Noise resistance and adaptability measurement method based on beta band temporal gradient measurement

Abstract

The application discloses a noise resistance and adaptability measuring method based on beta band time gradient measurement, characterized in that two types of abnormal data sensing test tasks, i.e., a music condition and a noise condition, are set, and the brain electrical signals of the tested personnel in the two types of tasks are collected; the baseline normalized band power average value of the beta band in the time window of 300-400 ms in the Fz channel of the tested personnel under different tasks is calculated; the minimum time gradient serial number between the beta power of the music.abnormal and the noise.abnormal, the music.normal and the noise.normal is calculated respectively; the smaller the minimum time gradient serial number, the stronger the noise resistance; if the minimum time gradient serial number is smaller, the stronger the noise adaptability; if the same tested personnel is represented as noise.normal, the tested personnel is of a normal sensitive type to noise; if the same tested personnel is represented as noise.hyper, the tested personnel is of a hyper sensitive type to noise; and the method has the advantages that the noise resistance and adaptability of the tested personnel are objectively and directly evaluated.

Description

Technical Field

[0001] This invention relates to the field of EEG neurological function measurement technology, and in particular to a method for measuring noise resistance and adaptability based on β-band time gradient measurement. Background Technology

[0002] Noise is unavoidable in many work and decision-making scenarios, such as driving airplanes, ships, trains, and cars. Noise directly interferes with the decision-making process through mechanisms such as distraction and increased mental workload. The interference effect of noise varies among individuals: there are significant differences in individuals' resistance to noise (the brain's resistance to noise under specific working conditions: manifested as being unaffected by noise during decision-making tasks but still affected by noise in non-decision-making tasks) and adaptability (the brain's desensitization effect to noise in all states: manifested as being unaffected by noise in both decision-making and non-decision-making tasks). These differences are of significant reference value for assessing and screening personnel with potential advantages in noisy environments. However, currently, there is still a lack of direct and accurate methods to measure the level of noise resistance and adaptability, which significantly affects the accurate assessment and effective screening of personnel for work and decision-making.

[0003] Current research shows that EEG activity characteristics can accurately reflect individual differences in noise processing and decision-making, especially the neural oscillations of the prefrontal and temporal lobes in the beta segments, which play a crucial role in cognitive decision-making and auditory processing. This invention proposes an evaluation method for measuring noise resistance and adaptability by comparing the differences in this indicator under specific decision-making tasks, based on the temporal beta segment temporal gradient measurement method. Summary of the Invention

[0004] The technical problem to be solved by the present invention is to provide a method for measuring noise resistance and adaptability based on beta band time gradient measurement. This method can effectively assess an individual's noise resistance and adaptability by analyzing the conditional differences in electroencephalogram (EEG) signals.

[0005] The technical solution adopted by this invention to solve the above-mentioned technical problems is: a method for measuring noise resistance and adaptability based on β-band time gradient measurement, comprising the following specific steps:

[0006] (1) The subject wears a multi-channel EEG measurement device to record EEG signals;

[0007] (2) Two types of tasks were set up: music sound condition abnormal data perception test and noise condition abnormal data perception test. Each type of task included two types of stimulus conditions: with abnormal data and without abnormal data. In the two types of tasks, the EEG signals of the test subjects were collected for 20 minutes 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, within ten time gradients, namely: minutes 1-2, minutes 3-4, ..., minutes 19-20, the baseline normalized average band power (dB) of the β band (14-30Hz) within a time window of 300-400ms after the stimulus interface is presented to the test subjects under different tasks and conditions.

