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Tactile Vibrating Toolkit and Driving Simulation Platform for Driving-Related Research

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Published on: December 18, 2020

Research on driving fatigue detection based on basic scale entropy and MVAR-PSI.

Fuwang Wang¹, Xiaogang Kang¹, Rongrong Fu²

¹School of Mechanic Engineering, Northeast Electric Power University, Jilin City, Jilin Province, People's Republic of China.

Biomedical Physics & Engineering Express

|July 5, 2022

Summary

Detecting driver fatigue using electroencephalogram (EEG) signals is crucial for safety. This study found that changes in brain connectivity patterns, specifically in the prefrontal cortex, effectively indicate the transition from alert to fatigued driving states.

Keywords:

EEG MVAR-PSI method basic scale entropy driving fatigue detection effect connection

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Area of Science:

Neuroscience
Traffic Safety Engineering
Biomedical Signal Processing

Background:

Driver fatigue is a primary cause of traffic accidents during prolonged driving.
Accurate and timely detection of driver fatigue is essential for enhancing road safety.
Electroencephalogram (EEG) signals offer a promising avenue for monitoring driver cognitive states.

Purpose of the Study:

To develop and validate a method for detecting driver fatigue using EEG signals.
To analyze changes in brain connectivity and signal complexity associated with fatigue.
To establish reliable fatigue characteristics for continuous driving monitoring.

Main Methods:

Preprocessing EEG signals to remove interference.
Applying Butterworth band-pass filtering to extract alpha and beta rhythms.
Calculating basic scale entropy of alpha and beta rhythms as a fatigue indicator.
Utilizing the fast multiple autoregressive (MVAR) model and phase slope index (PSI) to estimate effective connectivity.
Analyzing causality flow direction in prefrontal regions during different driving stages.
Correlating basic scale entropy, clustering coefficient, and global efficiency.

Main Results:

Causality flow outflow from the prefrontal lobe decreases as drivers transition from alert to fatigued states.
The prefrontal cortex shifts from being a source to a target of causality with increasing fatigue.
Basic scale entropy, clustering coefficient, and global efficiency show strong correlations, validating their use as fatigue indicators.
The combination of basic scale entropy and MVAR-PSI effectively detects long-term driving fatigue.

Conclusions:

Changes in prefrontal cortex causality flow dynamics are reliable markers of driving fatigue.
Basic scale entropy, in conjunction with MVAR-PSI, provides an effective approach for real-time fatigue detection.
This method holds significant potential for improving safety in long-term driving scenarios.