A Novel Framework Based on FastICA for High Density Surface EMG Decomposition

摘要

This study presents a progressive FastICA peel-off (PFP) framework for high-density surface electromyogram (EMG) decomposition. The novel framework is based on a shift-invariant model for describing surface EMG. The decomposition process can be viewed as progressively expanding the set of motor unit spike trains, which is primarily based on FastICA. To overcome the local convergence of FastICA, a peel-off strategy, i.e., removal of the estimated motor unit action potential trains from the previous step, is used to mitigate the effects of the already identified motor units, so more motor units can be extracted. A constrained FastICA is applied to assess the extracted spike trains and correct possible erroneous or missed spikes. These procedures work together to improve decomposition performance. The proposed framework was validated using simulated surface EMG signals with different motor unit numbers (30, 70, 91) and SNRs (20, 10, and 0 dB). The results demonstrated relatively large numbers of extracted motor units and high accuracies (high F1-scores). The framework was tested with 111 trials of 64-channel electrode array experimental surface EMG signals during the first dorsal interosseous muscle contraction at different intensities. On average were identified from each trial of experimental surface EMG signals.