Convolutional Monge Mapping Normalization for learning on sleep data
- URL: http://arxiv.org/abs/2305.18831v3
- Date: Mon, 13 Nov 2023 14:53:01 GMT
- Title: Convolutional Monge Mapping Normalization for learning on sleep data
- Authors: Th\'eo Gnassounou, R\'emi Flamary, Alexandre Gramfort
- Abstract summary: We propose a new method called Convolutional Monge Mapping Normalization (CMMN)
CMMN consists in filtering the signals in order to adapt their power spectrum density (PSD) to a Wasserstein barycenter estimated on training data.
Numerical experiments on sleep EEG data show that CMMN leads to significant and consistent performance gains independent from the neural network architecture.
- Score: 63.22081662149488
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: 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.
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