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.