A phase decoder for reconstructing missing muscle activity
- 1Carnegie Mellon University
Abstract
Upper-limb assistive devices are commonly controlled with electromyographic (EMG) signals, but nerve injury, muscle loss, or practical limits on electrode placement can leave only a few usable signals, which constrains the decoding of intended movements. Here, we introduce a decoder that reconstructs missing muscle activations from sparse EMGs using a two-stage real-time approach: (1) estimating ongoing movement phase from available signals, and (2) retrieving corresponding muscle activations from a library of pre-recorded muscle activity templates. In proof-of-concept tests, neurologically intact participants (N=4) performed object-transfer tasks under simulated denervation conditions. The decoder accurately estimated movement phase (median root-mean-square error [RMSE] <0.17 of movement duration) and reconstructed missing muscle activations (RMSE 0.08-0.34; correlation up to 0.97) within ~2 ms, in a previously unseen participant. The results indicate that template based reconstruction could leverage sparse EMG for low latency control of assistive technology.
Related
- Smooth muscle DDIT4 exacerbates vascular remodeling in atherosclerosis and vascular injury by impairing PDC activity
- Continuous Large Maf Activity Maintains Fast Myofiber Identity and Multicellular Homeostasis in Adult Skeletal Muscle
- FUS controls muscle differentiation through phase separation-mediated recruitment of the transcription factors MEF2 and ETV5
- Transcription factor activity divergence across muscle atrophy conditions: A comparative analysis of spaceflight, aging, and disuse using decoupleR
- Video-rate quantitative phase and polarimetric imaging (QP2I) for label-free imaging of contracting muscle cells
BibTeX
@misc{bahdasariants2026phase,
title = {{A phase decoder for reconstructing missing muscle activity}},
author = {S. Bahdasariants and S. Yakovenko},
howpublished = {bioRxiv},
year = {2026},
month = oct,
doi = {10.64898/2026.10.28.762227},
url = {https://www.biorxiv.org/content/10.64898/2026.10.28.762227v1},
note = {CC BY},
}
RIS
TY - UNPB
TI - A phase decoder for reconstructing missing muscle activity
AU - S. Bahdasariants
AU - S. Yakovenko
PY - 2026
DA - 2026/10/28
PB - bioRxiv
DO - 10.64898/2026.10.28.762227
UR - https://www.biorxiv.org/content/10.64898/2026.10.28.762227v1
AB - Upper-limb assistive devices are commonly controlled with electromyographic (EMG) signals, but nerve injury, muscle loss, or practical limits on electrode placement can leave only a few usable signals, which constrains the decoding of intended movements. Here, we introduce a decoder that reconstructs missing muscle activations from sparse EMGs using a two-stage real-time approach: (1) estimating ongoing movement phase from available signals, and (2) retrieving corresponding muscle activations from a library of pre-recorded muscle activity templates. In proof-of-concept tests, neurologically intact participants (N=4) performed object-transfer tasks under simulated denervation conditions. The decoder accurately estimated movement phase (median root-mean-square error [RMSE] <0.17 of movement duration) and reconstructed missing muscle activations (RMSE 0.08-0.34; correlation up to 0.97) within ~2 ms, in a previously unseen participant. The results indicate that template based reconstruction could leverage sparse EMG for low latency control of assistive technology.
ER -