用频谱特征提升脑电诊断，比注意力机制更有效。
Spectral Priors vs. Attention: Investigating the Utility of Attention Mechanisms in EEG-Based Diagnosis
- 从脑电波频段提取特征，替代原始数据建模。
- 传统模型在小数据集上表现超越顶尖深度学习模型。
- 注意力机制难以捕捉稳定神经信号，适合研究者参考。
脑电图（EEG）时间序列信号噪声大、空间分辨率低，导致神经退行性疾病分类困难。即使最先进的深度学习模型也难以区分健康人与患者，或不同疾病类型，因组间相似性高。本文表明，基于频谱选择的特征构建能显著增强类别可分性。通过提取主要脑波频段的信号强度，将高维原始数据转化为高价值频谱特征。实验结果表明：在小样本数据集上，频域与时频域特征使传统机器学习模型性能达到甚至超过当前最优深度学习模型；注意力机制无法提炼出健康神经活动的稳定特征，无论在静息态还是任务态脑电中均表现不佳；注意力模型在识别相关频谱特征上的局限性具有鲁棒性，即使提供频谱选择的时域输入也无法明显改善其表现。研究在三个公开的静息态脑电数据集和一个任务态脑电数据集上验证了方法的有效性，提供了坚实的实证依据。
原文摘要 · Abstract (English)
Electroencephalograph (EEG) timeseries signals are characterized by significant noise and coarse spatial resolution, which complicates the classification of neurodegenerative diseases. Even SOTA deep learning architectures struggle to distinguish between healthy controls and diseased subjects, or between different disease types, due to high intergroup similarity. In this paper, we show that a spectrally selective approach to feature construction enhances class separability. By isolating signal strengths within the primary brainwave bands, we transform high dimensional raw data into high value spectral features. Our results demonstrate that in small datasets a) features derived from frequency and time frequency domain allow traditional machine learning models to match or exceed the performance of SOTA deep learning models, b) Attention mechanism is unable to distill the stable feature signatures that characterize healthy neural activity in both resting and task EEGs, and c) the limitations of attention based models in finding relevant spectral features appear to be robust in that providing frequency selective time domain input do not appreciably improve their performance. We validate our methodology across three open source resting EEG datasets and one task EEG dataset, providing robust empirical evidence for our claims.
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