Enhancing Domain Diversity of Transfer Learning-Based SSVEP-BCIs by the Reconstruction of Channel Correlation

The RCC-enhanced transfer learning has the potential to facilitate the practical implementation of SSVEP-BCIs in real-world scenarios.

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Publication Details

Journal
IEEE Transactions on Biomedical Engineering
Published
2024-09-10
DOI
https://doi.org/10.1109/tbme.2024.3458389
Citations
7
Primary Topic
Neural Networks and Applications
Type
article
Field-Weighted Citation Impact
2.43
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article

Enhancing Domain Diversity of Transfer Learning-Based SSVEP-BCIs by the Reconstruction of Channel Correlation

Chengjuan Xie, Wenlong Ding, Kai Wang, Xun Chen et al.
7 citations
IEEE Transactions on Biomedical Engineering
Neural Networks and Applications
2.43
article

Enhancing Domain Diversity of Transfer Learning-Based SSVEP-BCIs by the Reconstruction of Channel Correlation

Chengjuan Xie, Wenlong Ding, Kai Wang, Xun Chen, Aiping Liu
article en
7 citations

Abstract

The RCC-enhanced transfer learning has the potential to facilitate the practical implementation of SSVEP-BCIs in real-world scenarios.

IEEE Transactions on Biomedical EngineeringVol. 72(2)
University of Science and Technology of China (CN), Anhui Medical University (CN), First Affiliated Hospital of Anhui Medical University (CN)
Openalex Percentile: Top 10%
Neural Networks and Applications
2.43
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