[0010] (5) Compare the baseline normalized β power P_dB of the test subjects when they perceive abnormal data under musical stimulation, one by one, according to the time gradient. 乐音.异常 Baseline normalized β power P_dB when perceiving anomalous data under noise stimulation 噪音 . 异常 The difference between them, and the baseline normalized β power P_dB when no abnormal data was perceived under musical stimulation. 乐音.正常 Baseline normalized β power P_dB when no abnormal data was perceived under noise stimulation 噪音.正常 The difference between them is calculated to obtain P_dB. 乐音.异常 With P_dB 噪音.异常 The smallest time gradient number where there is no statistically significant difference between them Get P_dB 乐音.正常 With P_dB 噪音.正常 The smallest time gradient number where there is no statistically significant difference between them Then, comparisons were made between different participants: if The smaller the value, the stronger the noise resistance; if the condition is met... but The smaller the value, the stronger the noise tolerance; when comparing within the same test subject: if This indicates that the person being tested is of normal noise sensitivity type. If the following conditions are met... This indicates that the person being tested is hypersensitive to noise.

[0011] Furthermore, in step (2), the test task is to judge abnormal numbers. There are two conditions: the presence of abnormal numbers and the absence of abnormal numbers. The test extracts the EEG data of the test subject from the start of the task to 2000ms. The test task under each condition is ensured to be performed 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 β band of the preprocessed EEG data is retained as the target data for subsequent analysis.

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

[0014] (4-1) For each type of task, the total test time of 20 minutes is divided into 10 time gradients, each gradient lasting 2 minutes. Then, each time gradient is processed separately: wavelet transform is performed on the preprocessed EEG data to obtain the band power value of the β band in the Fz channel in the interval of 300-400ms after the stimulus (data page) appears.

[0015] (4-2) Within each time gradient, for the four cases of perceiving abnormalities in musical stimuli, not perceiving abnormalities in musical stimuli, perceiving abnormalities in noise stimuli, and not perceiving abnormalities in noise stimuli, the band power is calculated separately. The baseline is set from -300ms to -100ms before the stimulus, and the baseline-normalized band power is obtained. The calculation relationship is as follows:

[0016] P_dB 乐音.异常,t =10×log10(P) 乐音.异常,t / base 乐音.异常,t )

[0017] P_dB 乐音.正常,t =10×log10(P) 乐音.正常,t / base 乐音.正常,t )

[0018] P_dB 噪音.异常,t =10×log10(P) 噪音.异常,t / base 噪音.异常,t )

[0019] P_dB 噪音.正常,t =10×log10(P) 噪音.正常,t / base 噪音.正常,t )

[0020] Where: the symbol P represents the β-band power, and the symbol t represents the time gradient index, t = 1, 2, ..., 10. This indicates that the average value is calculated from multiple experimental tests for this type of task. k is the frequency value, which ranges from 14 to 30 Hz; the symbol P_dB represents the β band power obtained after baseline normalization; and the symbol base represents the band power during the baseline period from -300 ms to -100 ms before stimulation.

[0021] Furthermore, in step (5), the power index of the β-band is calculated based on the baseline-normalized value. The calculation formula is as follows:

[0022]

[0023] Where: p (乐音.异常vs噪音.异常) (t) represents P_dB on the time gradient t. 乐音.异常 With P_dB 噪音.异常 p-value for statistical tests comparing two entities; p (乐音.正常vs噪音.正常) (t) represents P_dB on the time gradient t. 乐音.正常 With P_dB 噪音 p-value for statistical tests comparing normal values.

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

[0025] (1) This method objectively and directly assesses the resistance and adaptability of the tested personnel to noise by analyzing the task-specific changes in the β band in the EEG signal data. It has important reference value for assessing and screening workers with potential advantages in noisy scenarios.

[0026] (2) This method uses the baseline-normalized β-band power value to extract neural activity features in the key time window of 300-400ms in the Fz channel. It is simple, sensitive and efficient for recognizing brain noise processing activity patterns. 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 noise immunity and adaptability based on β-band time gradient measurement 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 up a music sound condition abnormal data perception test task: Play a continuous single tone (middle C tone of electronic keyboard) at 70 decibels in the task. The test task is to judge abnormal numbers, that is, to judge whether there are abnormal numbers in the entire screen. Nine three-digit numbers are displayed on the screen in a three-row, three-column manner. Numbers exceeding 120 are abnormal numbers. The screen numbers are divided into two cases: those containing abnormal numbers and those not containing abnormal numbers. The screen display time is 1000-2000ms. Wait for the test subject to press the key to judge whether there are abnormal numbers. Extract the EEG data of the test subject from the start of the task to 2000ms under different mental load levels to obtain the raw EEG data of the test subject; Set up a noise condition abnormal data perception test task: Play a continuous noise at 70 decibels (high-speed friction noise in machine tool processing) in the task. Other settings are the same as the above tasks. In the two types of tasks, collect the test subject's EEG signal for 20 minutes. Ensure that the test task under each condition is repeated 20 times or more;

