Classifying Features of Electroencephalography Signal to Detect Driver Drowsiness in the Early Drowsy Stage

Background and Objective: Driver drowsiness is one of the major reasons of severe accidents worldwide. In this study, an electroencephalography (EEG) measurement-based approach has been proposed to detect driver drowsiness. Materials and Methods: The driving tests were conducted in a driving simulator to collect brain data in the alert and drowsy states. Nineteen healthy men participated in these tests. The EEG signals were recorded from the central, parie-tal, and occipital regions of the brain. 12 features of EEG signal were extracted; then through neighborhood component analysis (NCA), a feature selection method, 6 features including mean, standard deviation (SD), kurtosis, energy, entro-py, and power of alpha band in 11-15 Hz, where alpha spindles occur, were selected. For the drowsiness stages assess-ment, the Observer Rating of Drowsiness (ORD) was applied. Four classifiers including k-nearest neighbor (KNN), support vector machine (SVM), classification tree, and Naive Bayes were employed to classify data. Results: The classification trees detected drowsiness in the early stage with 88.55%. The classification results showed that if only single-channel P4 was used for detecting drowsiness, the better performance could be achieved in compari-son to using data of all channels (C3, C4, P3, P4, O1, O2) together. The best performances were 93.13% which were obtained by the classification tree based on data of single-channel P4. Conclusion: This study suggested that the driver drowsiness was detectable based on single-channel P4 in the early stage.