Concentration-fatigue coordinated regulation and control method and system based on electroencephalogram bimodal feedback

Abstract

The invention discloses a concentration-fatigue cooperative regulation and control method and system based on electroencephalogram bimodal feedback, and the method comprises the steps: collecting first electroencephalogram data of a student in a resting state before training, and calculating a mean value of the ratio of a theta wave to an alpha wave as a fatigue resting state reference value; after training is started, second electroencephalogram data of the trainee are collected, a concentration index is calculated according to the ratio of the beta wave to the sum of the beta wave, the theta wave and the alpha wave, and a fatigue index is calculated according to the ratio of the theta wave to the alpha wave; calculating a dynamic threshold value according to the fatigue resting state reference value; and generating a corresponding risk prompt according to whether the concentration index and the fatigue index deviate from their thresholds. According to the method, the limitation of traditional single-dimensional biofeedback is broken through, the safety and efficiency of complex task training are remarkably improved, the limitation of the traditional single-dimensional biofeedback is broken through, and a quantitative neurocognitive enhancement scheme is provided for special operation training.

Description

Technical Field

[0001] The present invention relates to the field of safety training technology, and in particular to a concentration-fatigue coordinated regulation method and system based on electroencephalogram (EEG) dual-modal feedback. Background Art

[0002] In safety training for high-risk industries (such as electricity, chemical industry, aviation, etc.), trainees' attention and fatigue status directly affect the training effect and operational safety. Traditional training methods mainly rely on empirical prompts and post-event feedback, which makes it difficult to monitor trainees' concentration and fatigue level in real time, resulting in poor training results and trainees are prone to make mistakes in actual operations. In addition, in the existing technology, there is a high misjudgment rate in the method of monitoring fatigue or attention alone. For example, the rise of theta waves during fatigue may be mistaken for a decrease in concentration, resulting in inaccurate feedback; at the same time, individual differences between trainees also lead to a high misjudgment rate. Summary of the invention

[0003] Purpose of the invention: The purpose of the present invention is to provide a method and system for coordinated regulation of concentration and fatigue based on EEG dual-modal feedback, so as to solve the problems of single monitoring dimension and high misjudgment rate in the existing system.

[0004] Technical solution: The concentration-fatigue coordinated regulation method based on EEG dual-modal feedback of the present invention comprises the following steps: Before the training begins, the first EEG data of the trainees in the resting state is collected, and the average ratio of theta wave to alpha wave is calculated as the resting state baseline value of fatigue ; After the training started, the participants’ second EEG data were collected, and the concentration index was calculated based on the ratio of beta waves to the sum of beta waves, theta waves, and alpha waves, and the fatigue index was calculated based on the ratio of theta waves to alpha waves; According to the fatigue resting state reference value Calculate dynamic fatigue threshold; Set a static concentration threshold; Corresponding risk warnings are generated based on whether the concentration index deviates from the static concentration threshold and whether the fatigue index deviates from the dynamic fatigue threshold.

[0005] Furthermore, the fatigue resting state reference value Calculation of dynamic fatigue threshold includes: Dynamic fatigue threshold ; in, is the trend change value of the ratio of theta wave to alpha wave in the sliding window, , N is the sliding window length.

[0006] Furthermore, when the training start time is less than one sliding window time, The value is 0.

[0007] Furthermore, generating corresponding risk warnings according to whether the concentration index and the fatigue index exceed their corresponding thresholds includes: When the concentration index is less than its threshold and the fatigue index is greater than its threshold, the highest intervention is triggered; When the concentration index is not less than its threshold, or the fatigue index is greater than its threshold, intermediate intervention is triggered; When the concentration index is not less than its threshold and the fatigue index is not greater than its threshold, low-intensity feedback is triggered.

[0008] Furthermore, when the fatigue index deviates from its corresponding threshold by ±5% in three consecutive cycles, an automatic recalibration process is triggered; the triggering of the automatic recalibration process includes: Resample the first EEG data in a resting state, and calculate and update the resting state reference value of fatigue , based on the updated resting fatigue baseline Recalculate dynamic fatigue threshold.

[0009] The concentration-fatigue coordinated regulation system based on EEG dual-modal feedback of the present invention comprises: The resting state baseline calculation unit is used to collect the trainee's first resting state EEG data before the training begins, and calculate the average of the ratio of theta waves to alpha waves as the resting state baseline value of fatigue ; The concentration-fatigue coordinated regulation unit is used to collect the trainees' second EEG data after the training begins, calculate the concentration index according to the ratio of beta waves to the sum of beta waves, theta waves and alpha waves, and calculate the fatigue index according to the ratio of theta waves to alpha waves; According to the fatigue resting state reference value Calculate dynamic fatigue threshold; Set a static concentration threshold; Corresponding risk warnings are generated based on whether the concentration index deviates from the static concentration threshold and whether the fatigue index deviates from the dynamic fatigue threshold.