[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 maximum negative entropy principle to effectively remove EEG, EMG and other artifact interference. The preprocessed EEG data retains β (14-30Hz) as the target data for subsequent analysis, providing a clear and reliable basic signal for noise interference analysis.

[0033] (4) Based on the preprocessed EEG data, within ten time gradients, namely: minutes 1-2, minutes 3-4, ..., minutes 19-20, the baseline-normalized average band power value of the β band (14-30Hz) within a time window of 300-400ms after the stimulus interface was presented to the subjects under different tasks and conditions; specifically:

[0034] (4-1) For each type of task, the total test time of 20 minutes is divided into 10 time gradients, each gradient lasting 2 minutes. Then, each time gradient is processed separately: wavelet transform is performed on the preprocessed EEG data to obtain the band power value of the β band in the Fz channel in the interval of 300-400ms after the stimulus (data page) appears.

[0035] (4-2) Within each time gradient, for the four cases of perceiving abnormalities in musical stimuli, not perceiving abnormalities in musical stimuli, perceiving abnormalities in noise stimuli, and not perceiving abnormalities in noise stimuli, the band power is calculated separately. The baseline is set from -300ms to -100ms before the stimulus, and the baseline-normalized band power is obtained. The calculation relationship is as follows:

[0036] P_dB 乐音.异常,t =10×log10(P) 乐音.异常,t / base 乐音.异常,t )

[0037] P_dB 乐音.正常,t =10×log10(P) 乐音.正常,t / base 乐音.正常,t )

[0038] P_dB 噪音.异常,t =10×log10(P) 噪音.异常,t / base 噪音.异常,t )

[0039] P_dB 噪音.正常,t =10×log10(P) 噪音.正常,t / base 噪音.正常,t )

[0040] Where: the symbol P represents the β-band power, and the symbol t represents the time gradient index, t = 1, 2, ..., 10. This indicates that the average value is calculated from multiple experimental tests for this type of task. k is the frequency value, which ranges from 14 to 30 Hz; the symbol P_dB represents the β band power obtained after baseline normalization, and the symbol base represents the band power during the baseline period from -300 ms to -100 ms before stimulation.

[0041] (5) Compare the baseline normalized β power P_dB of the test subjects when they perceive abnormal data under musical stimulation, one by one, according to the time gradient. 乐音.异常 Baseline normalized β power P_dB when perceiving anomalous data under noise stimulation 噪音 . 异常 The difference between them, and the baseline normalized β power P_dB when no abnormal data was perceived under musical stimulation. 乐音.正常 Baseline normalized β power P_dB when no abnormal data was perceived under noise stimulation 噪音.正常 The difference between them is calculated to obtain P_dB. 乐音.异常 With P_dB 噪音.异常 The smallest time gradient number where there is no statistically significant difference between them Get P_dB 乐音.正常 With P_dB 噪音.正常 The smallest time gradient number where there is no statistically significant difference between them Then, comparisons were made between different participants: if The smaller the value, the stronger the noise resistance; if the condition is met... but The smaller the value, the stronger the noise tolerance; when comparing within the same test subject: if This indicates that the person being tested is of normal noise sensitivity type. If the following conditions are met... This indicates that the person being tested is hypersensitive to noise. and The calculation formula is as follows:

[0042]

[0043] Where: p (乐音.异常vs噪音.异常) (t) represents P_dB on the time gradient t. 乐音.异常 With P_dB 噪音.异常 p-value for statistical tests comparing two entities; p (乐音.正常vs噪音.正常) (t) represents P_dB on the time gradient t. 乐音.正常 With P_dB 噪音 p-value for statistical tests comparing normal values.

