Effect of Segmentation Parameters on Classification Accuracy of High-Density EMG recordings

Electromyography (EMG) based control systems rely on the accurate identification of patterns extracted from signal features to predict the corresponding movement. The selection of segmentation window parameters and their impact on overall accuracy of classifiers has been previously studied for systems with a low number of EMG channels (<; 16). In this study a High-density EMG electrode array was used to evaluate the impact of the parameters when a high number of channels (128) was recorded. Findings show that in combination with high channel counts the impact of window length and overlap were marginal (<; 2% and <; 1% respectively). The number of channels was found to have direct correlation with achieved accuracy, with an improvement of up to 19.5 ± 4.5% in classification accuracy when increasing from 4 to 128 channels.