MoEMba: A Mamba-based Mixture of Experts for High-Density EMG-based Hand Gesture Recognition

MoEMba: A Mamba-based Mixture of Experts for High-Density EMG-based Hand Gesture Recognition Mehran Shabanpour, Kasra Rad, Sadaf Khademi, and Arash Mohammadi Abstract -- High-Density surface Electromyography (HD-sEMG) has emerged as a pivotal resource for Human-Computer Interaction (HCI), offering direct insights into muscle activities and motion intentions. However, a significant challenge in practical implementations of HD-sEMG-based models is the low accuracy of inter-session and inter-subject classification. V ariability between sessions can reach up to 40% due to the inherent temporal variability of HD-sEMG signals. T argeting this challenge, the paper introduces the MoEMba framework, a novel approach leveraging Selective State-Space Models (SSMs) to enhance HD-sEMG-based gesture recognition. Furthermore, wavelet feature modulation is integrated to capture multi-scale temporal and spatial relations, improving signal representation. Experimental results on the CapgMyo HD-sEMG dataset demonstrate that MoEMba achieves a balanced accuracy of 56 .9% The proposed framework's robustness to session-to-session variability and its efficient handling of high-dimensional multivariate time series data highlight its potential for advancing HD-sEMG-powered HCI systems.