Boosting Transfer Learning Improves Performance of Driving Drowsiness Classification Using EEG
Published in International Workshop on Pattern Recognition in Neuroimaging (PRNI), 2018
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
Recommended citation: J. He et al., "Boosting Transfer Learning Improves Performance of Driving Drowsiness Classification Using EEG," 2018 International Workshop on Pattern Recognition in Neuroimaging (PRNI), 2018, pp. 1-4, doi: 10.1109/PRNI.2018.8423951.