Citation

Tian S, Wang Y, Dong G, Pei W, Chen H. Conf. Proc. IEEE Eng. Med. Biol. Soc. 2018; 2018: 1980-1983.

Copyright

(Copyright © 2018, IEEE (Institute of Electrical and Electronics Engineers))

DOI

10.1109/EMBC.2018.8512666

PMID

30440787

Abstract

Mental fatigue induced by long time mental work can cause deterioration in task performance and increase the risk of accidents. Recently, electroencephalogram (EEG)-based monitoring of mental fatigue has received increasing attention in the field of brain-computer interfaces (BCI). This study aims to employ EEG signals to measure the mental fatigue level by estimating reaction time (RT) in a psychomotor vigilance task (PVT). In a 36-hour sleep deprivation experiment, EEG data from 18 subjects were recorded every four hours in nine blocks, each consisting of three tasks: a 6-minute PVT task and two 3-minute resting states (eyes closed and eyes open). The mean RT in the PVT task showed a generally increasing trend during the 36-hour awake period, reflecting the increase of fatigue over time. For each task, multiple EEG features were extracted and selected to better estimate RT using a multiple linear regression (MLR) method. The correlation between predicted RT and actual RT was evaluated using a leave-one-subject-out (LOSO) validation strategy. After parameter optimization, EEG data from the PVT task obtained a mean correlation coefficient of $0.81 pm 0.16$ across all subjects. Resting-state EEG data showed lower correlations (eyes-closed: $0.65 pm 0.20$, eyes-open: $0.50 pm 0.30)$ partially due to the involvement of shorter data lengths. These results demonstrate the feasibility and robustness of the EEG-based fatigue monitoring method, which could be potential for applications in operational environments.

Language: en