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Continuous EEG Decoding of Pilots' Mental States Using Multiple Feature Block-Based Convolutional Neural Network

Authors
Lee, Dae-HyeokJeong, Ji-HoonKim, KidukYu, Baek-WoonLee, Seong-Whan
Issue Date
2020
Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Keywords
Brain-computer interface (BCI); electroencephalogram (EEG); mental states; deep convolutional neural network
Citation
IEEE ACCESS, v.8, pp.121929 - 121941
Indexed
SCIE
SCOPUS
Journal Title
IEEE ACCESS
Volume
8
Start Page
121929
End Page
121941
URI
https://scholar.korea.ac.kr/handle/2021.sw.korea/58937
DOI
10.1109/ACCESS.2020.3006907
ISSN
2169-3536
Abstract
Non-invasive brain-computer interface (BCI) has been developed for recognizing and classifying human mental states with high performances. Specifically, classifying pilots' mental states accurately is a critical issue because their cognitive states, which are induced by mental fatigue, workload, and distraction, may be fundamental in catastrophic accidents. In this study, we present an electroencephalogram (EEG) classification of four mental states (fatigue, workload, distraction, and the normal state) from EEG signals in both offline and pseudo-online analyses. To the best of our knowledge, this study is the first attempt to classify pilots' mental states using only EEG signals during continuous decoding. We recorded EEG signals from seven pilots under various simulated flight conditions. We proposed a multiple feature block-based convolutional neural network (MFB-CNN) with temporal-spatio EEG filters to recognize the pilot's current mental states. We validated the proposed method for two analyses across all subjects. In the offline analysis, we confirmed the classification accuracy of 0.75 (+/- 0.04). Also, in the pseudo-online analysis, we obtained the detection accuracy of 0.72 (+/- 0.20), 0.72 (+/- 0.27), and 0.61 (+/- 0.18) for fatigue, workload, and distraction, respectively. Hence, we demonstrate the feasibility of classifying various types of mental states for implementation in real-world environments.
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