Journal
SIGNAL PROCESSING-IMAGE COMMUNICATION
Volume 47, Issue -, Pages 549-555Publisher
ELSEVIER
DOI: 10.1016/j.image.2016.05.018
Keywords
Deep neural network; Convolutional neural network; Cognitive states
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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.
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