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Cross-domain correlation analysis to improve SSVEP signals recognition in brain-computer interfaces
Kaiwei Hu1, Yong Wang1, Kaixiang Tu1
1School of Mechanical Engineering and Electronic Information, China University of Geosciences, Wuhan, People's Republic of China.
Biomedical Physics & Engineering Express
|December 3, 2025
Summary
This study introduces a new unsupervised transfer learning framework for brain-computer interfaces (BCI) that improves steady-state visual evoked potential (SSVEP) recognition without calibration. The method enhances accuracy and practical use for plug-and-play SSVEP-BCI systems.
Area of Science:
- Neuroscience
- Biomedical Engineering
- Machine Learning
Background:
- Steady-state visual evoked potential (SSVEP) recognition in brain-computer interface (BCI) systems faces challenges due to limited training data and inter-subject variability.
- Existing methods often require subject-specific calibration, limiting their practical application.
Purpose of the Study:
- To develop a novel unsupervised transfer learning framework for enhancing SSVEP recognition in BCI systems.
- To eliminate the need for subject-specific calibration, enabling plug-and-play functionality.
Main Methods:
- A three-stage pipeline involving similarity-aware subject selection, Euclidean alignment for domain shift mitigation, and hybrid feature extraction using Canonical Correlation Analysis (CCA) and Task-Related Component Analysis (TRCA).
- Integration of weighted correlation fusion for robust classification.
Main Results:
- Achieved state-of-the-art performance on Benchmark and BETA datasets with average accuracies of 83.20% and 69.08% at 1-second data length, respectively.
- Significantly outperformed existing methods such as ttCCA and Ensemble-DNN.
- Reached a maximum information transfer rate of 157.53 bits min-1.
Conclusions:
- The proposed unsupervised transfer learning framework effectively enhances SSVEP recognition without calibration.
- The method demonstrates significant practical potential for developing plug-and-play SSVEP-based BCI systems.
