Leveraging Motor Unit Spatial Activation Patterns for Channel Selection in Finger Force Regression

Individual finger forces can be predicted by regression of high-density surface electromyography (sEMG) signals. This is promising for applications in human-machine interfaces, specifically prosthesis control, although the large number of electrodes imposes high computational requirements. In this study, we present strategies for a-priori channel selection guided by motor unit spatial activation patterns to reduce computational costs without compromising decoding accuracy. In contrast to subject-specific data-driven selection, we test the hypothesis that pre-selecting sEMG channels for finger-specific force estimation can still generalize across subjects. We show that a subset of 32 channels, out of a total of 256, achieves an RMSE of 6.32 ± 2.34 % of the Maximum Voluntary Contraction (MVC) on the HYSER RANDOM dataset, competitive with the state-of-the-art baseline model, using all channels, which attains an RMSE of 5.57 ± 1.94 % MVC. These results highlight the potential of simple, a-priori channel selection strategies in decoding finger forces from sEMG, which would be particularly suited for applications with limited computational resources.