Toward Drowsiness Detection Using Non-hair-Bearing EEG-Based Brain-Computer Interfaces

• Published in: IEEE transactions on neural systems and rehabilitation engineering Vol. 26; no. 2; pp. 400 - 406
• Main Authors: Wei, Chun-Shu, Wang, Yu-Te, Lin, Chin-Teng, Jung, Tzyy-Ping
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.02.2018; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Automobile Driving - psychology; Automobiles; Brain; Brain-Computer Interfaces; Brain-computer interfaces (BCI); Cognition - physiology; Computer applications; Discriminant Analysis; Driving ability; Drowsiness; EEG; Electrodes; electroencephalogram (EEG); Electroencephalography; Electroencephalography - methods; Fatigue; Feasibility studies; Feature extraction; Hair; Human-computer interface; Humans; Implants; Interfaces; Interference; Learning algorithms; non-hair-bearing electrodes; Pilot Projects; Reproducibility of Results; Scalp; Skin; Sleep deprivation; Support Vector Machine; Wakefulness - physiology

Drowsy driving is one of the major causes that lead to fatal accidents worldwide. For the past two decades, many studies have explored the feasibility and practicality of drowsiness detection using electroencephalogram (EEG)based brain-computer interface (BCI) systems. However, on the pathway of tra...