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.
Highlights
A brain-computer interface (BCI) is used between human and devices for recognizing user intention
In classifying five drowsiness levels, we obtained a classification performance of 0.69 (±0.02) that was a highly accurate compared to what would be expected by chance (0.2)
We demonstrated the feasibility of simple classification for two mental states as well as multi-classification for detailed drowsiness levels based on deep learning techniques
Summary
A brain-computer interface (BCI) is used between human and devices for recognizing user intention. Non-invasive BCI technology allows users to communicate with external devices without brain implant surgery [1,2,3,4] using the brain’s pattern encoding [5,6] or decoding ability [7,8,9]. As one of the critical issues, the BCI technology has been investigated for recognizing human mental states with high accuracy and decoding various types of mental conditions [20,21,22]. Detecting the drowsy state in a driving environment has mostly been done through camera-based vision technology using human face variability. This vision technology has achieved sufficient detection accuracy [31,32]. If the subjects wear glasses or do not look straight ahead, the camera cannot detect drowsy state robustly [33]
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