比较传统与深度学习模型在脑机接口中的不确定性估计能力
Uncertainty Quantification for Motor Imagery BCI -- Machine Learning vs. Deep Learning
- 用经典分类器与深度学习方法对比不确定性量化效果
- 传统方法不过度自信，深度模型普遍过自信，可调优解决
- 适合关注可靠性与可解释性的脑机接口研究者
脑机接口（BCI）将脑电信号转化为有用输出，但并非始终准确。优秀机器学习分类器应能对分类结果给出置信度概率。标准运动想象BCI分类器虽提供概率，但不确定性量化研究长期局限于深度学习。本文对比了常用BCI方法（CSP-LDA、MDRM）与深度学习专用方法（Deep Ensembles、Direct Uncertainty Quantification）及标准CNN的不确定性量化能力。发现深度学习普遍存在过自信现象，而CSP-LDA和MDRM表现更稳定；其中MDRM存在欠自信问题，通过引入温度缩放（MDRM-T）得到改善。尽管深度集成和标准CNN分类性能最优，但CSP-LDA与MDRM-T在不确定性估计上表现最佳。所有模型均能区分易判与难判样本，可通过拒绝模糊样本提升整体准确率。
原文摘要 · Abstract (English)
Brain-computer interfaces (BCIs) turn brain signals into functionally useful output, but they are not always accurate. A good Machine Learning classifier should be able to indicate how confident it is about a given classification, by giving a probability for its classification. Standard classifiers for Motor Imagery BCIs do give such probabilities, but research on uncertainty quantification has been limited to Deep Learning. We compare the uncertainty quantification ability of established BCI classifiers using Common Spatial Patterns (CSP-LDA) and Riemannian Geometry (MDRM) to specialized methods in Deep Learning (Deep Ensembles and Direct Uncertainty Quantification) as well as standard Convolutional Neural Networks (CNNs). We found that the overconfidence typically seen in Deep Learning is not a problem in CSP-LDA and MDRM. We found that MDRM is underconfident, which we solved by adding Temperature Scaling (MDRM-T). CSP-LDA and MDRM-T give the best uncertainty estimates, but Deep Ensembles and standard CNNs give the best classifications. We show that all models are able to separate between easy and difficult estimates, so that we can increase the accuracy of a Motor Imagery BCI by rejecting samples that are ambiguous.
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