Exploring spatial-frequency-sequential relationships for motor imagery classification with recurrent neural network.

Journal: BMC bioinformatics

Published Date: Sep 29, 2018

Abstract

BACKGROUND: Conventional methods of motor imagery brain computer interfaces (MI-BCIs) suffer from the limited number of samples and simplified features, so as to produce poor performances with spatial-frequency features and shallow classifiers.

Authors

Tian-Jian Luo

Department of Cognitive Science, School of Information Science and Engineering, Xiamen University, 422 Siming South Road, Siming District, Xiamen, 361005, China.
Chang-le Zhou

Department of Cognitive Science, School of Information Science and Engineering, Xiamen University, 422 Siming South Road, Siming District, Xiamen, 361005, China.
Fei Chao

Department of Cognitive Science, School of Information Science and Engineering, Xiamen University, 422 Siming South Road, Siming District, Xiamen, 361005, China. fchao@xmu.edu.cn.

Keywords

Algorithms Brain-Computer Interfaces Databases, Genetic Datasets as Topic Electroencephalography Humans Imagination Neural Networks, Computer Signal Processing, Computer-Assisted Support Vector Machine

External Resources

View on PubMed Access via DOI PubMed (30268089)

Exploring spatial-frequency-sequential relationships for motor imagery classification with recurrent neural network.

Abstract

Authors

Keywords

External Resources

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