通过自训练提升跨被试SSVEP识别准确率
Rethinking Self-Training Based Cross-Subject Domain Adaptation for SSVEP Classification
- 设计频带欧氏对齐策略，利用滤波器组频率信息
- 在两个数据集上实现当前最优性能，适应不同信号长度
- 适合需要低标注成本的脑机接口应用开发者
基于稳态视觉诱发电位（SSVEP）的脑机接口因信噪比高、易用性强而广泛应用。准确解码SSVEP信号对解析用户意图至关重要，但跨被试信号差异大且用户特定标注成本高，制约了识别性能。为此，本文提出一种基于自训练范式的新型跨被试域适应方法。首先设计滤波器组欧氏对齐（FBEA）策略，挖掘SSVEP滤波器组中的频率信息；随后提出双阶段跨被试自训练（CSST）框架：预训练阶段采用对抗学习（PTAL）对齐源域与目标域分布，自训练阶段通过双重集成（DEST）优化伪标签质量；此外引入时频增强对比学习（TFA-CL）模块，提升多视图特征判别性。在Benchmark和BETA数据集上的大量实验表明，该方法在不同信号长度下均达到当前最优表现，验证了其优越性。
原文摘要 · Abstract (English)
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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