Improved Domain Adaptation Network Based on Wasserstein Distance for Motor Imagery EEG Classification
Author:
Affiliation:
1. School of Automation, Hangzhou Dianzi University, Hangzhou, China
2. Department of Biomedical Engineering, University of Houston, Houston, TX, USA
3. Key Laboratory of Brain Machine Collaborative Intelligence of Zhejiang Province, Hangzhou, China
Funder
Zhejiang Provincial Natural Science Foundation of China
National Natural Science Foundation of China
National Key Research and Development Program
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
Biomedical Engineering,General Neuroscience,Internal Medicine,Rehabilitation
Link
http://xplorestaging.ieee.org/ielx7/7333/10031624/10035017.pdf?arnumber=10035017
Reference39 articles.
1. A novel deep learning approach for classification of EEG motor imagery signals
2. Wasserstein generative adversarial networks;arjovsky;Proc Int Conf Mach Learn,2017
3. Sub-band Common Spatial Pattern (SBCSP) for Brain-Computer Interface
4. A Multi-view CNN with Novel Variance Layer for Motor Imagery Brain Computer Interface
5. Learning Temporal Information for Brain-Computer Interface Using Convolutional Neural Networks
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