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Treffer: Cross-domain correlation analysis to improve SSVEP signals recognition in brain-computer interfaces.

Title:
Cross-domain correlation analysis to improve SSVEP signals recognition in brain-computer interfaces.
Authors:
Hu K; School of Mechanical Engineering and Electronic Information, China University of Geosciences, Wuhan, People's Republic of China., Wang Y; School of Mechanical Engineering and Electronic Information, China University of Geosciences, Wuhan, People's Republic of China., Tu K; School of Mechanical Engineering and Electronic Information, China University of Geosciences, Wuhan, People's Republic of China., Guo H; School of Mechanical Engineering and Electronic Information, China University of Geosciences, Wuhan, People's Republic of China., Yan J; School of Mechanical Engineering and Electronic Information, China University of Geosciences, Wuhan, People's Republic of China.
Source:
Biomedical physics & engineering express [Biomed Phys Eng Express] 2025 Dec 15; Vol. 12 (1). Date of Electronic Publication: 2025 Dec 15.
Publication Type:
Journal Article
Language:
English
Journal Info:
Publisher: IOP Publishing Ltd Country of Publication: England NLM ID: 101675002 Publication Model: Electronic Cited Medium: Internet ISSN: 2057-1976 (Electronic) Linking ISSN: 20571976 NLM ISO Abbreviation: Biomed Phys Eng Express Subsets: MEDLINE
Imprint Name(s):
Original Publication: Bristol : IOP Publishing Ltd., [2015]-
MeSH Terms:
Brain-Computer Interfaces* , Evoked Potentials, Visual*/physiology , Electroencephalography*/methods , Signal Processing, Computer-Assisted* , Brain*/physiology, Humans ; Algorithms ; Signal-To-Noise Ratio ; Adult ; Male ; Female
Contributed Indexing:
Keywords: brain-computer interface; correlation analysis; recognition algorithm; steady-state visual evoked potential
Entry Date(s):
Date Created: 20251203 Date Completed: 20251215 Latest Revision: 20251215
Update Code:
20251215
DOI:
10.1088/2057-1976/ae2772
PMID:
41335119
Database:
MEDLINE

Weitere Informationen

The recognition of steady-state visual evoked potential (SSVEP) signals in brain-computer interface (BCI) systems is challenging due to the lack of training data and significant inter-subject variability. To address this, we propose a novel unsupervised transfer learning framework that enhances SSVEP recognition without requiring any subject-specific calibration. Our method employs a three-stage pipeline: (1) preprocessing with similarity-aware subject selection and Euclidean alignment to mitigate domain shifts; (2) hybrid feature extraction combining canonical correlation analysis (CCA) and task-related component analysis (TRCA) to enhance signal-to-noise ratio and phase sensitivity; and (3) weighted correlation fusion for robust classification. Extensive evaluations on the Benchmark and BETA datasets demonstrate that our approach achieves state-of-the-art performance, with average accuracies of 83.20% and 69.08% at 1 s data length, respectively-significantly outperforming existing methods like ttCCA and Ensemble-DNN. The highest information transfer rate reaches 157.53 bits min <sup>-1</sup> , underscoring the framework's practical potential for plug-and-play SSVEP-based BCIs.
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