数据量大但多样，比小而一致的数据更利于提升脑电病理检测模型性能。
Quantity versus Diversity: Influence of Data on Detecting EEG Pathology with Advanced ML Models
- 用大规模多源数据训练，提升模型泛化能力。
- 数据多样性差导致模型性能显著下降，但样本量可部分弥补缺陷。
- 结合注意力机制与梯度提升的融合模型表现最优，适合临床部署。
本研究探讨了数据数量与多样性对多种机器学习模型在检测通用脑电图（EEG）病理方面的影响。我们使用了来自坦普尔大学医院的2,993个脑电记录和来自Elmiko Biosignals sp. z o.o.的55,787个记录，后者涵盖39家医院、患者条件多样，因此我们提出了目前最大的公开可用脑电图语料库——Elmiko数据集。结果表明，小而一致的数据集可使多种模型达到高准确率；然而，病理状况、记录协议和标注标准的差异会导致性能显著下降。尽管如此，增加数据量能提升预测准确性，甚至可部分抵消多样性不足的影响，尤其在基于注意力机制或变压器架构的神经网络中。一种结合这些网络与手工特征梯度提升的元模型，在不同数据集上均表现出色。
原文摘要 · Abstract (English)
This study investigates the impact of quantity and diversity of data on the performance of various machine-learning models for detecting general EEG pathology. We utilized an EEG dataset of 2,993 recordings from Temple University Hospital and a dataset of 55,787 recordings from Elmiko Biosignals sp. z o.o. The latter contains data from 39 hospitals and a diverse patient set with varied conditions. Thus, we introduce the Elmiko dataset - the largest publicly available EEG corpus. Our findings show that small and consistent datasets enable a wide range of models to achieve high accuracy; however, variations in pathological conditions, recording protocols, and labeling standards lead to significant performance degradation. Nonetheless, increasing the number of available recordings improves predictive accuracy and may even compensate for data diversity, particularly in neural networks based on attention mechanism or transformer architecture. A meta-model that combined these networks with a gradient-boosting approach using handcrafted features demonstrated superior performance across varied datasets.
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