Key result
Optimized machine learning of nonlinear brain signals detects driving drowsiness and guides comfortable electrode placement.
Why the study?
Drowsiness while driving can cause accidents leading to harm, and machine learning methods using brain signals can help detect drowsiness while minimizing participant discomfort through optimal electrode placement.
Optimized machine learning methods using nonlinear features of EEG signals can effectively detect drowsiness, potentially preventing driving accidents.
May support EEG drowsiness monitoring in drivers; leaves open validation in larger trials before clinical use.
Drowsiness while driving can lead to accidents that are related to the loss of perception during emergencies that harm the health. Among physiological signals, brain waves have been used as informative signals for the analyses of behavioral observations, steering information, and other biosignals during drowsiness. We inspected the machine learning methods for drowsiness detection based on brain signals with varying quantities of information. The results demonstrated that machine learning could be utilized to compensate for a lack of information and to account for individual differences. Cerebral area selection approaches to decide optimal measurement locations could be utilized to minimize the discomfort of participants. Although other statistics could provide additional information in further study, the optimized machine learning method could prevent the dangers of drowsiness while driving by considering a transitional state with nonlinear features. Because brain signals can be altered not only by mental fatigue but also by health status, the optimization analysis of the system hardware and software will be able to increase the power-efficiency and accessibility in acquiring brain waves for health enhancements in daily life.
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Hong et al. (2021) studied Drowsiness while driving. Machine learning methods with nonlinear features was evaluated on Drowsiness detection. Optimized machine learning methods using nonlinear features of brain signals can detect drowsiness while driving and determine optimal electrode placement to minimize participant discomfort.
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