提出双焦点掩码注意力模型，提升脑机接口的解码准确率与跨被试鲁棒性。
SSVEP-BiMA: Bifocal Masking Attention Leveraging Native and Symmetric-Antisymmetric Components for Robust SSVEP Decoding
- 利用原信号与对称-反对称分量的双路特征融合，增强多视角特征学习。
- 在两个公开数据集上实现更高解码准确率与信息传输率（ITR）。
- 适合需要低延迟、高鲁棒性的跨被试脑机接口应用场景。
基于稳态视觉诱发电位（SSVEP）的脑机接口（BCI）因其简单性和高信息传输率（ITR）而广受欢迎。精确快速的SSVEP解码对可靠BCI性能至关重要。然而，传统解码方法需较长时间窗，深度学习模型通常需针对个体微调，在跨被试场景中难以达到最优性能。本文提出一种生物双焦点掩码注意力方法（SSVEP-BiMA），协同利用原始信号与对称-反对称分量进行解码。通过多信号表征，网络能从更广泛的样本视角整合特征，实现更泛化、全面的特征学习，从而提升预测准确率与鲁棒性。我们在两个公开数据集上进行了实验，结果表明所提方法在准确率与ITR方面均优于基线模型。我们认为该工作将推动更高效SSVEP基脑机接口系统的发展。
原文摘要 · Abstract (English)
Brain-computer interface (BCI) based on steady-state visual evoked potentials (SSVEP) is a popular paradigm for its simplicity and high information transfer rate (ITR). Accurate and fast SSVEP decoding is crucial for reliable BCI performance. However, conventional decoding methods demand longer time windows, and deep learning models typically require subject-specific fine-tuning, leaving challenges in achieving optimal performance in cross-subject settings. This paper proposed a biofocal masking attention-based method (SSVEP-BiMA) that synergistically leverages the native and symmetric-antisymmetric components for decoding SSVEP. By utilizing multiple signal representations, the network is able to integrate features from a wider range of sample perspectives, leading to more generalized and comprehensive feature learning, which enhances both prediction accuracy and robustness. We performed experiments on two public datasets, and the results demonstrate that our proposed method surpasses baseline approaches in both accuracy and ITR. We believe that this work will contribute to the development of more efficient SSVEP-based BCI systems.
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