[0010] Furthermore, in the concentration-fatigue coordinated regulation unit, the fatigue resting state reference value Calculation of dynamic fatigue threshold includes: Dynamic fatigue threshold ; in, is the trend change value of the ratio of theta wave to alpha wave in the sliding window, , N is the sliding window length.

[0011] Furthermore, in the concentration-fatigue coordinated control unit, generating corresponding risk warnings according to whether the concentration index and the fatigue index exceed their corresponding thresholds includes: When the concentration index is less than its threshold and the fatigue index is greater than its threshold, the highest intervention is triggered; When the concentration index is not less than its threshold, or the fatigue index is greater than its threshold, intermediate intervention is triggered; When the concentration index is not less than its threshold and the fatigue index is not greater than its threshold, low-intensity feedback is triggered; In the concentration-fatigue coordinated control unit, when the fatigue index deviates from its corresponding threshold by ±5% in three consecutive cycles, an automatic recalibration process is triggered; the triggering of the automatic recalibration process includes: Resample the first EEG data in a resting state, and calculate and update the resting state reference value of fatigue , based on the updated resting fatigue baseline Recalculate dynamic fatigue threshold.

[0012] The electronic device described in the present invention includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the computer program is loaded into the processor, the concentration-fatigue coordinated regulation method based on EEG dual-modal feedback is implemented.

[0013] The computer-readable storage medium described in the present invention stores a computer program, and when the computer program is executed by a processor, the concentration-fatigue coordinated regulation method based on EEG dual-modal feedback is implemented.

[0014] Beneficial effects: Compared with the prior art, the advantages of the present invention are: the present invention integrates the dual-modal physiological indicators of prefrontal beta waves and theta / alpha ratio to achieve real-time monitoring and dynamic intervention of the operator's concentration and fatigue status in high-risk operation training; combined with the preset safety threshold, a three-level intervention strategy (voice warning, sound and light stimulation, and simplified operation) is generated, which significantly improves the safety and efficiency of complex task training. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 This is a flow chart of the concentration-fatigue coordinated regulation method based on EEG dual-modal feedback of the present invention.

[0016] Figure 2 This is a flow chart of the automatic recalibration of the present invention. DETAILED DESCRIPTION

[0017] The technical solution of the present invention is further described below in conjunction with the accompanying drawings.

[0018] like Figure 1As shown, the concentration-fatigue coordinated regulation method based on EEG dual-modal feedback includes the following steps.

[0019] (1) EEG data acquisition and processing.

[0020] Acquisition equipment: This embodiment uses a single-channel dry electrode EEG headband and adopts the open BCI (OpenBCI CytonBoard) single-channel dry electrode system. The electrode positioning refers to the Fp1 position in the international 10-20 system standard, and the sampling rate is ≥200Hz to meet the high-frequency band requirements of the β wave.

[0021] Signal preprocessing: Enable the device's built-in bandpass filter (0.5-40Hz) to eliminate power frequency interference and myoelectric noise. Further clean up the signal through software filtering.

[0022] Power spectral density calculation: Fast Fourier transform (FFT) is used to calculate the power (θ / α / β) of each frequency band every 5 seconds, in units of μV² / Hz.

[0023] Theta wave: The frequency range is [4Hz, 8Hz]. It is commonly found in people's trance or hypnotic state.

[0024] α wave: The frequency range is [8Hz, 13Hz). It is the basic rhythm of normal human brain waves. If there is no external stimulation, its frequency is quite constant. This rhythm is most obvious when a person is awake, quiet and has his eyes closed.

[0025] Beta wave: The frequency range is [13Hz, 30Hz]. It is the most common high-frequency wave when people are awake. It is the dominant brain wave of alert people.

[0026] (2) Calculation of bimodal indicators.

[0027] Concentration index (beta wave ratio): ,in, , and Represents the power of theta wave, alpha wave and beta wave respectively.

[0028] Fatigue index (θ / α ratio): .

[0029] (3) Collaborative judgment logic (3.1) Threshold definition Static Threshold of Concentration : Set to 20% based on experimental data and not adjusted based on individual differences. It is judged as insufficient concentration.

