Euclidean space data alignment approach for multi-channel LSTM network in EEG based fatigue driving detection

Abstract

Fatigue driving is the main reason of traffic accidents. The electroencephalograph (EEG) signal can accurately reflect the changes of human physiological state, so EEG is considered as a reliable method to detect fatigue driving. In this letter, the Euclidean space data alignment approach is adopted to reduce individual differences. Then, to consider the spatial correlation between multiple channels EEG signals and further improve the accuracy, an efficient long short-term memory (LSTM) network structure is designed to detect the fatigue state of drivers. The experimental result shows that the method improves the generalisation capability and achieves the performance with an average accuracy of 95.70% for three categories. Evaluation of the algorithm performance in comparison with other traditional methods indicates that the method has higher accuracy and lower standard deviation. Generally speaking, the proposed method is promising for the application of fatigue driving monitoring.