Boosting Transfer Learning Improves Performance of Driving Drowsiness Classification Using EEG
2018 International Workshop on Pattern Recognition in Neuroimaging (PRNI)(2018)
摘要
Drowsy driving poses considerable risk not only to drivers themselves but also to other people on the road. It has been demonstrated that information contained in electroencephalography (EEG) signal can be used to identify driving drowsiness. To date, most of work focused on the detection of drowsiness within a session. This hampers the generalization of the trained model to a following session conducted after a few days. As we know, EEG is non-stationary and changes dramatically across sessions, which leads to a great challenge how to establish a model that has a good performance across sessions. In this study, we combined boosting strategy and transfer learning method to establish a model for identifying driving drowsiness states from alertness states based on the features of power spectral density (PSD). The model trained using the data collected a few days ago (session1) was tuned using very small portion of the data collected in the current session can achieve a good performance as tested in the current session (session2). The results demonstrated that the proposed boosting transfer learning method significantly outperformed the support vector machine (SVM) and AdaBoost methods. The proposed method could promote practical use of drowsiness detection system in a real vehicle due to its good cross-session performance.
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关键词
drowsiness detection system,transfer learning method,EEG signal,electroencephalography signal,power spectral density,support vector machine,SVM,AdaBoost method
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