Beta high-frequency wave band time specificity-based mental load forward effect threshold value measurement method

Abstract

The invention discloses a method for measuring a brain load forward effect threshold based on beta high-frequency band time specificity, which is characterized by comprising the following steps of: setting a plurality of test tasks with different brain load levels from low to high, and collecting original electroencephalogram data of a tested person under the different brain load levels; preprocessing the original electroencephalogram data, and calculating an average waveband power index value of the tested person in a beta high-frequency waveband under different mental load levels according to the preprocessed electroencephalogram data; the minimum time segment sequence number ti with the statistical significance difference between the two conditions of different mental load levels and the time segment sequence number difference Adjti between the adjacent mental levels are calculated, the mental load level i * corresponding to the minimum Adjti is obtained, and i * is the mental load forward effect level threshold value of the tested person; the method has the advantage of objectively and accurately evaluating the mental load positive effect threshold value of the tested person by analyzing the beta high-frequency band time specificity change in the electroencephalogram signal data.

Description

Technical Field

[0001] The present invention relates to the technical field of electroencephalogram (EEG) nerve function measurement, and in particular to a method for measuring the threshold value of the positive effect of mental workload based on the time specificity of the β high-frequency band. Background Art

[0002] In complex cognitive tasks, an individual's ability to tolerate and regulate mental workload is crucial for task efficiency. Current research suggests that mental workload has a potential positive effect: at a specific mental workload level (threshold), increasing workload to an appropriate intensity can activate more neurons, thereby improving brain processing efficiency and bringing positive effects, manifested as faster processing times and faster response times to tasks. Different thresholds represent different load conditions for positive brain activation, reflecting the individual's ability to tolerate mental workload. Individuals vary significantly in their ability to tolerate mental workload. Accurately identifying an individual's threshold for the positive effect of mental workload is crucial for assessing workload tolerance, rationally scheduling cognitive tasks, and dynamically adjusting workload intensity to optimize cognitive efficiency. However, currently, there is no method that can accurately and objectively determine the threshold for the positive effect of mental workload.

[0003] Existing research also shows that EEG activity characteristics under high cognitive load can reflect an individual's dynamic adaptability in information processing and resource allocation. In particular, neural oscillations in the high-frequency beta band (26-30 Hz) play a significant role in cognitive load and task decision-making. This paper utilizes the measurement and analysis of high-frequency beta band signals and observes the forward shift effect of these signal response times (i.e., temporal specificity) to propose a method for measuring the threshold for the positive effect of mental workload in different individuals. Summary of the Invention

[0004] The technical problem to be solved by the present invention is to provide a method for measuring the threshold of the positive effect of mental workload based on the time specificity of the β high-frequency band. This method can accurately and efficiently evaluate an individual's mental workload tolerance by analyzing the time specific characteristics of EEG signals (i.e., the time advance effect).

[0005] The technical solution adopted by the present invention to solve the above technical problems is: a method for measuring the threshold value of the positive effect of mental workload based on the time specificity of the β high-frequency band, comprising the following specific steps: (1) The subjects are given a multi-channel EEG measurement device to record EEG signals; (2) Set multiple different mental load levels from low to high, and each mental load level contains a dual-condition test task, collect the EEG signals of the subjects at different mental load levels, and obtain the original EEG data of the subjects; (3) Preprocessing of the raw EEG data, including signal amplification, segmentation, signal noise reduction, bandpass filtering and artifact removal; (4) Based on the pre-processed EEG data, the average band power index value of the subjects in the β high-frequency band at different mental workload levels is calculated, and the time-frequency energy distribution within the key time window of 250-500ms is extracted based on time segmentation; (5) Compare the changes in the β high-frequency band power of the subjects under different mental load levels and calculate the different mental load levels i The minimum time segment number for which there is a statistically significant difference between the two conditions under t i and adjacent mental workload levels i and i- 1 The time segment sequence number difference between Adjt i , then compare all Adjt i ,get Adjt i The minimum mental workload level i *, i *This is the threshold value of the positive effect of mental workload of the person being tested. i* The smaller it is, the lower the threshold of the positive effect of mental workload of the person being tested is, that is, the weaker the person being tested's ability to withstand mental workload is.

