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Classification of Drowsiness Levels Based on a Deep Spatio-Temporal Convolutional Bidirectional LSTM Network Using Electroencephalography Signals

Authors
Jeong, Ji-HoonYu, Baek-WoonLee, Dae-HyeokLee, Seong-Whan
Issue Date
12월-2019
Publisher
MDPI
Keywords
Brain-Computer Interface; electroencephalogram; mental states; drowsiness levels classification; deep learning
Citation
BRAIN SCIENCES, v.9, no.12
Indexed
SCIE
SCOPUS
Journal Title
BRAIN SCIENCES
Volume
9
Number
12
URI
https://scholar.korea.ac.kr/handle/2021.sw.korea/61488
DOI
10.3390/brainsci9120348
ISSN
2076-3425
Abstract
Non-invasive brain-computer interfaces (BCI) have been developed for recognizing human mental states with high accuracy and for decoding various types of mental conditions. In particular, accurately decoding a pilot's mental state is a critical issue as more than 70% of aviation accidents are caused by human factors, such as fatigue or drowsiness. In this study, we report the classification of not only two mental states (i.e., alert and drowsy states) but also five drowsiness levels from electroencephalogram (EEG) signals. To the best of our knowledge, this approach is the first to classify drowsiness levels in detail using only EEG signals. We acquired EEG data from ten pilots in a simulated night flight environment. For accurate detection, we proposed a deep spatio-temporal convolutional bidirectional long short-term memory network (DSTCLN) model. We evaluated the classification performance using Karolinska sleepiness scale (KSS) values for two mental states and five drowsiness levels. The grand-averaged classification accuracies were 0.87 (+/- 0.01) and 0.69 (+/- 0.02), respectively. Hence, we demonstrated the feasibility of classifying five drowsiness levels with high accuracy using deep learning.
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