Treffer: Dual-channel TRCA-net based on cross-subject positive transfer for SSVEP-BCI.

Title:
Dual-channel TRCA-net based on cross-subject positive transfer for SSVEP-BCI.
Authors:
Xiong H; School of Control Science and Engineering, Tiangong University, Tianjin, People's Republic of China.; Tianjin Key Laboratory of Intelligent Control of Electrical Equipment, Tiangong University, Tianjin, People's Republic of China., Chang S; School of Control Science and Engineering, Tiangong University, Tianjin, People's Republic of China.; Tianjin Key Laboratory of Intelligent Control of Electrical Equipment, Tiangong University, Tianjin, People's Republic of China., Liu J; School of Control Science and Engineering, Tiangong University, Tianjin, People's Republic of China.; Tianjin Key Laboratory of Intelligent Control of Electrical Equipment, Tiangong University, Tianjin, People's Republic of China.
Source:
Biomedical physics & engineering express [Biomed Phys Eng Express] 2025 Dec 18; Vol. 12 (1). Date of Electronic Publication: 2025 Dec 18.
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, Humans ; Algorithms ; Deep Learning ; Neural Networks, Computer ; Signal Processing, Computer-Assisted ; Male ; Adult ; Female
Contributed Indexing:
Keywords: brain–computer interface (BCI); convolutional neural network; cross-subject transfer learning; steady-state visual evoked potential
Entry Date(s):
Date Created: 20251208 Date Completed: 20251218 Latest Revision: 20251218
Update Code:
20251219
DOI:
10.1088/2057-1976/ae291c
PMID:
41360014
Database:
MEDLINE

Weitere Informationen

Objective . To enhance the decoding accuracy and information transfer rate of steady-state visual evoked potential-based brain-computer interface (SSVEP-BCI) systems and to reduce inter-subject variability for broader SSVEP-BCI applications, a dual-channel TRCA-net (DC-TRCA-net) method is proposed, based on cross-subject positive transfer. The proposed method incorporates an innovative Transfer-Accuracy-based Subject Selection (T-ASS) strategy and a deep learning network integrated with the SSVEP Domain Adaptation Network (SSVEP-DAN) to enhance SSVEP-BCI decoding performance. The T-ASS strategy constructs contribution scores by computing each subject's self-accuracy and transfer accuracy, and enables effective source subject selection while mitigating negative transfer risks. DC-TRCA-net is further developed to improve model generalization through cross-subject data augmentation. The effectiveness of the proposed method is validated on two large-scale public benchmark datasets. Experimental results demonstrate that DC-TRCA-net outperforms existing networks across both datasets, with particularly substantial performance gains observed in complex experimental scenarios.
(© 2025 IOP Publishing Ltd. All rights, including for text and data mining, AI training, and similar technologies, are reserved.)