Method for measuring a positive effect threshold of mental workload based on beta high frequency band time specificity

Abstract

The application discloses a measurement method of a brain load positive effect threshold based on a beta high-frequency band time specificity, and is characterized in that a plurality of test tasks of different brain load grades are set from low to high, and original electroencephalogram data of a person to be measured under different brain load grades are collected; the original electroencephalogram data are preprocessed, average band power index values of the person to be measured in a beta high-frequency band under different brain load grades are calculated according to the preprocessed electroencephalogram data; and the minimum time segment serial number of a statistical significant difference between double conditions of different brain load grades is calculated t i and a time segment serial number difference between adjacent brain load grades Adjt i , obtaining Adjt i a brain load grade corresponding to the minimum time i , i *is the brain load positive effect grade threshold of the person to be measured; and the method has the advantages that the beta high-frequency band time specificity change in electroencephalogram signal data is analyzed, and the brain load positive effect threshold of the person to be measured is objectively and accurately evaluated.

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 threshold of positive mental load effect based on the time specificity of the β high-frequency band. Background Technology

[0002] In complex cognitive tasks, an individual's mental workload tolerance and regulatory capacity are crucial to task processing efficiency. Current research shows that mental workload has a potential positive effect: at a specific mental workload level (threshold), increasing the intensity of mental workload appropriately can activate more neurons, thereby improving brain processing efficiency and bringing positive effects, manifested as shorter brain processing time and an earlier response time to the task. Different thresholds represent different load conditions under which the brain is positively activated, reflecting the varying levels of mental workload tolerance among individuals. Since there are significant differences in mental workload tolerance among individuals, accurately identifying the threshold of the positive effect of mental workload on an individual is crucial for measuring an individual's mental workload tolerance, rationally allocating cognitive tasks, and dynamically adjusting the workload intensity to achieve optimal cognitive efficiency. However, there is currently no method that can accurately and objectively determine the threshold of the positive effect of mental workload.

[0003] Existing research also shows that EEG activity characteristics under high cognitive load conditions can reflect an individual's dynamic adaptability in information processing and resource allocation, especially the neural oscillations in the β high-frequency band (26–30 Hz), which play an important role in cognitive load and task decision-making. This invention utilizes the measurement and analysis of β high-frequency band signals and proposes a method to measure the threshold of the positive effect of mental load on different individuals by observing the forward shift effect (i.e., time specificity) of this signal response time. 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 positive mental load effect based on the time specificity of the β high-frequency band. This method can accurately and efficiently assess an individual's mental load tolerance by analyzing the time specific characteristics of EEG signals (i.e., the time advance effect).

[0005] The technical solution adopted by this invention to solve the above-mentioned technical problem is: a method for measuring the threshold of the positive effect of mental workload based on the time specificity of the β high-frequency band, comprising the following specific steps:

[0006] (1) The subjects were fitted with multi-channel EEG measurement devices to record EEG signals;

[0007] (2) Set up multiple different mental load levels from low to high, and each mental load level includes a dual-condition test task. Collect the EEG signals of the test subjects at different mental load levels to obtain the raw EEG data of the test subjects.

[0008] (3) Preprocess the raw EEG data, including signal amplification, segmentation, signal noise reduction, bandpass filtering and artifact removal.

[0009] (4) Based on the preprocessed EEG data, calculate the average band power index value of the subject in the β high frequency band under different mental load levels, and extract the time-frequency energy distribution within the key time window of 250-500ms based on time segmentation.

[0010] (5) Compare the changes in β-high frequency band power of the test subjects under different mental workload levels, and calculate the power of different mental workload levels. i The smallest time segment number where there is a statistically significant difference between the two conditions. t i and adjacent mental workload levels i and i- Difference in time segment numbers between 1 Adjt i Then compare all Adjt i ,get Adjt i The minimum level of mental workload i *, i *This refers to the positive effect threshold of mental workload on the tested individuals. i* The smaller the value, the lower the threshold of the positive effect of mental load on the test subject, meaning the weaker the test subject's ability to withstand mental load.

