Journal
IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING
Volume 26, Issue 2, Pages 400-406Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TNSRE.2018.2790359
Keywords
Brain-computer interfaces (BCI); electroencephalogram (EEG); non-hair-bearing electrodes; drowsiness
Categories
Funding
- Army Research Laboratory [W911NF-10-2-0022, W911NF-10-D-0002/TO 0023]
- Australian Research Council [DP180100670, DP180100656]
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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.
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