Citation

Ahn S, Nguyen T, Jang H, Kim JG, Jun SC. Front. Hum. Neurosci. 2016; 10: e219.

Affiliation

School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology Gwangju, South Korea.

Copyright

(Copyright © 2016, Frontiers Research Foundation)

DOI

10.3389/fnhum.2016.00219

PMID

27242483

Abstract

Investigations of the neuro-physiological correlates of mental loads, or states, have attracted significant attention recently, as it is particularly important to evaluate mental fatigue in drivers operating a motor vehicle. In this research, we collected multimodal EEG/ECG/EOG and fNIRS data simultaneously to develop algorithms to explore neuro-physiological correlates of drivers' mental states. Each subject performed simulated driving under two different conditions (well-rested and sleep-deprived) on different days. During the experiment, we used 68 electrodes for EEG/ECG/EOG and 8 channels for fNIRS recordings. We extracted the prominent features of each modality to distinguish between the well-rested and sleep-deprived conditions, and all multimodal features, except EOG, were combined to quantify mental fatigue during driving. Finally, a novel driving condition level (DCL) was proposed that distinguished clearly between the features of well-rested and sleep-deprived conditions. This proposed DCL measure may be applicable to real-time monitoring of the mental states of vehicle drivers. Further, the combination of methods based on each classifier yielded substantial improvements in the classification accuracy between these two conditions.

Language: en