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Determining The Electromyographic Fatigue Threshold Following a Single Visit Exercise Test

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Published on: July 27, 2015

[Research on fatigue recognition based on graph convolutional neural network and electroencephalogram signals].

Song Li¹, Yunfa Fu², Yan Zhang¹

¹Intelligent System Laboratory of Qinghai University, Xining 810000, P. R. China.

Sheng Wu Yi Xue Gong Cheng Xue Za Zhi = Journal of Biomedical Engineering = Shengwu Yixue Gongchengxue Zazhi

|August 31, 2025

Summary

This summary is machine-generated.

This study uses electroencephalogram (EEG) signals and a graph convolutional neural network (GCNN) to accurately detect driver fatigue. The method achieves high accuracy, enhancing safe driving brain-computer interfaces.

Keywords:

Electroencephalogram signals Fatigue driving Graph convolutional neural network Pearson correlation

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Last Updated: May 5, 2026

Determining The Electromyographic Fatigue Threshold Following a Single Visit Exercise Test

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Published on: July 27, 2015

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A Single-Channel and Non-Invasive Wearable Brain-Computer Interface for Industry and Healthcare

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Published on: July 7, 2023

Area of Science:

Neuroscience
Machine Learning
Human-Computer Interaction

Context:

Driver fatigue is a major cause of road accidents.
Electroencephalogram (EEG) signals offer a promising method for objective fatigue detection.
Existing methods may not fully capture the complex interdependencies within EEG data.

Purpose:

To develop a robust method for classifying driving states (awake, tired, drowsy) using EEG.
To leverage Graph Convolutional Neural Networks (GCNNs) for analyzing EEG channel relationships.
To utilize the SEED-VIG dataset for evaluating the proposed fatigue detection model.

Summary:

An adjacency matrix was constructed using Pearson correlation coefficients of EEG signals and channel positions.
A GCNN model was developed and trained on the SEED-VIG dataset, incorporating differential entropy (DE) features.
The model achieved an average classification accuracy of 91.66% for detecting driving states.

Impact:

Demonstrates a reliable and accurate approach for fatigue driving detection.
Achieved a highest classification accuracy of 98.87% and an average Kappa coefficient of 0.83.
Provides a guideline for advancing safe driving brain-computer interface research.