Extraction of muscle synergies from electromyography (EMG) recordings relies on the analysis of multi-trial muscle activation data. To identify the underlying modular structure, dimensionality reduction algorithms are usually applied to the EMG signals. This process requires a rigid alignment of muscle activity across trials that is typically achieved by the normalization of the length of each trial. However, this time-normalization ignores important temporal variability that is present on single trials as result of neuromechanical processes or task demands. To overcome this limitation, we propose a novel method that simultaneously aligns muscle activity data and extracts spatial and temporal muscle synergies. This approach relies on an unsupervised learning algorithm that extends our previously developed space-by-time decomposition to incorporate the identification of linear time warps for individual trials. We apply the proposed method to high-dimensional spatiotemporal EMG data recorded during performance of whole-body reaching movements and show that it identifies meaningful spatial and temporal structure in muscle activity despite differences in trial lengths. We suggest that this algorithm is a useful tool to identify muscle synergies in a variety of natural self-paced motor behaviors.
CITATION STYLE
Delis, I., Hilt, P. M., Pozzo, T., & Berret, B. (2019). Identification of Spatial-Temporal Muscle Synergies from EMG Epochs of Various Durations: A Time-Warped Tensor Decomposition. In Biosystems and Biorobotics (Vol. 21, pp. 663–667). Springer International Publishing. https://doi.org/10.1007/978-3-030-01845-0_132
Mendeley helps you to discover research relevant for your work.