[0006] Furthermore, in step (2), the test task is abnormal number judgment, and the dual conditions are two stimulation conditions: one containing abnormal numbers and the other not containing abnormal numbers; and the mental load level is set to 10 levels according to the number of numbers included in the total. In the test, the EEG data of the test person from the start of the task to 2000ms is extracted, and the test task for each mental load level is performed 20 times or more.

[0007] Furthermore, in the step (3), during the preprocessing of the raw EEG data, the raw EEG data is subjected to a 0.5-40 Hz band-pass filter, and the FastICA algorithm based on the maximum negative entropy principle is used to perform independent component analysis and data reorganization to effectively remove artifact interference; and the preprocessed EEG data retains the β high-frequency band as the target data for subsequent analysis.

[0008] Furthermore, in step (4), the calculation method of the average band power index value is: (4-1) Perform wavelet transform on the pre-processed EEG data to obtain the power values ​​of the β high-frequency band in the Fz and Pz channels in the interval 250 to 500 ms after the digital stimulus page appears; (4-2) For each mental workload level test task, calculate the average band power index value in the β high frequency band within the range of 250-500ms under the conditions with and without abnormal numbers. Divide the 250-500ms into time segments, with each time segment spanning 50ms. The next time segment is shifted back 10ms, and is divided into 25 time segments in total. The relationship formula for the average band power index value is: , , in: The mental workload level of the tested persons under the condition of no abnormal numbers i And the average band power index value under time segment s; i is the mental workload level, s is the time segment number, s=1, 2, ..., 25; is the average band power value at frequency k of electrode n under the condition of no abnormal digital conditions; n is the electrode number, and its value range is 10 and 47, representing Fz electrode and Pz electrode respectively; k is the frequency value, and its value range is 26~30Hz; The mental workload level of the subjects under abnormal digital conditions i And the average band power index value under time segment s, is the average band power value at frequency k of electrode n under abnormal digital conditions.

[0009] Furthermore, in step (5), the minimum time segment number at which there is a statistically significant difference between the two conditions is t i , adjacent mental workload levels i and i- 1 The time segment sequence number difference between Adjt i as well as Adjt i The minimum mental workload level i *The calculation formula is: , , , in: Indicates the level of mental workload i conditions, and There is a significant difference The time segment number s; t i Indicates the level of mental workload iThe minimum time segment number at which there is a statistically significant difference between the two conditions. t i-1 Indicates the level of mental workload i Adjacent mental workload levels i-1 The minimum time segment number at which there is a statistically significant difference between the two conditions; Indicates that The smallest value corresponds to the mental workload level i , the mental workload level at this time i That is the threshold value of the positive effect of mental workload of the tested person i *.

[0010] Compared with the prior art, the advantages of the present invention are: (1) This method objectively and accurately evaluates the threshold of the positive effect of mental workload of the subjects by analyzing the time-specific changes in the β high-frequency band in the EEG signal data (i.e., the time advance effect). It plays an important role in measuring the mental workload tolerance of the subjects, rationally arranging cognitive work tasks, and dynamically regulating the workload intensity to achieve the optimization of cognitive efficiency. (2) This method uses the average band power of the β high-frequency band to extract the neural activity characteristics within the key time window, and realizes the efficient judgment of mental load tolerance through the calculation of the positive threshold. It is simple, efficient and scientific. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] Figure 1 Flowchart of the present invention. DETAILED DESCRIPTION

[0012] The present invention will be described in further detail below with reference to the accompanying drawings and embodiments.

