NDLI: Boosting template-based SSVEP decoding by cross-domain transfer learning

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Boosting template-based SSVEP decoding by cross-domain transfer learning

Content Provider	Scilit
Author	Chiang, Kuan-Jung Wei, Chun-Shu Nakanishi, Masaki Jung, Tzyy-Ping
Copyright Year	2020
Description	Journal: Journal of Neural Engineering Objective. This study aims to establish a generalized transfer-learning framework for boosting the performance of steady-state visual evoked potential (SSVEP)-based brain–computer interfaces (BCIs) by leveraging cross-domain data transferring. Approach. We enhanced the state-of-the-art template-based SSVEP decoding through incorporating a least-squares transformation (LST)-based transfer learning to leverage calibration data across multiple domains (sessions, subjects, and electroencephalogram montages). Main results. Study results verified the efficacy of LST in obviating the variability of SSVEPs when transferring existing data across domains. Furthermore, the LST-based method achieved significantly higher SSVEP-decoding accuracy than the standard task-related component analysis (TRCA)-based method and the non-LST naive transfer-learning method. Significance. This study demonstrated the capability of the LST-based transfer learning to leverage existing data across subjects and/or devices with an in-depth investigation of its rationale and behavior in various circumstances. The proposed framework significantly improved the SSVEP decoding accuracy over the standard TRCA approach when calibration data are limited. Its performance in calibration reduction could facilitate plug-and-play SSVEP-based BCIs and further practical applications.
Related Links	https://iopscience.iop.org/article/10.1088/1741-2552/abcb6e/pdf
ISSN	17412560
e-ISSN	17412552
DOI	10.1088/1741-2552/abcb6e
Journal	Journal of Neural Engineering
Issue Number	1
Volume Number	18
Language	English
Publisher	IOP Publishing
Publisher Date	2021-02-01
Access Restriction	Open
Subject Keyword	Journal: Journal of Neural Engineering Medical Informatics Brain-computer Interface Transfer Learning
Content Type	Text
Resource Type	Article
Subject	Cellular and Molecular Neuroscience Biomedical Engineering

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