通过融合谱域与时域特征，提升脑机接口跨被试泛化能力。
ASPEN: Spectral-Temporal Fusion for Cross-Subject Brain Decoding
- 用乘法融合方式结合频谱与波形特征，要求双模态一致才传递信息。
- 在六个数据集上三组达到最优跨被试准确率，其余表现竞争力。
- 适合需要高泛化性能的脑电跨被试解码场景。
基于脑电的脑机接口在跨被试泛化方面仍面临挑战，主要源于神经信号的个体差异。我们研究了频谱表示是否比时域波形提供更稳定的跨被试特征。通过对三种脑电范式（SSVEP、P300、运动想象）的相关性分析发现，频谱特征在跨被试间表现出更高的相似性。受此启发，我们提出ASPEN，一种通过乘法融合结合频谱与时域特征流的混合架构，要求双模态一致才能使特征传播。在六个基准数据集上的实验表明，ASPEN能根据范式动态实现最优的频谱-时域平衡。在六个数据集中的三个上达到最佳未见被试准确率，在其余数据集上也表现出竞争力，证明乘法多模态融合可有效实现跨被试泛化。
原文摘要 · Abstract (English)
Cross-subject generalization in EEG-based brain-computer interfaces (BCIs) remains challenging due to individual variability in neural signals. We investigate whether spectral representations offer more stable features for cross-subject transfer than temporal waveforms. Through correlation analyses across three EEG paradigms (SSVEP, P300, and Motor Imagery), we find that spectral features exhibit consistently higher cross-subject similarity than temporal signals. Motivated by this observation, we introduce ASPEN, a hybrid architecture that combines spectral and temporal feature streams via multiplicative fusion, requiring cross-modal agreement for features to propagate. Experiments across six benchmark datasets reveal that ASPEN is able to dynamically achieve the optimal spectral-temporal balance depending on the paradigm. ASPEN achieves the best unseen-subject accuracy on three of six datasets and competitive performance on others, demonstrating that multiplicative multimodal fusion enables effective cross-subject generalization.
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