[0013] As shown in the figure, the method for measuring the threshold of the positive effect of mental workload based on the time specificity of the high-frequency beta band includes the following specific steps: (1) The person being tested is equipped with 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) Set 10 different brain load levels from low to high, and the test task is to judge whether there is an abnormal number in the entire screen, and according to the number of numbers in the screen, it is divided into 10 different brain load levels, such as 2 numbers, 4 numbers, 6 numbers, …, 20 numbers, and each brain load level test task is divided into two cases of containing abnormal numbers and no abnormal numbers, the screen presentation time is 1000-2000ms, and the EEG data of the testee from the start of the task to 2000ms under different brain load levels is extracted during the test, and each brain load level test task is 20 times or more to obtain the original EEG data of the testee; (3) The original EEG data is preprocessed, including signal amplification, segment interception, signal noise reduction, band pass filtering and artifact removal; wherein: the original EEG data is band pass filtered at 0.5-40Hz, and the FastICA algorithm based on the maximum negative entropy principle is used for independent component analysis and data reorganization to effectively remove electrooculogram, electromyogram and other artifact interference; and the β high frequency band (26-30Hz) of the preprocessed EEG data is retained as the target data for subsequent analysis, providing clear and reliable basic signals for the evaluation of time advance effect; (4) According to the preprocessed EEG data, the average band power index value of the testee in the β high frequency band under different brain load levels is calculated, and the time-frequency energy distribution in the key time window 250-500ms is extracted based on time segmentation; Specifically: (4-1) Wavelet transform is performed on the preprocessed EEG data to obtain the band power value of the β high frequency band in the Fz and Pz channels (Fz and Pz channels are the Fz and Pz electrode channels of the international 10-10 lead electrode standard system) in the 250-500ms interval after the appearance of the number stimulus page; (4-2) For each brain load level test task, the average band power index value in the β high frequency band in the range of 250-500ms under the conditions of containing abnormal numbers and no abnormal numbers is calculated, and the 250-500ms is time segmented, each time segment has a span of 50ms, the next time segment moves back 10ms, a total of 25 time segments, specifically: 250-300ms, 260-310ms, 270-320ms, …, 450-500ms, the relationship of the average band power index value is: , , Wherein: is the average band power index value of the testee under the brain load level i and time segment s without abnormal numbers; iis the mental workload level, s is the time segment number, s=1, 2, ..., 25; is the average band power value at frequency k of electrode n under the condition of no abnormal digital conditions; n is the electrode number, and its value range is 10 and 47, representing Fz electrode and Pz electrode respectively; k is the frequency value, and its value range is 26~30Hz; The mental workload level of the subjects under abnormal digital conditions i And the average band power index value under time segment s, is the average band power value at frequency k of electrode n under abnormal digital conditions; (5) Compare the changes in the β high-frequency band power of the subjects under different mental load levels and calculate the different mental load levels i The minimum time segment number for which there is a statistically significant difference between the two conditions under t i and adjacent mental workload levels i and i- 1 The time segment sequence number difference between Adjt i (This parameter reflects the time advance effect), and then compare all Adjt i ,get Adjt i The minimum mental workload level i *, i *This is the threshold value of the positive effect of mental workload of the person being tested. i* The smaller it is, the lower the threshold of the positive effect of mental workload of the person being tested is, that is, the weaker the person's ability to withstand mental workload is. t i 、 Adjt i as well as i *The calculation formula is: , , , in: Indicates the level of mental workload i conditions, and There is a significant difference The time segment number s; t i Indicates the level of mental workload i The minimum time segment number for which there is a statistically significant difference between the two conditions under t i-1 Indicates the level of mental workload iAdjacent mental workload levels i-1 The minimum time segment number at which there is a statistically significant difference between the two conditions under the same condition; Indicates that The smallest value corresponds to the mental workload level i , the mental workload level at this time i That is the threshold value of the positive effect of mental workload of the tested person i *.

[0014] The protection scope of the present invention includes but is not limited to the above embodiments, and its protection scope is subject to the claims. Any replacement, deformation, and improvement of this technology that can be easily thought of by those skilled in the art fall within the protection scope of the present invention.

