Convolutional Monge Mapping Normalization for learning on sleep data
This addresses the problem of domain adaptation in biomedical signal processing for researchers and practitioners, offering a computationally efficient alternative to intensive methods.
The paper tackles the challenge of data variability across subjects, sessions, and hardware in EEG signal analysis by proposing Convolutional Monge Mapping Normalization (CMMN), which adapts power spectrum density to a Wasserstein barycenter, resulting in significant and consistent performance gains independent of neural network architecture.
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.