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Communication Dans Un Congrès Année : 2019

Identification of Spatial-Temporal Muscle Synergies from EMG Epochs of Various Durations: A Time-Warped Tensor Decomposition

Ioannis Delis
  • Fonction : Auteur
Pauline Hilt
Thierry Pozzo
  • Fonction : Auteur

Résumé

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.

Domaines

Neurosciences
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Dates et versions

hal-04252767 , version 1 (24-10-2023)

Identifiants

Citer

Ioannis Delis, Pauline Hilt, Thierry Pozzo, Bastien Berret. Identification of Spatial-Temporal Muscle Synergies from EMG Epochs of Various Durations: A Time-Warped Tensor Decomposition. ICNR 2018: 4th International Conference on NeuroRehabilitation, Oct 2018, Pisa (Italy), Italy. pp.663-667, ⟨10.1007/978-3-030-01845-0_132⟩. ⟨hal-04252767⟩
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