Driver's fatigue prediction by deep covariance learning from EEG

We present here deep covariance learning models for predicting drivers' drowsy and alert states from Electroencephalography (EEG). Three types of deep covariance learning models are proposed: SPDNet, CNN, and DNN on covariance matrices. Our test results show that all the deep covariance learning methods reported better performance than shallow learning methods including Riemannian methods and STCNN, a previously proposed CNN model for EEG classification. Among the deep covariance learning methods, the best classification performance is obtained by a CNN model applied on sample spatial EEG covariance matrices and it improved the AUC of the best shallow algorithm (logistic regression + Log-Euclidean Metric) by 12.32% from 70.96% to 86.14%. Our study showed that deep covariance learning is a very promising approach for drivers' fatigue prediction.