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
• ISSN: 1534-4320; 1558-0210; 1558-0210
• DOI: 10.1109/TNSRE.2018.2790359

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 transitioning laboratory-oriented BCI into real-world environments, one chief challenge is to obtain high-quality EEG with convenience and long-term wearing comfort. Recently, acquiring EEG from non-hair-bearing (NHB) scalp areas has been proposed as an alternative solution to avoid many of the technical limitations resulted from the interference of hair between electrodes and the skin. Furthermore, our pilot study has shown that informative drowsiness-related EEG features are accessible from the NHB areas. This study extends the previous work to quantitatively evaluate the performance of drowsiness detection using cross-session validation with widely studied machine-learning classifiers. The offline results showed no significant difference between the accuracy of drowsiness detection using the NHB EEG and the whole-scalp EEG across all subjects (p = 0.31). The findings of this study demonstrate the efficacy and practicality of the NHB EEG for drowsiness detection and could catalyze explorations and developments of many other real-world BCI applications.