Independent component analysis based algorithms for high-density electromyogram decomposition: Systematic evaluation through simulation

Abstract

Motor unit activities provide important theoretical and clinical insights regarding different aspects of neuromuscular control. Based on high-density electromyogram (HD EMG) recordings, we systematically evaluated the performance of three independent component analysis (ICA)-based EMG decomposition algorithms (Infomax, FastICA and RobustICA). The algorithms were tested on simulated HD EMG signals with a range of muscle contraction levels and with a range of signal quality. Our results showed that all the three algorithms can output accurate (85%–100%) motor unit discharge timings. Specifically, the RobustICA consistently showed high decomposition accuracy among the three algorithms under a variety of signal conditions, especially with a low signal quality and varying contraction levels. But the yield of decomposition of RobustICA tended to be low at high contraction levels. In contrast, FastICA tended to show the lowest accuracy, but can detect the largest number of motor units, especially at high contraction levels. Our results also showed that the computation time was similar for FastICA and RobustICA, which was shorter than Infomax. Additionally, the accuracy of each algorithm correlated moderately with the clustering index—the silhouette distance measure, and correlated strongly with the rate of agreement of the algorithm pairs. Overall, our findings provide guidance on selecting particular decomposition algorithms based on specific applications with different requirement on the accuracy/yield of the decomposition.