Noise resistance and adaptability measuring method based on beta-band time gradient measurement

Abstract

The invention discloses a noise resistance and adaptive capacity measuring method based on beta-band time gradient measurement, which is characterized by comprising the following steps of: setting two types of abnormal data perception test tasks of musical sound conditions and noise conditions, and collecting electroencephalogram signals of a tested person in the two types of tasks; calculating a normalized band power average value of a base line of a beta band within a time window of 300-400ms in an Fz channel under different tasks of the tested personnel; the smaller the minimum time gradient sequence number of beta power having no statistical difference between the abnormal, the musical sound, the normal and the noise is, the higher the noise resistance capability is, and the smaller the minimum time gradient sequence number of beta power having no statistical difference between the abnormal, the musical sound, the normal and the noise is; if the noise is smaller, the noise adaptability is higher; if the same person to be tested is detected, the person to be tested is in a noise normal sensitive type; if yes, the detected person is in a noise hypersensitive type; the method has the advantage of objectively and directly evaluating the noise resistance and adaptability of the tested person.

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 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 for measuring noise immunity and adaptability based on β-band time gradient measurement, characterized in that... The specific steps include the following: (1) The subject wears a multi-channel EEG measurement device to record EEG signals; (2) Set up two types of tasks: music sound condition abnormal data perception test task and noise condition abnormal data perception test task. Each type of task includes 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. (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, within ten time gradients, namely: minutes 1-2, minutes 3-4, ..., minutes 19-20, calculate the baseline normalized average band power value of the β band within a time window of 300-400ms after the stimulation interface is presented to the test subjects under different tasks and conditions. (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.

2. The method for measuring noise resistance and adaptability based on β-band time gradient measurement as described in claim 1, characterized in that: In step (2), the test task is to judge abnormal numbers. There are two conditions: abnormal numbers and no 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.

3. The method for measuring noise resistance and adaptability based on β-band time gradient measurement 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 40 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 β band of the preprocessed EEG data is retained as the target data for subsequent analysis.

4. The method for measuring noise resistance and adaptability based on β-band time gradient measurement as described in claim 2, characterized in that: In step (4), the method for calculating the baseline normalized band power index is as follows: (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 appears. (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: 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.

5. The method for measuring noise immunity and adaptability based on β-band time gradient measurement as described in claim 4, 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: 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.

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