Brain dynamic states analysis based on 3D convolutional neural network

Publication Type:
Conference Proceeding
Citation:
2017 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2017, 2017, 2017-January pp. 222 - 227
Issue Date:
2017-11-27
Metrics:
Full metadata record
Files in This Item:
Filename Description Size
Brain Dynamic States Analysis based 3D Convolutional Neural Network_final.pdfAccepted manucript564.43 kB
Adobe PDF
© 2017 IEEE. Drowsiness driving is one major factor of traffic accident. Monitoring the changes of brain signals provides an effective and direct way for drowsiness detection. One 3D convolutional neural network (3D CNN)-based forecasting system has been proposed to monitor electroencephalography (EEG) signals and predict fatigue level during driving. The limited weight sharing and channel-wise convolution were both applied to extract the significant phenomenon in various frequency bands of brain signals and the spatial information of EEG channel location, respectively. The proposed 3D CNN with limited weight sharing and channel-wise convolution has been demonstrated to predict reaction time (RT) of driving with low root mean square error (RMSE) through the brain dynamics. This proposed approach outperforms with the state-of-the-art algorithms, such as traditional CNN, Neural Network (NN), and support vector regression (SVR). Compared with traditional CNN and Artificial Neural Network, the RMSE of 3D CNN-based RT prediction has been improved 9.5% (RMSE from 0.6322 to 0.5720) and 8% (RMSE from 0.6217 to 0.5720), respectively. We envision that this study might open a new branch between deep learning application in neuro-cognitive analysis and real world application.
Please use this identifier to cite or link to this item: