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...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:Journal of physics. Conference series Ročník 1631; číslo 1; s. 12007 - 12014
Hlavní autoři: Chen, Junjian, Yu, Zhuliang, Gu, Zhenghui
Médium: Journal Article
Jazyk:angličtina
Vydáno: Bristol IOP Publishing 01.09.2020
Témata:
Deep learning
Electroencephalography
Human-computer interface
Imagery
Physics
Semi-supervised learning
Teaching methods
ISSN:1742-6588, 1742-6596
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Popis
Shrnutí: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.
Bibliografie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:1742-6588
1742-6596
DOI:10.1088/1742-6596/1631/1/012007