Investigating Feedback-Informed Screen-Guided Training to Enhance Myoelectric Control and Predictability

Screen-guided training is a widely used method for calibrating myoelectric prostheses, wherein users follow visual prompts. However, this approach often fails to capture the complexities of real-world usage when the user is actively engaging with the controller. This study, therefore, aimed to develop an alternative training protocol that promotes more robust pattern recognition-based myoelectric control. In an experiment with 20 participants, we compared three training methods: conventional screen-guided training without feedback, real-time visual feedback of principal component analysis (PCA)-based projections of EMG activity, and real-time classification feedback with intentionally corrupted classifier outputs. After training, participants completed a Fitts' law-style target acquisition task in a virtual environment, repeating it at three different difficulty levels. We then evaluated how offline accuracy and metrics, particularly Bhattacharyya Distances computed from combinations of the PCA projections, correlated with online control performance. Our findings indicate that training with feedback yielded the best performance, with PCA-based visual feedback providing the most effective calibration environment. Additionally, projecting the EMG data collected with PCA-based feedback into the PCA space derived from the no-feedback data improved the correlation between offline separability metrics and the online Fitts' Law throughput. Interestingly, this correlation was stronger for the easy difficulty level. Nevertheless, the benefits of PCA-based feedback were consistent across the three different difficulty levels of the Fitts' law task, it as a beneficial and robust approach worthy of further exploration.