An Unsupervised Deep-Transfer-Learning-Based Motor Imagery EEG Classification Scheme for Brain-Computer Interface

Wang, Xuying, Yang, Rui ORCID: 0000-0002-5634-5476 and Huang, Mengjie (2022) An Unsupervised Deep-Transfer-Learning-Based Motor Imagery EEG Classification Scheme for Brain-Computer Interface. SENSORS, 22 (6). 2241-.

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Official URL: http://dx.doi.org/10.3390/s22062241

Abstract

Brain-computer interface (BCI) research has attracted worldwide attention and has been rapidly developed. As one well-known non-invasive BCI technique, electroencephalography (EEG) records the brain's electrical signals from the scalp surface area. However, due to the non-stationary nature of the EEG signal, the distribution of the data collected at different times or from different subjects may be different. These problems affect the performance of the BCI system and limit the scope of its practical application. In this study, an unsupervised deep-transfer-learning-based method was proposed to deal with the current limitations of BCI systems by applying the idea of transfer learning to the classification of motor imagery EEG signals. The Euclidean space data alignment (EA) approach was adopted to align the covariance matrix of source and target domain EEG data in Euclidean space. Then, the common spatial pattern (CSP) was used to extract features from the aligned data matrix, and the deep convolutional neural network (CNN) was applied for EEG classification. The effectiveness of the proposed method has been verified through the experiment results based on public EEG datasets by comparing with the other four methods.

Item Type:	Article
Uncontrolled Keywords:	brain-computer interface, motor imagery, electroencephalography, transfer learning, common spatial pattern
Divisions:	Faculty of Science and Engineering > School of Electrical Engineering, Electronics and Computer Science
Depositing User:	Symplectic Admin
Date Deposited:	06 Jun 2022 15:52
Last Modified:	18 Jan 2023 21:00
DOI:	10.3390/s22062241
Open Access URL:	https://www.mdpi.com/1424-8220/22/6/2241
Related URLs:	Author Publisher
URI:	https://livrepository.liverpool.ac.uk/id/eprint/3155963