Comparison Between Machine Learning and Deep Learning on Multiple Motor Imagery Paradigms in a Low-Resource Context

Langlois Quentin, Jodogne Sébastien

2025

Abstract

Motor Imagery (MI) decoding is a task aimed at interpreting the mental imagination of movement without any physical action. MI decoding is typically performed through automated analysis of electroencephalographic (EEG) signals, which capture electrical activity of the brain via electrodes placed on the scalp. MI decoding holds significant potential for controlling devices or assisting in patient rehabilitation. In recent years, Deep Learning (DL) techniques have been extensively studied in the MI decoding domain, often outperforming traditional Machine Learning (ML) methods. However, these DL models are known to require large amounts of data to achieve good results and substantial computational resources, limiting their applicability in low-data or low-resource contexts. This work explores these assumptions by comparing state-of-the-art ML and DL models under simulated low-resource conditions. Experiments were conducted on the Kaya2018 dataset, enabling this comparison across multiple MI paradigms, which contrasts with other studies that typically focus only on left/right-hand decoding task. The results indicate that even with limited data, DL models consistently outperform ML techniques across all evaluated MI tasks, with the most significant advantage observed in advanced experimental setups.

Paper Citation

in Harvard Style

Quentin L. and Sébastien J. (2025). Comparison Between Machine Learning and Deep Learning on Multiple Motor Imagery Paradigms in a Low-Resource Context. In Proceedings of the 18th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 1: BIOSIGNALS; ISBN 978-989-758-731-3, SciTePress, pages 704-715. DOI: 10.5220/0013077600003911

in Bibtex Style

@conference{biosignals25,
author={Langlois Quentin and Jodogne Sébastien},
title={Comparison Between Machine Learning and Deep Learning on Multiple Motor Imagery Paradigms in a Low-Resource Context},
booktitle={Proceedings of the 18th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 1: BIOSIGNALS},
year={2025},
pages={704-715},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013077600003911},
isbn={978-989-758-731-3},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 18th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 1: BIOSIGNALS
TI - Comparison Between Machine Learning and Deep Learning on Multiple Motor Imagery Paradigms in a Low-Resource Context
SN - 978-989-758-731-3
AU - Quentin L.
AU - Sébastien J.
PY - 2025
SP - 704
EP - 715
DO - 10.5220/0013077600003911
PB - SciTePress