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/).
Cite this article
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 -