Driver drowsiness detection using the in-ear EEG

Driver drowsiness monitoring is one of the most demanded technologies for active prevention of severe road accidents. Electroencephalogram (EEG) and several peripheral signals have been suggested for the drowsiness monitoring. However, each type of signal has partial limitations in terms of either convenience or accuracy. Recent emerged concept of in-ear EEG raises expectations due to reduced obtrusiveness. It is yet unclear whether the in-ear EEG is effective enough for drowsiness detection in comparison with on-scalp EEG or peripheral signals. In this work, we evaluated performance of the in-ear EEG in drivers' alertness-drowsiness classification for the first time. Simultaneously, we also tested three peripheral signals including electrocardiogram (ECG), photoplethysmogram (PPG), and galvanic skin response (GSR) which have advantage in convenience of measurement. The classification analysis using the in-ear EEG resulted in high classification accuracy comparable to that of the individual on-scalp EEG channels. The ECG, PPG and GSR showed competitive performance but only when used together in pairwise combinations. Our results suggest that the in-ear EEG would be viable alternative to the single channel EEG or the individual peripheral signals for the drowsiness monitoring.