A novel motor imagery EEG recognition method based on deep learning

DOI

10.2991/ifmeita-16.2016.133How to use a DOI?

Keywords

Motor Imagery EEG. Deep Belief Networks. Wavelet Packet Transform. Softmax. Brain Computer Interface. Deep Learning.

Abstract

The Motor Imagery electroencephalogram (MI-EGG) is time varying and subject-specific, its recognition needs the perfect adaptability and combination of feature extraction method and classifier. In this paper, Deep Belief Networks (DBN) is integrated with Wavelet Packet Transform (WPT) to yield a novel recognition method, denoted as WPT-DBN. Firstly, the MI-EEG is transformed into power signal and analyze the effective time domain. Then, WPT is applied to each channel of MI-EEG to obtain the effective time-frequency information. Finally, DBN is used for the identification and classification simultaneously. Experiments are conducted on a publicly available dataset, and the 5-fold cross validation experimental results show that WPT-DBN yields relatively higher classification accuracies compared to the existing approaches.

Copyright

© 2016, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Download article (PDF)

TY  - CONF
AU  - Ming-Ai Li
AU  - Meng Zhang
AU  - Yan-Jun Sun
PY  - 2016/01
DA  - 2016/01
TI  - A novel motor imagery EEG recognition method based on deep learning
BT  - Proceedings of the 2016 International Forum on Management, Education and Information Technology Application
PB  - Atlantis Press
SP  - 728
EP  - 733
SN  - 2352-5398
UR  - https://doi.org/10.2991/ifmeita-16.2016.133
DO  - 10.2991/ifmeita-16.2016.133
ID  - Li2016/01
ER  -