[0011] Furthermore, in step (2), the test task is to judge abnormal numbers, and the two conditions are two stimulus conditions: the presence of abnormal numbers and the absence of abnormal numbers; and the mental load level is set to 10 levels according to the total number of numbers contained. The test extracts the EEG data of the test subject from the start of the task to 2000ms, and the test task of each mental load level is repeated 20 times or more.

[0012] 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 40 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 β high-frequency band as the target data for subsequent analysis.

[0013] Furthermore, in step (4), the method for calculating the average band power index value is as follows:

[0014] (4-1) Wavelet transform was performed on the preprocessed EEG data to obtain the band power values ​​of the β high-frequency band in the Fz and Pz channels in the interval of 250-500ms after the appearance of the digital stimulus page.

[0015] (4-2) For each level of mental workload test task, calculate the average band power index value in the β high-frequency band within the range of 250-500ms under conditions with and without abnormal numbers. Divide the 250-500ms time segment into segments, with each segment spanning 50ms. Shift the next time segment forward by 10ms, resulting in a total of 25 time segments. The formula for the average band power index value is as follows:

[0016] ,

[0017] ,

[0018] in: For test subjects under conditions without abnormal numbers, the level of mental workload is as follows: i The average band power index value under time segment s; i The mental workload level is represented by s, where s is the time segment number, and s = 1, 2, ..., 25. The average band power value of electrode n at frequency k under the condition of no abnormal numbers; n is the electrode number, which ranges from 10 to 47, representing Fz electrode and Pz electrode respectively; k is the frequency value, which ranges from 26 to 30 Hz. For test subjects under conditions containing abnormal numbers, the level of mental workload i The average band power index value under time segment s, This represents the average band power value of the nth electrode at frequency k under conditions containing anomalous numbers.

[0019] Furthermore, in step (5), the smallest time segment number where there is a statistically significant difference between the two conditions is... t i Adjacent mental workload levels i and i- Difference in time segment numbers between 1 Adjt i as well as Adjt i The minimum level of mental workload i The calculation formula for * is:

[0020] ,

[0021] ,

[0022] ,

[0023] in: Indicates the level of mental workload i conditions, and Significant differences exist. Time segment number s; t i Indicates the level of mental workload i The smallest time segment number where 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 smallest time segment number where there is a statistically significant difference between the two conditions; Indicates that The lowest value corresponds to the level of mental workload. i The level of mental workload at this time i This is the threshold of the positive effect of mental workload on the test subjects. i *

[0024] Compared with the prior art, the advantages of the present invention are:

[0025] (1) This method objectively and accurately assesses the threshold of positive mental load effect of the test subjects by analyzing the time-specific changes of 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 load tolerance of the test subjects, rationally arranging cognitive work tasks, and dynamically adjusting the load intensity to achieve the optimal cognitive efficiency.

[0026] (2) This method uses the average band power of the β high-frequency band to extract neural activity features within the key time window, and achieves efficient discrimination of mental load capacity through the calculation of positive threshold. It is simple, efficient and scientific. Attached Figure Description

[0027] Figure 1 This is a flowchart of the present invention. Detailed Implementation

[0028] The present invention will be further described in detail below with reference to the accompanying drawings and embodiments.

[0029] 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 β high-frequency band includes the following specific steps:

[0030] (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.

[0031] (2) Set 10 different mental load levels from low to high. The test task is to judge abnormal numbers, that is, to judge whether there are abnormal numbers in the whole screen. According to the number of numbers in the screen, it is divided into 10 different mental load levels, such as: 2 numbers, 4 numbers, 6 numbers, ..., 20 numbers. Each mental load level test task is divided into two cases: with abnormal numbers and without abnormal numbers. The screen presentation time is 1000-2000ms. The test extracts the EEG data of the test subjects from the start of the task to 2000ms under different mental load levels. Each mental load level test task is repeated 20 times or more to obtain the raw EEG data of the test subjects.

[0032] (3) Preprocessing of raw EEG data includes signal amplification, segmentation, signal denoising, bandpass filtering, and artifact removal. Among them, the raw EEG data is bandpass filtered at 0.5-40Hz, 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. The preprocessed EEG data retains the β high-frequency band (26-30Hz) as the target data for subsequent analysis, providing a clear and reliable basic signal for the assessment of the time advance effect.

[0033] (4) Based on the preprocessed EEG data, calculate the average band power index value of the subjects in the β high-frequency band under different levels of mental workload, and extract the time-frequency energy distribution within the key time window of 250-500ms based on time segmentation; specifically:

[0034] (4-1) Wavelet transform was performed on the preprocessed EEG data to obtain the band power values ​​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 EEG electrodes in the international 10-10 lead electrode standard system) in the interval of 250-500ms after the appearance of the digital stimulation page.

[0035] (4-2) For each level of mental workload test task, calculate the average band power index value in the β high-frequency band within the range of 250–500 ms under conditions with and without abnormal numbers. Divide the 250–500 ms time segment into 25 segments, with each segment spanning 50 ms. The next segment is shifted forward by 10 ms, resulting in 25 segments: 250–300 ms, 260–310 ms, 270–320 ms, …, 450–500 ms. The formula for the average band power index value is as follows:

[0036] ,

[0037] ,

[0038] in: For test subjects under conditions without abnormal numbers, the level of mental workload is as follows: i The average band power index value under time segment s; i The mental workload level is represented by s, where s is the time segment number, and s = 1, 2, ..., 25. The average band power value of electrode n at frequency k under the condition of no abnormal numbers; n is the electrode number, which ranges from 10 to 47, representing Fz electrode and Pz electrode respectively; k is the frequency value, which ranges from 26 to 30 Hz. For test subjects under conditions containing abnormal numbers, the level of mental workload i The average band power index value under time segment s, The average band power value of the nth electrode at frequency k under the condition of abnormal numbers;

[0039] (5) Compare the changes in β-high frequency band power of the test subjects under different mental workload levels, and calculate the power of different mental workload levels. i The smallest time segment number where there is a statistically significant difference between the two conditions. t i and adjacent mental workload levels i and i- Difference in time segment numbers between 1 Adjt i (This parameter reflects the time advance effect), then compare all Adjt i ,get Adjt i The minimum level of mental workload i *, i *This refers to the positive effect threshold of mental workload on the tested individuals. i* The smaller the value, the lower the threshold of the positive effect of mental load on the test subject, that is, the weaker the mental load tolerance of the test subject.t i , Adjt i as well as i The calculation formula for * is:

[0040] ,

[0041] ,

[0042] ,

[0043] in: Indicates the level of mental workload i conditions, and Significant differences exist. Time segment number s; t i Indicates the level of mental workload i The smallest time segment number where 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 smallest time segment number where there is a statistically significant difference between the two conditions; Indicates that The lowest value corresponds to the level of mental workload. i The level of mental workload at this time i This is the threshold of the positive effect of mental workload on the test subjects. i *

[0044] 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 a positive effect threshold of mental workload based on beta high frequency band time specificity, characterized by The method comprises the following specific steps: (1) wearing a multi-channel electroencephalogram measuring device on the measured person to record the electroencephalogram signal; (2) setting a plurality of different brain load levels from low to high, each brain load level containing double conditions, collecting the electroencephalogram signal of the measured person under different brain load levels, and obtaining the original electroencephalogram data of the measured person; (3) pre-processing the original electroencephalogram data, including signal amplification, segment interception, signal noise reduction, band pass filtering and artifact removal in sequence; (4) calculating the average band power index value of the measured person in the beta high frequency band under different brain load levels according to the pre-processed electroencephalogram data, and extracting the time-frequency energy distribution in the key time window of 250-500 ms based on time segmentation; (5) comparing the beta high frequency band power variation of the measured person under different brain load levels, calculating the minimum time segment number t of statistical significant difference of the average band power index value of the beta high frequency band between the double conditions under different brain load levels i i and the time segment number difference Adj t between adjacent brain load levels i and i-1 i , Adj t i refers to the difference between the minimum time segment number t of statistical significant difference of the average band power index value of the beta high frequency band between the double conditions under brain load level i i and the minimum time segment number t of statistical significant difference of the average band power index value of the beta high frequency band between the double conditions under the brain load level i-1 adjacent to the brain load level i i-1 ; Then compare all Adjt i , get Adjt i The minimum time corresponding to the mental load level i*, i* is the mental load positive effect threshold of the measured personnel, i* is smaller, indicating that the mental load positive effect threshold of the measured personnel is lower, that is, the mental load bearing capacity of the measured personnel is weaker.

