EEG-based fatigue state evaluation by combining complex network and frequency-spatial features

Background

The proportion of traffic accidents caused by fatigue driving is increasing year by year, which has aroused wide concerns for researchers. In order to rapidly and accurately detect drivers' fatigue, this paper proposed an electroencephalogram (EEG)-based fatigue state evaluation method by combining complex network and frequency-spatial features.

New method

First, this paper constructed a complex network model based on the relative wavelet entropy to characterize the correlation strength information between channels. Then, the differential entropy and symmetry quotient were respectively calculated to extract frequency and spatial features. Then, the brain heat map combined the complex network and frequency-spatial features with different dimensions together as the fusion features. Finally, a convolutional neural network-long short-term memory (CNN-LSTM) neural network was used to evaluate the three-class fatigue states of the EEG data in the Shanghai Jiao Tong University (SJTU) Emotion EEG Dataset (SEED)-VIG dataset, and it was validated on the dataset on the Mendeley Data website.

Results

The experimental results of SEED-VIG dataset show that the average classification accuracy of three-class fatigue states, namely, awake, tired and drowsy, reaches 96.57 %. The average classification accuracy on the dataset on the Mendeley Data website reaches 99.23 %.

Comparison with existing methods

This method has a best evaluation performance compared with the state-of-the-art methods for the three-class fatigue states recognition.

Conclusions

The experiment results validated the feasibility of the fatigue state evaluation method based on the correlations between channels and the frequency-spatial features, which is of great significance for developing a driver fatigue detection system.