Semi-supervised Deep Learning in Motor Imagery-Based Brain-Computer Interfaces with Stacked Variational Autoencoder

Recently, deep learning methods have contributed to the development of motor imagery (MI) based brain-computer interface (BCI) research. However, these methods typically focused on supervised deep learning with the labelled data and failed to learn from the unlabelled data, where additional informat...

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Bibliographic Details
Published in:Journal of physics. Conference series Vol. 1631; no. 1; pp. 12007 - 12014
Main Authors: Chen, Junjian, Yu, Zhuliang, Gu, Zhenghui
Format: Journal Article
Language:English
Published: Bristol IOP Publishing 01.09.2020
Subjects:
Deep learning
Electroencephalography
Human-computer interface
Imagery
Physics
Semi-supervised learning
Teaching methods
ISSN:1742-6588, 1742-6596
Online Access:Get full text
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Summary:Recently, deep learning methods have contributed to the development of motor imagery (MI) based brain-computer interface (BCI) research. However, these methods typically focused on supervised deep learning with the labelled data and failed to learn from the unlabelled data, where additional information may be critical for performance improvement in MI decoding. To address this problem, we propose a semi-supervised deep learning method based on the stacked variational autoencoder (SVAE) for MI decoding, where the input to the network is an envelope representation of EEG signal. Under the framework of SVAE, the labelled training data and unlabelled test data can be trained collaboratively. Experimental evaluation on the BCI IV 2a dataset reveals that SVAE outperforms competing methods and it also yields state-of-the-art performance in decoding MI tasks. Hence, the proposed method is a promising tool in the research of the MI-based BCI system.
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ISSN:1742-6588
1742-6596
DOI:10.1088/1742-6596/1631/1/012007