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A method for calibrating the critical point of mental fatigue based on self-organized criticality

A self-organizing criticality and mental fatigue technology, applied in the field of biomedical signal processing and analysis, can solve the problems of signal mixing, difficulty in accurately distinguishing different states of operators, low signal-to-noise ratio, and low spatial resolution, and achieve high reliability. Effect

Active Publication Date: 2021-08-10
纽桥智能科技(上海)有限公司
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Problems solved by technology

In particular, the EEG signal-to-noise ratio and spatial resolution are low, which is prone to signal mixing problems
For continuous tasks, the complexity of the environment and the dynamic changes in task requirements have brought great difficulties to the calibration of fatigue critical points and the accurate distinction of different states of operators

Method used

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  • A method for calibrating the critical point of mental fatigue based on self-organized criticality
  • A method for calibrating the critical point of mental fatigue based on self-organized criticality
  • A method for calibrating the critical point of mental fatigue based on self-organized criticality

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Embodiment Construction

[0032] In order to deepen the understanding of the present invention, the present invention will be further described in detail in connection with the accompanying drawings and examples, and the embodiments are not limited to the scope of the invention.

[0033] Refer figure 1 One embodiment of the present invention provides a method of standardizing a mental fatigue perspective of self-organizing criticality. During driving, distinguishing between mental fatigue state includes the following steps:

[0034] Step 1, according to the international standard 10-20 EEG electrode positioning system, figure 2 The 61 electrodes are used to collect the driver's long-time eElectronic signal during fatigue driving, while recording driving behavior data, including: lane position variability, with the vehicle distance and real-time speed. Then, the electromal signal is subjected to data pretreatment, including the removal of 50 Hz baseline drift, band pass filter (0.5-30 Hz), mining sample and...

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Abstract

The invention belongs to the technical field of biomedical signal processing and analysis, and provides a mental fatigue critical point calibration method based on self-organized criticality, using the dynamic characteristics of the brain network to construct a self-organized critical model, and performing avalanche dynamics deduction of mental fatigue, which conforms to fatigue Internal mechanism of complexity evolution, calibrated critical state is dynamically stable and robust. Through the verification of behavioral data, the reliability of the critical state of mental fatigue determined from the two dimensions of physiology and behavior is higher, which can provide support for guiding the setting of fatigue category labels to complete more accurate classification and identification.

Description

Technical field [0001] The present invention belongs to the field of biomedical signal processing analysis, mainly based on EEG signals, proposes a mental fatigue, a standard, a mental fatigue, a spiritual fatigue perspective. Background technique [0002] With the rapid development of society, the pressure of work and learning is growing, long-term work and learning will lead to sleepy oriented, vague thinking, slow action ability, and even mental fatigue. For special industry staff, such as long-distance bus drivers or pilots, mental fatigue can lead to driver's decline, mainly manifested as a point of attention, dozing off, narrowed, and the response is slow, and severely constitutes a threat to driving safety. Therefore, identifying meaningful fatigue metrics, mental fatigue detection is made to enhance the persistent work ability of special industry, and ensure that personnel safety have important practical significance. [0003] EECTROENCEPHALOGRAPH, EEG is recorded in the ...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): A61B5/369A61B5/00
CPCA61B5/7264A61B2503/22
Inventor 张驰李莹丛丰裕高寒冰
Owner 纽桥智能科技(上海)有限公司
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