Claims

1. A method for measuring the threshold of positive mental workload effect based on the temporal specificity of the β high-frequency band, characterized by The specific steps include: (1) The subjects are given a multi-channel EEG measurement device to record EEG signals; (2) Set multiple different mental load levels from low to high, and each mental load level contains a dual-condition test task, collect the EEG signals of the subjects at different mental load levels, and obtain the original EEG data of the subjects; (3) Preprocessing of the raw EEG data, including signal amplification, segmentation, signal noise reduction, bandpass filtering and artifact removal; (4) Based on the pre-processed EEG data, the average band power index value of the subjects in the β high-frequency band at different mental workload levels is calculated, and the time-frequency energy distribution within the key time window of 250-500ms is extracted based on time segmentation; (5) Compare the changes in the β high-frequency band power of the subjects under different mental load levels and calculate the different mental load levels i The minimum time segment number for which there is a statistically significant difference between the two conditions under t i and adjacent mental workload levels i and i- 1 The time segment sequence number difference between Adjt i , then compare all Adjt i ,get Adjt i The minimum mental workload level i *, i *This is the threshold value of the positive effect of mental workload of the person being tested. i* The smaller it is, the lower the threshold of the positive effect of mental workload of the person being tested is, that is, the weaker the person being tested's ability to withstand mental workload is.

2. The method for measuring the threshold value of the positive effect of mental workload based on the time specificity of the β high-frequency band as claimed in claim 1, characterized in that: In the step (2), the test task is abnormal number judgment, and the dual conditions are two stimulation conditions: one containing abnormal numbers and the other not containing abnormal numbers; and the mental load level is set to 10 levels according to the number of numbers included in the total. In the test, the EEG data of the test person from the start of the task to 2000ms is extracted, and the test task for each mental load level is tested 20 times or more.

3. The method for measuring the threshold value of the positive effect of mental workload based on the time specificity of the β high-frequency band as claimed in claim 1, characterized in that: In the step (3), during the preprocessing of the raw EEG data, the raw EEG data is subjected to a 0.5-40 Hz band-pass filter, and the FastICA algorithm based on the maximum negative entropy principle is used to perform independent component analysis and data reorganization to effectively remove artifact interference; and the preprocessed EEG data retains the β high-frequency band as the target data for subsequent analysis.

4. The method for measuring the threshold value of the positive effect of mental workload based on the time specificity of the β high-frequency band as claimed in claim 2, characterized in that: In the step (4), the calculation method of the average band power index value is: (4-1) Perform wavelet transform on the pre-processed EEG data to obtain the power values ​​of the β high-frequency band in the Fz and Pz channels in the interval 250 to 500 ms after the digital stimulus page appears; (4-2) For each mental workload level test task, calculate the average band power index value in the β high frequency band within the range of 250-500ms under the conditions with and without abnormal numbers. Divide the 250-500ms into time segments, with each time segment spanning 50ms. The next time segment is shifted back 10ms, and is divided into 25 time segments in total. The relationship formula for the average band power index value is: ， ， in: The mental workload level of the tested persons under the condition of no abnormal numbers i And the average band power index value under time segment s; i is the mental workload level, s is the time segment number, s=1, 2, ..., 25; is the average band power value at frequency k of electrode n under the condition of no abnormal digital conditions; n is the electrode number, and its value range is 10 and 47, representing Fz electrode and Pz electrode respectively; k is the frequency value, and its value range is 26~30Hz; The mental workload level of the subjects under abnormal digital conditions i And the average band power index value under time segment s, is the average band power value at frequency k of electrode n under abnormal digital conditions.

5. The method for measuring the threshold value of the positive effect of mental workload based on the time specificity of the β high-frequency band as claimed in claim 4, characterized in that: In step (5), the minimum time segment number for which there is a statistically significant difference between the two conditions is t i , adjacent mental workload levels i and i- 1 The time segment sequence number difference between Adjt i as well as Adjt i The minimum mental workload level i *The calculation formula is: ， ， ， in: Indicates the level of mental workload i conditions, and There is a significant difference The time segment number s; t i Indicates the level of mental workload i The minimum time segment number at which there is a statistically significant difference between the two conditions. t i-1 Indicates the level of mental workload i Adjacent mental workload levels i-1 The minimum time segment number at which there is a statistically significant difference between the two conditions; Indicates that The smallest value corresponds to the mental workload level i , the mental workload level at this time i That is the threshold value of the positive effect of mental workload of the tested person i *.

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