用图神经网络分析脑电数据，参数少却效果好。
Lightweight Transformer for EEG Classification via Balanced Signed Graph Algorithm Unrolling
- 将脑电信号建模为带负边的平衡图，通过谱滤波去噪
- 在真实数据上达到与深度模型相当的分类准确率
- 模型轻量可解释，适合医疗信号处理场景
通过脑电传感器采集的脑信号样本具有固有的反相关性，可用有限图中的负边有效建模。为区分癫痫患者与健康人，本文基于平衡符号图（无奇数个负边的环）上的谱去噪算法，构建轻量且可解释的类Transformer神经网络。平衡符号图具备明确定义的频率，可通过图拉普拉斯矩阵的相似变换映射到正图。利用Lanczos近似在映射后的正图上高效实现理想低通滤波，最优截断频率由数据学习得到。由于两个平衡符号图去噪器在训练中分别学习两类信号的后验概率，我们通过评估其重构误差进行二分类。实验表明，该方法性能媲美主流深度学习模型，同时参数量大幅减少。
原文摘要 · Abstract (English)
Samples of brain signals collected by EEG sensors have inherent anti-correlations that are well modeled by negative edges in a finite graph. To differentiate epilepsy patients from healthy subjects using collected EEG signals, we build lightweight and interpretable transformer-like neural nets by unrolling a spectral denoising algorithm for signals on a balanced signed graph -- graph with no cycles of odd number of negative edges. A balanced signed graph has well-defined frequencies that map to a corresponding positive graph via similarity transform of the graph Laplacian matrices. We implement an ideal low-pass filter efficiently on the mapped positive graph via Lanczos approximation, where the optimal cutoff frequency is learned from data. Given that two balanced signed graph denoisers learn posterior probabilities of two different signal classes during training, we evaluate their reconstruction errors for binary classification of EEG signals. Experiments show that our method achieves classification performance comparable to representative deep learning schemes, while employing dramatically fewer parameters.
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