EEG-based prediction of driver's cognitive performance by deep convolutional neural network

Mehdi Hajinoroozi, Zijing Mao, Tzyy Ping Jung, Chin-Teng Lin, Yufei Huang

Research output: Contribution to journalArticlepeer-review

87 Scopus citations


We considered the prediction of driver's cognitive states related to driving performance using EEG signals. We proposed a novel channel-wise convolutional neural network (CCNN) whose architecture considers the unique characteristics of EEG data. We also discussed CCNN-R, a CCNN variation that uses Restricted Boltzmann Machine to replace the convolutional filter, and derived the detailed algorithm. To test the performance of CCNN and CCNN-R, we assembled a large EEG dataset from 3 studies of driver fatigue that includes samples from 37 subjects. Using this dataset, we investigated the new CCNN and CCNN-R on raw EEG data and also Independent Component Analysis (ICA) decomposition. We tested both within-subject and cross-subject predictions and the results showed CCNN and CCNN-R achieved robust and improved performance over conventional DNN and CNN as well as other non-DL algorithms. (C) 2016 Elsevier B.V. All rights reserved.
Original languageEnglish
Pages (from-to)549-555
Number of pages7
JournalSignal Processing: Image Communication
StatePublished - Sep 2016


  • Deep neural network
  • Cognitive states
  • Convolutional neural network

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