用Transformer自动选关键脑电通道，提升脑机接口精度。
EEGChaT: A Transformer-Based Modular Channel Selector for SEEG Analysis
- 引入通道聚合令牌，用注意力机制整合多通道信息。
- 在DuIN数据集上提升分类准确率最高达17%绝对增长。
- 输出可解释的通道重要性分数，适合神经科学与临床研究。
脑机接口和神经科学研究中，立体脑电（SEEG）信号分析至关重要，但因其通道数量多且相关性异质，面临巨大挑战。传统通道选择方法难以扩展或提供有意义的可解释性。本文提出EEGChaT，一种基于Transformer的模块化通道选择器，可自动识别SEEG记录中与任务最相关的通道。EEGChaT引入通道聚合令牌（CATs）以聚合跨通道信息，并采用改进的Attention Rollout技术计算可解释、量化的通道重要性得分。我们在DuIN数据集上评估了EEGChaT，结果表明将其集成到现有分类模型中能持续提升解码准确率，最高实现17%的绝对增益。此外，EEGChaT生成的通道权重与人工选定通道有显著重叠，验证了方法的可解释性。结果表明，EEGChaT是高维SEEG分析中有效且通用的通道选择方案，兼具性能提升与神经信号相关性洞察。
原文摘要 · Abstract (English)
Analyzing stereoelectroencephalography (SEEG) signals is critical for brain-computer interface (BCI) applications and neuroscience research, yet poses significant challenges due to the large number of input channels and their heterogeneous relevance. Traditional channel selection methods struggle to scale or provide meaningful interpretability for SEEG data. In this work, we propose EEGChaT, a novel Transformer-based channel selection module designed to automatically identify the most task-relevant channels in SEEG recordings. EEGChaT introduces Channel Aggregation Tokens (CATs) to aggregate information across channels, and leverages an improved Attention Rollout technique to compute interpretable, quantitative channel importance scores. We evaluate EEGChaT on the DuIN dataset, demonstrating that integrating EEGChaT with existing classification models consistently improves decoding accuracy, achieving up to 17\% absolute gains. Furthermore, the channel weights produced by EEGChaT show substantial overlap with manually selected channels, supporting the interpretability of the approach. Our results suggest that EEGChaT is an effective and generalizable solution for channel selection in high-dimensional SEEG analysis, offering both enhanced performance and insights into neural signal relevance.
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