decline in the test subject's danger perception: if and and This indicates that the measurement was valid, and the degree of decline in danger perception induced by the test subject's negative emotions was [value missing]. The larger the value, the greater the degree of decline; If the condition and and If the result is not valid, then the measurement is invalid.

2. The method for measuring the degree of decline in a driver's danger perception induced by negative emotions based on gamma electroencephalogram (GEG) signals as described in claim 1, characterized in that: In step (2), the test task is a danger signal judgment task. There are two conditions: a stimulation condition with danger signal and a stimulation condition without danger signal. The test extracts the EEG data of the test subject within 2000ms from the appearance of the stimulus. The test task under each condition is ensured to be performed 20 times or more.

3. The method for measuring the degree of decline in a driver's danger perception induced by negative emotions based on gamma electroencephalogram (GEG) signals as described in claim 1, characterized in that: In step (3), during the preprocessing of the raw EEG data, the raw EEG data is subjected to bandpass filtering of 0.5 to 45 Hz, and independent component analysis and data reconstruction are performed using the FastICA algorithm based on the principle of maximum negative entropy to effectively remove artifact interference; and the preprocessed EEG data retains the γ band as the target data for subsequent analysis.

4. The method for measuring the degree of decline in a driver's danger perception induced by negative emotions based on gamma electroencephalogram (GEG) signals as described in claim 1, characterized in that: In step (4), the method for calculating the baseline-normalized band power value is as follows: (4-1) For each type of task, the test time is 10 minutes. Wavelet transform is performed on the preprocessed EEG data to obtain the band power value of the γ band in the Fz electrode channel in the interval of 550-750ms after the stimulus signal appears. (4-2) For the four scenarios of perceiving danger with neutral emotions, perceiving danger with negative emotions, not perceiving danger with neutral emotions, and not perceiving danger with negative emotions, the band power is calculated separately. The baseline is set at -300ms to -100ms before the stimulus, and the baseline-normalized band power value is obtained. The calculation relationship is as follows: ， ， ， ， ， ， ， ， Where: symbol P This represents the γ-band power value under a single stimulus condition. k These are frequency values, ranging from 31 to 45 Hz; [symbol] This represents the γ-band power value (unit: dB) obtained after baseline normalization under single stimulus conditions, with the symbol... This represents the gamma band power value during the baseline period from -300ms to -100ms before stimulation.

5. The method for measuring the degree of decline in a driver's danger perception induced by negative emotions based on gamma electroencephalogram (GEG) signals as described in claim 1, characterized in that: In step (5), the mean mean The calculation formula is as follows: ， ， Where: symbol This represents the average value under multiple stimulus conditions.

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