In many machine learning applications on signals and biomedical data, especially electroencephalogram (EEG), one major challenge is the variability of the data across subjects, sessions, and hardware devices. In this work, we propose a new method called Convolutional Monge Mapping Normalization (CMMN), which consists in filtering the signals in order to adapt their power spectrum density (PSD) to a Wasserstein barycenter estimated on training data. CMMN relies on novel closed-form solutions for optimal transport mappings and barycenters and provides individual test time adaptation to new data without needing to retrain a prediction model. Numerical experiments on sleep EEG data show that CMMN leads to significant and consistent performance gains independent from the neural network architecture when adapting between subjects, sessions, and even datasets collected with different hardware. Notably our performance gain is on par with much more numerically intensive Domain Adaptation (DA) methods and can be used in conjunction with those for even better performances.

本文提出了一种新的方法，名为卷积蒙日映射归一化（Convolutional Monge Mapping Normalization，CMMN），用于适应不同主体、会话和硬件设备之间的信号变异性并提高机器学习应用的性能表现。实验结果表明，CMMN方法在睡眠EEG数据上的表现与数值计算密集的领域自适应(Domain Adaptation, DA)方法基本相当，并且可以与DA方法一起使用以获得更好的性能表现。

基于卷积蒙特映射规范化的生物信号学习