Classification of drowsiness levels based on a deep spatio-temporal convolutional bidirectional lstm network using electroencephalography signals

Ji Hoon Jeong, Baek Woon Yu, Dae Hyeok Lee, Seong Whan Lee

Research output: Contribution to journalArticlepeer-review

8 Citations (Scopus)

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.

Original languageEnglish
Article number348
JournalBrain Sciences
Volume9
Issue number12
DOIs
Publication statusPublished - 2019

Keywords

  • Brain-computer interface
  • Deep learning
  • Drowsiness levels classification
  • Electroencephalogram
  • Mental states

ASJC Scopus subject areas

  • Neuroscience(all)

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