提出可解释的脑电图分析模型，提升诊断可信度。
EEG-GMACN: Interpretable EEG Graph Mutual Attention Convolutional Network
- 引入反向图权重模块，输出可解释的电极重要性
- 通过互注意力机制识别关键电极，提升分类性能
- 支持预测置信度校准，适合临床与神经科学研究
脑电图（EEG）通过头皮电极记录大脑电活动，对理解神经系统疾病和开发脑机接口具有重要意义。图信号处理（GSP）通过考虑电极间的拓扑关系，成为EEG时空分析的有力方法。然而，现有研究缺乏对电极重要性的可解释性以及预测置信度的可信评估。本文提出EEG-GMACN模型，引入‘反向图权重模块’以生成可解释的电极图权重，增强分类结果的临床可信度与可解释性。同时，模型融合互注意力机制，提升对关键电极的区分能力，并引入置信度校准模块，评估预测结果的不确定性。该研究提升了EEG分析的透明性与有效性，推动其在临床与神经科学中的广泛应用。
原文摘要 · Abstract (English)
Electroencephalogram (EEG) is a valuable technique to record brain electrical activity through electrodes placed on the scalp. Analyzing EEG signals contributes to the understanding of neurological conditions and developing brain-computer interface. Graph Signal Processing (GSP) has emerged as a promising method for EEG spatial-temporal analysis, by further considering the topological relationships between electrodes. However, existing GSP studies lack interpretability of electrode importance and the credibility of prediction confidence. This work proposes an EEG Graph Mutual Attention Convolutional Network (EEG-GMACN), by introducing an 'Inverse Graph Weight Module' to output interpretable electrode graph weights, enhancing the clinical credibility and interpretability of EEG classification results. Additionally, we incorporate a mutual attention mechanism module into the model to improve its capability to distinguish critical electrodes and introduce credibility calibration to assess the uncertainty of prediction results. This study enhances the transparency and effectiveness of EEG analysis, paving the way for its widespread use in clinical and neuroscience research.
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