Browsing by Author "Hajinoroozi, Mehdi"

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EEG-based prediction of driver\'s cognitive performance by deep convolutional neural network
(2016-09)
Hajinoroozi, Mehdi
;
Mao, Zijing
;
Jung, Tzyy-Ping
;
Lin, Chin-Teng
;
Huang, Yufei
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.
FEATURE EXTRACTION WITH DEEP BELIEF NETWORKS FOR DRIVER\'S COGNITIVE STATES PREDICTION FROM EEG DATA
(2015)
Hajinoroozi, Mehdi
;
Jung, Tzyy-Ping
;
Lin, Chin-Teng
;
Huang, Yufei
;
腦科學研究中心
This study considers the prediction of driver\'s cognitive states from electroencephalographic (EEG) data. Extracting EEG features correlated with driver\'s cognitive states is key for achieving accurate prediction. However, high dimensionality and temporal-and-spatial correlations of EEG data make extraction of effective features difficult. This study explores the approaches based on deep belief networks (DBN) for feature extraction and dimension reduction. Experimental results of this study showed that DBN applied to channel epochs (DBN-C) produces the most discriminant features and the best classification performance is achieved when DBN-C is applied to the time-frequency and independent-component analysis transformed EEG data. The results suggested that DBN-C is a promising new method for extracting complex, discriminant features for EEG-based brain computer interfaces.