这篇论文用了一种叫bag-of-waves的巧办法，只需少量数据就能从脑电信号里学到可解释的波形模板，比那些黑箱深度模型好懂，还省钱省算力。
论文提出bag-of-waves框架，利用移位不变k-means无监督学习EEG波形模板（原子），将连续信号转为原子序列计数进行分类。方法扩展了原子间转移n-gram和跨通道空间原子。在三个数据集测试：仅16只小鼠的单通道基因型聚类、静息态痴呆分类、以及TUEV临床EEG六分类基准。性能与深度/基础模型相当，但参数量极少且完全可解释——每个原子对应可检验的波形形态，神经生理学家可验证已知临床特征。
Interpretable EEG biomarkers with bag-of-waves: Spatial and temporal waveform dictionaries for low-data regimes
Electroencephalography (EEG) is widely used to diagnose neurological conditions, but its analysis usually relies on either predefined spectral features or deep neural networks. Predefined features carry a strong bias, since they fix in advance what counts as informative, while deep neural networks and foundation models are hard to interpret and need large amounts of data and compute. We present bag-of-waves, an interpretable framework that learns a small dictionary of recurring EEG waveform templates, called atoms, using shift-invariant k-means without labels. The continuous EEG is then turned into a sequence of atom tokens, whose counts feed a simple downstream classifier or clustering step. We extend this representation in two ways: we add atom-to-atom transitions, which we call n- grams, to capture temporal structure, and we move from single-channel atoms to regional and cross-channel spatial atoms for the multichannel case. We test the method on three complementary datasets, each probing a different aspect: single-channel mouse genotype clustering with only sixteen animals (the low-data and temporal case), resting-state dementia classification (the spatial case), and the TUEV benchmark, a six-way classification of clinical EEG events (a high-data comparison against strong deep and foundation baselines). Across all three datasets, bag-of-waves achieves performance competitive with state-of-the-art deep and foundation models. Yet, it operates with a fraction of the parameter count and provides full interpretability: because every atom corresponds to an inspectable waveform, the method explicitly recovers known clinical morphologies that a neurophysiologist can directly validate. Its main advantage is that it works in the low-data regime where heavier models are a poor fit.