2. The method of measuring the threshold of positive effect of brain power load based on the beta high-frequency band time specificity according to claim 1, characterized in that: In the step (2), the test task is abnormal number judgment, and the double conditions are two stimulation conditions containing abnormal numbers and no abnormal numbers; and the brain load level is set to 10 levels according to the number of overall containing numbers, the electroencephalogram data of the measured person within 2000 ms from the start of the task is extracted in the test, and the test task of each brain load level is 20 times or more.

3. The method of claim 1, wherein the threshold is determined based on a beta high frequency band time-specific mental workload positive effect. In the step (3), in the process of pre-processing the original electroencephalogram data, the original electroencephalogram data is subjected to band pass filtering of 0.5-40 Hz, and FastICA algorithm based on the maximum negative entropy principle is used for independent component analysis and data reorganization to effectively remove artifact interference; and the pre-processed electroencephalogram data is retained in the beta high frequency band as the target data for subsequent analysis.

4. The method of measuring the threshold of positive effect of brain power load based on the beta high-frequency band time specificity according to claim 2, characterized in that: In the step (4), the calculation method of the average band power index value is: (4-1) wavelet transform is performed on the pre-processed electroencephalogram data to obtain the band power value of the beta high frequency band in the Fz and Pz channels within the interval of 250-500 ms after the appearance of the number stimulation page; (4-2) for each brain load level test task, the average band power index value of the beta high frequency band within 250-500 ms under the conditions of containing abnormal numbers and no abnormal numbers is calculated respectively, 250-500 ms is time segmented, the span of each time segment is 50 ms, the next time segment moves back 10 ms, and a total of 25 time segments are divided, and the relationship of the average band power index value is: ， Wherein: is the average band power index value of the measured person under the condition of no abnormal numbers under the mental load level i and the time segment s; i is the mental load level, s is the time segment number, s = 1, 2, …, 25; is the average band power value of the k frequency of the n electrode under the condition of no abnormal numbers; n is the electrode label, which takes the value range of 10 and 47, representing the Fz electrode and the Pz electrode respectively, and k is the frequency value, which takes the value range of 26-30 Hz; is the average band power index value of the measured person under the condition of abnormal numbers under the mental load level i and the time segment s, is the average band power value of the k frequency of the n electrode under the condition of abnormal numbers.

5. The method of measuring the threshold of positive effect of brain power load based on the beta high-frequency band time specificity according to claim 4, characterized in that: The minimum time segment sequence number t between the two conditions with statistically significant difference in step (5) i The time segment sequence number difference Adjt between adjacent mental load levels i and i-1 i And Adjt i The calculation relationship of the mental load level i* corresponding to the minimum time is: wherein: denotes the minimum time segment number t at which there is a statistically significant difference between the two conditions at mental workload level i and there is a statistically significant difference the time segment number s; t i denotes the minimum time segment number t at which there is a statistically significant difference between the two conditions at mental workload level i i-1 denotes the minimum time segment number t at which there is a statistically significant difference between the two conditions at mental workload level i-1 adjacent to mental workload level i denotes the corresponding mental workload level i that minimizes the value of the mental workload level i, which is the positive effect threshold i* of the mental workload of the person being tested.

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