[0030] Dynamic fatigue threshold : Based on resting limit value The dynamic fatigue threshold is the critical value for judging whether the fatigue level has entered the danger zone. The present invention is based on the dual mechanism of individualized baseline value correction and real-time adaptive adjustment, and its calculation process includes the following key elements: a) Physiological signal basis: θ / α ratio, reflecting the degree of fatigue accumulation; b) Individual adaptability: Eliminate individual differences by correcting the resting fatigue baseline value, and establish a personalized baseline using the trainees' EEG activity in a non-task state; c) Nonlinear amplification factor: An empirical coefficient of 1.2 is used to compensate for the nonlinear change trend of physiological signals.

[0031] (3.2) The calculation method of the dynamic fatigue threshold includes the following steps: Before training, let the participants sit quietly with their eyes closed for 2 minutes, and record the mean value of the θ / α ratio at this time. ; Dynamic fatigue threshold calculation formula: ; in: The resting baseline value (unit: standardized score) is the average physiological index in the non-task state; 1.2 is the amplification factor, that is, the nonlinear compensation factor determined by experience; It is a lower limit constraint to prevent threshold collapse caused by overcorrection. is the trend change rate based on the sliding window (120 seconds in this embodiment), which is used to dynamically amplify or suppress the threshold to avoid the lack of individual adaptability caused by the static coefficient; assuming that the θ / α ratio sequence in the window is , the corresponding time point is , by linear regression fitting the data in the window, we get the trend slope k, and convert the slope k into the proportional change rate relative to the current mean. .

[0032] (3.3) Double threshold fusion strategy.

[0033] Level III risk (high fatigue and low concentration): and triggering the highest intervention (e.g., forced pause + neural recovery stimulation); Level II risk (single indicator abnormality, normal concentration, high fatigue): or Triggering intermediate intervention (such as sound and light warning); Level Ⅰ risk (low risk): and Trigger low-intensity feedback (such as operation accuracy prompts).

[0034] (4) Dynamic calibration and status determination.

[0035] When the threshold deviation exceeds ±5% for 3 consecutive cycles, the automatic recalibration process is triggered. Figure 2 As shown in the figure, when the fatigue index is monitored to deviate from the dynamic fatigue threshold by ±5% for three consecutive cycles, a 30-second resting state resampling is started and the dynamic fatigue threshold is recalculated.

[0036] The method of the present invention is verified by specific experiments below.

[0037] The power company provides safety training for high-voltage equipment operators, monitors trainees’ concentration and fatigue in real time, and ensures that thresholds adapt to individual differences and changes in physiological status through a dynamic calibration mechanism.

[0038] Step 1: Collect resting baseline values.

[0039] The student closes his eyes and sits quietly for 2 minutes. The system records the average of his θ / α ratio as the resting fatigue baseline value. .

[0040] Student A: (mean of θ / α).

[0041] Step 2: Dynamic threshold calculation and initial calibration.

[0042] When a student first starts training, the system needs to set the initial threshold based on the resting baseline value. At this time, there is not enough real-time data to calculate the trend change rate, so Temporarily set to 0; Initial calibration for student A: .

[0043] Step 3: Real-time data monitoring and dynamic adjustment.

[0044] Participant A performed high-voltage cable operation training and updated his EEG data every 5 seconds. The monitoring and calculation results are shown in Table 1.

[0045] Table 1

[0046] Adjustment logic: When When it continues to rise, Amplify the trend (e.g., from 1.2 to 1.58) to avoid underestimation of the threshold due to fatigue accumulation. Descending (e.g. due to a short break), Will be automatically called back.

[0047] Step 4: Abnormal data filtering and calibration triggering.

[0048] Student A At 240 seconds, it was 1.58, but later, due to the recovery of physiological state, the θ / α ratio dropped back to 1.3, with a deviation of -17.7% (>5%). The system re-collected data for nearly 5 minutes and updated , and reset .

[0049] Step 5: Collaborative judgment and graded intervention strategy.

[0050] When student A was stripping the cable, (<20%), (>1.2), triggering forced pause operation + neural recovery stimulation (10Hz strobe light + white noise).

[0051] The comparison of fatigue index monitoring and regulation effects is shown in Table 2.

[0052] Table 2

[0053] Among them, the intervention misjudgment rate: the number of times the system makes an incorrect judgment and intervenes / the total number of interventions.

[0054] In high-voltage cable operation training, trainees' misoperations due to fatigue were reduced by 60%, and the system response delay was <100ms, meeting the real-time requirements of high-risk scenarios. The method described in the present invention can be extended to high-risk industries such as chemical industry and aviation, providing a standardized neurocognitive enhancement solution for special operation training.

