Journal
IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING
Volume 26, Issue 11, Pages 2086-2095Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TNSRE.2018.2876129
Keywords
Electroencephalograph (EEG); long short-term memory (LSTM); motor imagery; one dimension-aggregate approximation (1d-AX)
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Funding
- National Natural Science Foundation of China [61471157, 61772090, 61801055]
- Fundamental Research Funds for the Central Universities [26120182018B23014, 2018B47114]
- Key Development Program of Jiangsu Province of China [BE2017071, BE2017647, BE2018004-04]
- Projects of International Cooperation and Exchanges of Changzhou of China [CZ20170018]
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Classification of motor imagery electroencephalograph signals is a fundamental problem in brain-computer interface (BCI) systems. We propose in this paper a classification framework based on long short-term memory (LSTM) networks. To achieve robust classification, a one dimension-aggregate approximation (1d-AX) is employed to extract effective signal representation for LSTM networks. Inspired by classical common spatial pattern, channel weighting technique is further deployed to enhance the effectiveness of the proposed classification framework. Public BCI competition data are used for the evaluation of the proposed feature extraction and classification network, whose performance is also compared with that of the state-of-the-arts approaches based on other deep networks.
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