Zhang, Y, Xie, SQ orcid.org/0000-0002-8082-9112, Shi, C et al. (2 more authors) (2023) Cross-Subject Transfer Learning for Boosting Recognition Performance in SSVEP-based BCIs. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 31. pp. 1574-1583. ISSN 1558-0210
Abstract
Steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs) have been substantially studied in recent years due to their fast communication rate and high signal-to-noise ratio. The transfer learning is typically utilized to improve the performance of SSVEP-based BCIs with auxiliary data from the source domain. This study proposed an inter-subject transfer learning method for enhancing SSVEP recognition performance through transferred templates and transferred spatial filters. In our method, the spatial filter was trained via multiple covariance maximization to extract SSVEP-related information. The relationships between the training trial, the individual template, and the artificially constructed reference are involved in the training process. The spatial filters are applied to the above templates to form two new transferred templates, and the transferred spatial filters are obtained accordingly via the least-square regression. The contribution scores of different source subjects can be calculated based on the distance between the source subject and the target subject. Finally, a four-dimensional feature vector is constructed for SSVEP detection. To demonstrate the effectiveness of the proposed method, a publicly available dataset and a self-collected dataset were employed for performance evaluation. The extensive experimental results validated the feasibility of the proposed method for improving SSVEP detection.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | This is an author produced version of an article accepted for publication in IEEE Transactions on Neural Systems and Rehabilitation Engineering, made available under the terms of the Creative Commons Attribution License (CC-BY), which permits unrestricted use, distribution and reproduction in any medium, provided the original work is properly cited. |
Keywords: | Brain–computer interface (BCI) , electroencephalography (EEG) , steady-state visual evoked potential (SSVEP) , transfer learning , cross-subject |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Electronic & Electrical Engineering (Leeds) > Robotics, Autonomous Systems & Sensing (Leeds) |
Funding Information: | Funder Grant number EPSRC (Engineering and Physical Sciences Research Council) EP/S019219/1 UKRI (UK Research and Innovation) Not Known |
Depositing User: | Symplectic Publications |
Date Deposited: | 09 Mar 2023 14:18 |
Last Modified: | 12 May 2023 01:19 |
Status: | Published |
Publisher: | IEEE |
Identification Number: | 10.1109/TNSRE.2023.3250953 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:196816 |