Key result
An automatic progressive FastICA peel-off framework for high density surface EMG decomposition achieved results comparable to those from the framework with human operator interaction.
The APFP framework successfully automates the decomposition of high density surface EMG signals with performance comparable to human-operated methods.
May facilitate operator-independent high-density EMG decomposition; leaves open prospective clinical validation.
This study presents automatic decomposition of high density surface electromyogram (EMG) signals through a progressive FastICA peel-off (PFP) framework. By incorporating FastICA, constrained FastICA and a peel-off strategy, the PFP can progressively expand the set of motor unit spike trains contributing to the EMG signal. A series of signal processing techniques were applied and integrated in this study to automatically implement the two tasks that often require human operator interaction during application of the PFP framework, including extraction of motor unit spike trains from FastICA outputs and reliability judgment of the extracted motor units. Based on these advances, an automatic PFP (APFP) framework was consequently developed. The decomposition performance of APFP was validated using simulated high density surface EMG signals. The APFP was also evaluated with experimental surface EMG signals, and the decomposition results were comparable to those achieved from the PFP with human operator interaction.
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Chen et al. (2017) studied High density surface EMG signal decomposition. Automatic progressive FastICA peel-off (APFP) framework vs. Progressive FastICA peel-off (PFP) with human operator interaction was evaluated on Decomposition performance. An automatic progressive FastICA peel-off framework for high density surface EMG decomposition achieved results comparable to those from the framework with human operator interaction.
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