Spectral Analysis of EEG During Microsleep Events Annotated via Driver Monitoring System to Characterize Drowsiness

Abstract

Drowsiness detection has a significant importance in aviation industry. Electroencephalogram (EEG) has been extensively studied to characterize drowsiness; nevertheless, its behavior with respect to accurately annotated drowsy periods remains to be investigated. At present, Karolinska Sleepiness Scale and psychomotor vigilance task are widely used references for subjective drowsiness analysis. However, their practical application in real-time monitoring of alertness is limited. To address this limitation, we combined Seeing Machines driver monitoring system and electrooculogram (EOG) for localization of microsleep (MS) events and studied EEG spectral behavior during MS events. EEG, EOG, and facial behavior data were recorded simultaneously from 16 commercially rated pilots during simulated flight. Relative spectral power in delta (0.5-4 Hz), theta (4-8 Hz), alpha (8-13 Hz), and beta (13-30 Hz) in frontal, central, temporal, parietal, and occipital brain regions was analyzed. Compared to baseline, delta power reduced during MS (p0.05). The research findings highlight the capability of EEG delta and alpha spectrum toward characterizing MS events; therefore, with consideration to user acceptability, application toward drowsiness detection is plausible via EEG electrodes embedded in the typical aviation headset.

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