Details

Autor(en) / Beteiligte

• Hajinoroozi, Mehdi
• Zhang, Jianqiu Michelle
• Yufei Huang

Titel

Driver's fatigue prediction by deep covariance learning from EEG

Ist Teil von

• 2017 IEEE International Conference on Systems, Man, and Cybernetics (SMC), 2017, p.240-245

Ort / Verlag

IEEE

Erscheinungsjahr

2017

Quelle

IEL

Beschreibungen/Notizen

• 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.