TY - JOUR
T1 - Exploring neuro-physiological correlates of drivers’ mental fatigue caused by sleep deprivation using simultaneous EEG, ECG, and fNIRS data
AU - Ahn, Sangtae
AU - Nguyen, Thien
AU - Jang, Hyojung
AU - Kim, Jae G.
AU - Jun, Sung C.
N1 - Publisher Copyright:
© 2016 Ahn, Nguyen, Jang, Kim and Jun.
PY - 2016/5/13
Y1 - 2016/5/13
N2 - 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.
AB - 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.
KW - Drivers’ mental fatigue
KW - Driving condition level
KW - EEG/ECG/EOG/fNIRS
KW - Multimodal integration
KW - Neuro-physiological correlates
KW - Simulated driving
KW - Sleep deprivation
UR - http://www.scopus.com/inward/record.url?scp=84973401335&partnerID=8YFLogxK
U2 - 10.3389/fnhum.2016.00219
DO - 10.3389/fnhum.2016.00219
M3 - Article
AN - SCOPUS:84973401335
SN - 1662-5161
VL - 10
JO - Frontiers in Human Neuroscience
JF - Frontiers in Human Neuroscience
IS - MAY2016
M1 - 219
ER -