An EEG-Based Fatigue Detection and Mitigation System

• Published in: International journal of neural systems Vol. 26; no. 4; p. 1650018
• Main Authors: Huang, Kuan-Chih, Huang, Teng-Yi, Chuang, Chun-Hsiang, King, Jung-Tai, Wang, Yu-Kai, Lin, Chin-Teng, Jung, Tzyy-Ping
• Format: Journal Article
• Language: English
• Published: Singapore 01.06.2016
• Subjects: Acoustic Stimulation - methods; Adult; Alpha Rhythm - physiology; Attention - physiology; Automobile Driving - psychology; Brain - physiopathology; Cognition - physiology; Electroencephalography - methods; Fatigue - diagnosis; Fatigue - physiopathology; Fatigue - therapy; Feasibility Studies; Feedback, Psychological - physiology; Female; Humans; Male; Reaction Time; Time Factors; User-Computer Interface; Young Adult; fatigue; driving safety; EEG; auditory feedback; brain dynamics
• ISSN: 0129-0657
• DOI: 10.1142/s0129065716500180

Research has indicated that fatigue is a critical factor in cognitive lapses because it negatively affects an individual's internal state, which is then manifested physiologically. This study explores neurophysiological changes, measured by electroencephalogram (EEG), due to fatigue. This study further demonstrates the feasibility of an online closed-loop EEG-based fatigue detection and mitigation system that detects physiological change and can thereby prevent fatigue-related cognitive lapses. More importantly, this work compares the efficacy of fatigue detection and mitigation between the EEG-based and a nonEEG-based random method. Twelve healthy subjects participated in a sustained-attention driving experiment. Each participant's EEG signal was monitored continuously and a warning was delivered in real-time to participants once the EEG signature of fatigue was detected. Study results indicate suppression of the alpha- and theta-power of an occipital component and improved behavioral performance following a warning signal; these findings are in line with those in previous studies. However, study results also showed reduced warning efficacy (i.e. increased response times (RTs) to lane deviations) accompanied by increased alpha-power due to the fluctuation of warnings over time. Furthermore, a comparison of EEG-based and nonEEG-based random approaches clearly demonstrated the necessity of adaptive fatigue-mitigation systems, based on a subject's cognitive level, to deliver warnings. Analytical results clearly demonstrate and validate the efficacy of this online closed-loop EEG-based fatigue detection and mitigation mechanism to identify cognitive lapses that may lead to catastrophic incidents in countless operational environments.