在真实临床环境中用脑电图检测轻度认知障碍面临数据质量与分布差异挑战。
On the challenges of detecting MCI using EEG in the wild
- 对比实验室与普通诊所数据，发现训练数据小导致模型不可靠。
- 跨域数据分布差异大，模型泛化能力差，准确率下降明显。
- 健康人与患者脑电特征重叠，检测存在根本性局限，适合临床研究者参考。
近期研究显示，利用易获取的脑电图（EEG）数据可有效检测轻度认知障碍（MCI），有助于早期干预痴呆。然而此类系统在实际应用中的可靠性仍存疑。本文通过两个对比数据集——由神经科专家在受控环境下采集的CAUEEG，以及在普通诊所真实场景下收集的GENEEG——探讨了构建稳健MCI检测方法的挑战。研究发现，多数前期工作使用的小样本训练易导致模型方差大、预测过于自信，在实际中不可靠；同时，不同数据集间的分布偏移使得跨域泛化困难。此外，由于患者与对照组的脑电特征分布高度重叠，基于EEG的MCI检测可能面临根本性限制。研究呼吁在临床实用场景中加强高质量数据采集，以推动非侵入式MCI检测的发展。
原文摘要 · Abstract (English)
Recent studies have shown promising results in the detection of Mild Cognitive Impairment (MCI) using easily accessible Electroencephalogram (EEG) data which would help administer early and effective treatment for dementia patients. However, the reliability and practicality of such systems remains unclear. In this work, we investigate the potential limitations and challenges in developing a robust MCI detection method using two contrasting datasets: 1) CAUEEG, collected and annotated by expert neurologists in controlled settings and 2) GENEEG, a new dataset collected and annotated in general practice clinics, a setting where routine MCI diagnoses are typically made. We find that training on small datasets, as is done by most previous works, tends to produce high variance models that make overconfident predictions, and are unreliable in practice. Additionally, distribution shifts between datasets make cross-domain generalization challenging. Finally, we show that MCI detection using EEG may suffer from fundamental limitations because of the overlapping nature of feature distributions with control groups. We call for more effort in high-quality data collection in actionable settings (like general practice clinics) to make progress towards this salient goal of non-invasive MCI detection.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。