Motion Intention Decoding: The Role of Data Parameters in Motor Unit-Based Decoders

Accurate decoding of human motion intention from surface electromyography (sEMG) signals recorded non-invasively from the skin surface is critical for enabling intuitive control in assistive robotics and human-machine interactions. With the advancement of high-density sEMG (HD-sEMG), neural decoding methods based on motor unit (MU) activity have shown promise due to their potential to capture finely controlled movement information. However, the effects of data segmentation parameters on the decomposition and decoding accuracy remain underexplored. In this study, we systematically investigated how the segmentation length and data size of sEMG signals used for decomposition affect the performance of finger force decoding. Specifically, HD-sEMG signals were recorded from eight human participants during single- and multi-finger isometric force tasks. A neural decoding pipeline was developed for finger force predictions. We first evaluated the impact of four segmentation window lengths (10 s, 20 s, 40 s, and 80 s) on decoding accuracy, and found that a 20-second window was sufficient to ensure accurate decoding, with no additional benefit from using longer segments. Using this setting, we further examined the effect of training data size by comparing decoders trained with different data sizes. Our results showed that using the full training dataset significantly improved decoding performance compared to using only half of the training dataset. These findings offer practical guidelines for optimizing data usage in MU-based motion intention decoding systems.