A Novel Framework Based on FastICA for High Density Surface EMG Decomposition
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 14.1 5.0 motor units were identified from each trial of experimental surface EMG signals.
IEEE Transactions on Neural Systems and Rehabilitation Engineering