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

Quentin Langlois, Sébastien Jodogne

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 multipl

External IDs:dblp:conf/biostec/LangloisJ25