Claims

1. A method for coordinated regulation of concentration and fatigue based on EEG dual-modal feedback, characterized in that: The steps include: Before the training begins, the first EEG data of the trainees in the resting state is collected, and the average ratio of theta wave to alpha wave is calculated as the resting state baseline value of fatigue ; After the training started, the participants’ second EEG data were collected, and the concentration index was calculated based on the ratio of beta waves to the sum of beta waves, theta waves, and alpha waves, and the fatigue index was calculated based on the ratio of theta waves to alpha waves; According to the fatigue resting state reference value Calculate dynamic fatigue threshold; Set a static concentration threshold; Corresponding risk warnings are generated based on whether the concentration index deviates from the static concentration threshold and whether the fatigue index deviates from the dynamic fatigue threshold.

2. The method for coordinated control of concentration and fatigue based on EEG dual-modal feedback according to claim 1, characterized in that: The fatigue resting state reference value according to the Calculation of dynamic fatigue threshold includes: Dynamic fatigue threshold ; in, is the trend change value of the ratio of theta wave to alpha wave in the sliding window, , N is the sliding window length.

3. The method for coordinated control of concentration and fatigue based on EEG dual-modal feedback according to claim 2 is characterized in that: When the training start time is less than one sliding window time, The value is 0.

4. The method for coordinated control of concentration and fatigue based on EEG dual-modal feedback according to claim 1, characterized in that: The generating of corresponding risk warnings according to whether the concentration index and the fatigue index exceed their corresponding thresholds includes: When the concentration index is less than its threshold and the fatigue index is greater than its threshold, the highest intervention is triggered; When the concentration index is not less than its threshold, or the fatigue index is greater than its threshold, intermediate intervention is triggered; When the concentration index is not less than its threshold and the fatigue index is not greater than its threshold, low-intensity feedback is triggered.

5. The method for coordinated control of concentration and fatigue based on EEG dual-modal feedback according to claim 1, characterized in that: When the fatigue index deviates from its corresponding threshold by ±5% in three consecutive cycles, the automatic recalibration process is triggered; the triggering of the automatic recalibration process includes: Resample the first EEG data in a resting state, and calculate and update the resting state reference value of fatigue , based on the updated resting fatigue baseline Recalculate dynamic fatigue threshold.

6. A concentration-fatigue coordinated control system based on EEG dual-modal feedback, characterized in that: include: The fatigue resting state reference value calculation unit is used to collect the trainee's first resting state EEG data before the training begins, and calculate the average of the ratio of theta wave to alpha wave as the fatigue resting state reference value. ; The concentration-fatigue coordinated regulation unit is used to collect the trainees' second EEG data after the training begins, calculate the concentration index according to the ratio of beta waves to the sum of beta waves, theta waves and alpha waves, and calculate the fatigue index according to the ratio of theta waves to alpha waves; According to the fatigue resting state reference value Calculate dynamic fatigue threshold; Set a static concentration threshold; Corresponding risk warnings are generated based on whether the concentration index deviates from the static concentration threshold and whether the fatigue index deviates from the dynamic fatigue threshold.

7. The concentration-fatigue coordinated control system based on EEG dual-modal feedback according to claim 6 is characterized in that: In the concentration-fatigue coordinated regulation unit, the fatigue resting state reference value Calculation of dynamic fatigue threshold includes: Dynamic fatigue threshold ; in, is the trend change value of the ratio of theta wave to alpha wave in the sliding window, , N is the sliding window length.

8. The concentration-fatigue coordinated control system based on EEG dual-modal feedback according to claim 6 is characterized in that: In the concentration-fatigue coordinated control unit, the generating of corresponding risk warnings according to whether the concentration index and the fatigue index exceed their corresponding thresholds includes: When the concentration index is less than its threshold and the fatigue index is greater than its threshold, the highest intervention is triggered; When the concentration index is not less than its threshold, or the fatigue index is greater than its threshold, intermediate intervention is triggered; When the concentration index is not less than its threshold and the fatigue index is not greater than its threshold, low-intensity feedback is triggered; In the concentration-fatigue coordinated control unit, when the fatigue index deviates from its corresponding threshold by ±5% in three consecutive cycles, an automatic recalibration process is triggered; the triggering of the automatic recalibration process includes: Resample the first EEG data in a resting state, and calculate and update the resting state reference value of fatigue , based on the updated resting fatigue baseline Recalculate dynamic fatigue threshold.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the computer program is loaded into the processor, the concentration-fatigue coordinated regulation method based on EEG dual-modal feedback is implemented according to any one of claims 1-5.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the concentration-fatigue coordinated regulation method based on EEG dual-modal feedback is implemented according to any one of claims 1-5.

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