Rethinking Self-Training Based Cross-Subject Domain Adaptation for SSVEP Classification

• URL: http://arxiv.org/abs/2601.21203v1
• Date: Thu, 29 Jan 2026 03:08:14 GMT
• Title: Rethinking Self-Training Based Cross-Subject Domain Adaptation for SSVEP Classification
• Authors: Weiguang Wang, Yong Liu, Yingjie Gao, Guangyuan Xu,
• Abstract summary: We propose a novel cross-subject domain adaptation method built upon the self-training paradigm.<n>Specifically, a Filter-Bank Euclidean Alignment (FBEA) strategy is designed to exploit frequency information from SSVEP filter banks.<n>Then, we propose a Cross-Subject Self-Training (CSST) framework consisting of two stages: Pre-Training with Adversarial Learning (PTAL), which aligns the source and target distributions, and Dual-Ensemble Self-Training (DEST), which refines pseudo-label quality.
• Score: 11.404309384526355
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Steady-state visually evoked potentials (SSVEP)-based brain-computer interfaces (BCIs) are widely used due to their high signal-to-noise ratio and user-friendliness. Accurate decoding of SSVEP signals is crucial for interpreting user intentions in BCI applications. However, signal variability across subjects and the costly user-specific annotation limit recognition performance. Therefore, we propose a novel cross-subject domain adaptation method built upon the self-training paradigm. Specifically, a Filter-Bank Euclidean Alignment (FBEA) strategy is designed to exploit frequency information from SSVEP filter banks. Then, we propose a Cross-Subject Self-Training (CSST) framework consisting of two stages: Pre-Training with Adversarial Learning (PTAL), which aligns the source and target distributions, and Dual-Ensemble Self-Training (DEST), which refines pseudo-label quality. Moreover, we introduce a Time-Frequency Augmented Contrastive Learning (TFA-CL) module to enhance feature discriminability across multiple augmented views. Extensive experiments on the Benchmark and BETA datasets demonstrate that our approach achieves state-of-the-art performance across varying signal lengths, highlighting its superiority.

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