[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 of measuring the noise resistance and adaptability based on the time gradient measurement of the beta band, characterized in that 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 two types of tasks, i.e., a music condition abnormal data perception test task and a noise condition abnormal data perception test task, each type of task comprising two stimulation conditions, i.e., a condition containing abnormal data and a condition not containing abnormal data, and in the two types of tasks, 20-minute electroencephalogram signals of the measured person are collected to obtain original electroencephalogram data of the measured person; specifically, the test task is an abnormal number judgment, and the two conditions are two stimulation conditions, i.e., a condition containing abnormal numbers and a condition not containing abnormal numbers, and in the test, the electroencephalogram data of the measured person within 2000ms from the start of the task is extracted, and the test task under each condition is ensured to be 20 times or more; (3) preprocessing the original electroencephalogram data, which comprises signal amplification, segment interception, signal noise reduction, band pass filtering and artifact removal in sequence; the band pass filtering and artifact removal are specifically as follows: The original electroencephalogram data is subjected to 0.5-40Hz band pass filtering, 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 β wave band of the preprocessed electroencephalogram data is retained as target data for subsequent analysis; (4) according to the preprocessed electroencephalogram data, the baseline-normalized average band power value of the β wave band within a 300-400ms time window after the stimulation interface is presented is calculated for the measured person under different tasks and different conditions within ten time gradients, i.e., the first 1-2 minutes, the third 3-4 minutes, …, and the nineteenth 19-20 minutes; In step (4), the calculation method of the baseline-normalized band power index value is as follows: (5) comparing the difference between the baseline normalized β power P_dB when the abnormal data is perceived under the tone stimulation and the baseline normalized β power P_dB when the abnormal data is perceived under the noise stimulation, the difference between the baseline normalized β power P_dB when the abnormal data is not perceived under the tone stimulation and the baseline normalized β power P_dB when the abnormal data is not perceived under the noise stimulation, and calculating P_dB 乐音.异常 噪音.异常 乐音.正常 噪音.正常 乐音.异常 噪音.异常 乐音.正常 噪音.正常 ​​​​​​​​​​​​​ In the same testee, if , it means that the testee is of normal sensitivity to noise, if , it means that the testee is of hypersensitivity to noise.

2. The beta band time gradient measurement based noise resistance and adaptation capability measurement method according to claim 1, characterized in that: (4-1) for each type of task, the total test time of 20 minutes is divided into 10 time gradients, each gradient lasting 2 minutes, and then each time gradient is processed: wavelet transform is performed on the preprocessed electroencephalogram data to obtain the band power value of the β wave band in the Fz channel within a 300-400ms interval after the stimulation appears; (4-2) within each time gradient, for the four cases of music stimulation perception of abnormality, music stimulation non-perception of abnormality, noise stimulation perception of abnormality, and noise stimulation non-perception of abnormality, the band power is calculated, and the baseline-normalized band power is obtained by taking -300ms to -100ms before stimulation as the baseline, and the calculation relationship is as follows: ​ , , , , Wherein: the symbol P represents the beta band power, the symbol t represents the time gradient sequence number, t = 1, 2, …, 10, represents the average of multiple experimental tests on this type of task, that is: , k is the frequency value, which ranges from 14 to 30 Hz; the symbol represents the beta band power after baseline normalization processing, the symbol represents the wave band power of the baseline period of-300ms to-100ms before stimulation.

3. The beta band time gradient measurement based noise resistance and adaptation capability measurement method according to claim 2, characterized in that: In step (5), the power index of the β-band is calculated based on the baseline-normalized value. , The calculation formula is as follows: , , wherein: represents the statistical test p-value of the comparison between P_dB 乐音.异常 and P_dB 噪音.异常 at time gradient t. represents the statistical test p-value of the comparison between P_dB 乐音.正常 and P_dB 噪音.正常 at time gradient t.

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