Classification of pilots’ mental states using a multimodal deep learning network

Soo Yeon Han, No Sang Kwak, Taegeun Oh, Seong Whan Lee

Research output: Contribution to journalArticlepeer-review

29 Citations (Scopus)


An automation system for detecting the pilot's diversified mental states is an extremely important and essential technology, as it could prevent catastrophic accidents caused by the deteriorated cognitive state of pilots. Various types of biosignals have been employed to develop the system, since they accompany neurophysiological changes corresponding to the mental state transitions. In this study, we aimed to investigate the feasibility of a robust detection system of the pilot's mental states (i.e., distraction, workload, fatigue, and normal) based on multimodal biosignals (i.e., electroencephalogram, electrocardiogram, respiration, and electrodermal activity) and a multimodal deep learning (MDL) network. To do this, first, we constructed an experimental environment using a flight simulator in order to induce the different mental states and to collect the biosignals. Second, we designed the MDL architecture – which consists of a convolutional neural network and long short-term memory models – to efficiently combine the information of the different biosignals. Our experimental results successfully show that utilizing multimodal biosignals with the proposed MDL could significantly enhance the detection accuracy of the pilot's mental states.

Original languageEnglish
Pages (from-to)324-336
Number of pages13
JournalBiocybernetics and Biomedical Engineering
Issue number1
Publication statusPublished - 2020 Jan 1


  • ECG
  • EDA
  • EEG
  • MDL
  • Pilot's mental states
  • Respiration

ASJC Scopus subject areas

  • Biomedical Engineering


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