@inproceedings{
ma2024learning,
title={Learning Cortico-Muscular Dependence through Orthonormal Decomposition of Density Ratios},
author={Shihan Ma and Bo Hu and Tianyu Jia and Alexander Kenneth Clarke and Blanka Zicher and Arnault H. Caillet and Dario Farina and Jose C Principe},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=wdGvRud1LS}
}
The cortico-spinal neural pathway is fundamental for motor control and movement execution, and in humans it is typically studied using concurrent electroencephalography (EEG) and electromyography (EMG) recordings. However, current approaches for capturing high-level and contextual connectivity between these recordings have important limitations. Here, we present a novel application of statistical dependence estimators based on orthonormal decomposition of density ratios to model the relationship between cortical and muscle oscillations. Our method extends from traditional scalar-valued measures by learning eigenvalues, eigenfunctions, and projection spaces of density ratios from realizations of the signal, addressing the interpretability, scalability, and local temporal dependence of cortico-muscular connectivity. We experimentally demonstrate that eigenfunctions learned from cortico-muscular connectivity can accurately classify movements and subjects. Moreover, they reveal channel and temporal dependencies that confirm the activation of specific EEG channels during movement.
Learning Cortico-Muscular Dependence through Orthonormal Decomposition of Density Ratios
Shihan Ma Bo Hu Tianyu Jia Alexander Kenneth Clarke Blanka Zicher Arnault H. Caillet Dario Farina Jose C Principe