AI 中文总结
研究针对脑电分析难题，提出波形包框架，通过学习波形模板字典及扩展表示，在三个数据集上测试，能在低数据量下以少参数实现与先进模型竞争的性能，且具有完全可解释性。
AI 中文摘要
脑电图（EEG）广泛用于诊断神经疾病，但其分析通常依赖于预定义频谱特征或深度神经网络。预定义特征有偏差，深度神经网络难以解释且需大量数据和计算。我们提出波形包，这是一个可解释框架，使用无标签的平移不变k均值学习一个由重复脑电波形模板（原子）组成的小字典。连续脑电被转换为原子令牌序列，其计数输入简单的下游分类器或聚类步骤。我们从两方面扩展此表示：添加原子到原子的转换（n-gram）以捕获时间结构，从单通道原子转换为多通道情况下的区域和跨通道空间原子。我们在三个互补数据集上测试该方法，在所有三个数据集上，波形包实现了与最先进的深度和基础模型相竞争的性能。它以较少的参数数量运行并提供完全可解释性，其主要优势在于能在低数据量情况下工作。
英文摘要
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.
CommentsIn Review
