Fatigue Detection with Covariance Manifolds of Electroencephalography in Transportation Industry

Driver fatigue has become a leading cause of accidents and death in the transportation industry. Electroencephalography (EEG)-based fatigue detection can be a good way to reduce accidents and improve safety and efficiencies throughout the transportation system. In this article, we focus on investigating whether the spatial-temporal changes in the relations between EEG channels are specific to different driving states. EEG signals were first partitioned into several segments, and the covariance matrices obtained from each segment were input into a recurrent neural network to extract high-level temporal features. Meanwhile, the covariance matrices of whole signals were leveraged to extract spatial characteristics that were fused with temporal features to obtain comprehensive spatial-temporal information. In experiments on an open benchmark dataset, our method achieved an excellent classification accuracy of 89.28% and showed superior performance compared to several other state-of-the-art methods. These results indicate that our method can enable higher performance in driver fatigue detection.