A phase decoder for reconstructing missing muscle activity

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.