Convolutional Monge Mapping between EEG Datasets to Support Independent Component Labeling

• URL: http://arxiv.org/abs/2509.01721v1
• Date: Mon, 01 Sep 2025 19:01:14 GMT
• Title: Convolutional Monge Mapping between EEG Datasets to Support Independent Component Labeling
• Authors: Austin Meek, Carlos H. Mendoza-Cardenas, Austin J. Brockmeier,
• Abstract summary: EEG recordings contain rich information about neural activity.<n>They are subject to artifacts, noise, and superficial differences due to sensors, amplifiers, and filtering.<n>Independent component analysis and automatic labeling of independent components (ICs) enable artifact removal in EEG pipelines.
• Score: 0.45880283710344066
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: EEG recordings contain rich information about neural activity but are subject to artifacts, noise, and superficial differences due to sensors, amplifiers, and filtering. Independent component analysis and automatic labeling of independent components (ICs) enable artifact removal in EEG pipelines. Convolutional Monge Mapping Normalization (CMMN) is a recent tool used to achieve spectral conformity of EEG signals, which was shown to improve deep neural network approaches for sleep staging. Here we propose a novel extension of the CMMN method with two alternative approaches to computing the source reference spectrum the target signals are mapped to: (1) channel-averaged and $l_1$-normalized barycenter, and (2) a subject-to-subject mapping that finds the source subject with the closest spectrum to the target subject. Notably, our extension yields space-time separable filters that can be used to map between datasets with different numbers of EEG channels. We apply these filters in an IC classification task, and show significant improvement in recognizing brain versus non-brain ICs. Clinical relevance - EEG recordings are used in the diagnosis and monitoring of multiple neuropathologies, including epilepsy and psychosis. While EEG analysis can benefit from automating artifact removal through independent component analysis and labeling, differences in recording equipment and context (the presence of noise from electrical wiring and other devices) may impact the performance of machine learning models, but these differences can be minimized by appropriate spectral normalization through filtering.

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