PSDNorm: Test-Time Temporal Normalization for Deep Learning on EEG Signals

• URL: http://arxiv.org/abs/2503.04582v1
• Date: Thu, 06 Mar 2025 16:20:25 GMT
• Title: PSDNorm: Test-Time Temporal Normalization for Deep Learning on EEG Signals
• Authors: Théo Gnassounou, Antoine Collas, Rémi Flamary, Alexandre Gramfort,
• Abstract summary: PSDNorm is a layer that leverages Monge mapping and temporal context to normalize feature maps in deep learning models.<n> PSDNorm achieves state-of-the-art performance at test time on datasets not seen during training.<n> PSDNorm provides a significant improvement in robustness, achieving markedly higher F1 scores for the 20% hardest subjects.
• Score: 63.05435596565677
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Distribution shift poses a significant challenge in machine learning, particularly in biomedical applications such as EEG signals collected across different subjects, institutions, and recording devices. While existing normalization layers, Batch-Norm, LayerNorm and InstanceNorm, help address distribution shifts, they fail to capture the temporal dependencies inherent in temporal signals. In this paper, we propose PSDNorm, a layer that leverages Monge mapping and temporal context to normalize feature maps in deep learning models. Notably, the proposed method operates as a test-time domain adaptation technique, addressing distribution shifts without additional training. Evaluations on 10 sleep staging datasets using the U-Time model demonstrate that PSDNorm achieves state-of-the-art performance at test time on datasets not seen during training while being 4x more data-efficient than the best baseline. Additionally, PSDNorm provides a significant improvement in robustness, achieving markedly higher F1 scores for the 20% hardest subjects.

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