ICA-based muscle–tendon units localization and activation analysis during dynamic motion tasks

Abstract

This study proposed an independent component analysis (ICA)-based framework for localization and activation level analysis of muscle-tendon units (MTUs) within skeletal muscles during dynamic motion. The gastrocnemius muscle and extensor digitorum communis were selected as target muscles. High-density electrode arrays were used to record surface electromyographic (sEMG) data of the targeted muscles during dynamic motion tasks. First, the ICA algorithm was used to decompose multi-channel sEMG data into a weight coefficient matrix and a source matrix. Then, the source signal matrix was analyzed to determine EMG sources and noise sources. The weight coefficient vectors corresponding to the EMG sources were mapped to target muscles to find the location of the MTUs. Meanwhile, the activation level changes in MTUs during dynamic motion tasks were analyzed based on the corresponding EMG source signals. Eight subjects were recruited for this study, and the experimental results verified the feasibility and practicality of the proposed ICA-based method for the MTUs' localization and activation level analysis during dynamic motion. This study provided a new, in-depth way to analyze the functional state of MTUs during dynamic tasks and laid a solid foundation for MTU-based accurate muscle force estimation, muscle fatigue prediction, neuromuscular control characteristic analysis, etc.

Spatial filtering for enhanced high-density surface electromyographic examination of neuromuscular changes and its application to spinal cord injury

“…Although any individual surface electrode within the grid works as the regular single-channel, the grid formation warrants collection of important spatial information concerning muscle activation. Therefore, the HD-sEMG measurement is able to better characterize the muscle's structural and functional heterogeneity, which is regarded as the reflection of activities from different sources such as subcomponent muscles [11][12][13], muscle-tendon units [14][15][16], and even microscopic MUs [17][18][19][20]. Such spatial information is also helpful in suppressing muscular cross-talks within channels so as to improve the signal-noise ratio.…”

Spatial Filtering for Enhanced High-Density Surface Electromyographic Examination of Neuromuscular Changes and Its Application to Spinal Cord Injury

“…Therefore, the HD-sEMG measurement is able to better characterize the muscle's structural and functional heterogeneity, which is regarded as the reflection of activities from different sources such as subcomponent muscles [11][12][13], muscle-tendon units [14][15][16], and even microscopic MUs [17][18][19][20]. Such spatial information is also helpful in suppressing muscular cross-talks within channels so as to improve the signal-noise ratio.…”

“…The other category always employs unsupervised matrix factorization algorithms to process multi-channel sEMG and HD-sEMG data. These algorithms including principle component analysis (PCA) [27][28][29], canonical correlation analysis (CCA) [22], [30], [31], independent component analysis (ICA) [15], [16], [32] and nonnegative matrix analysis (NMF) [14], [29], [33][34][35] rely on different criteria concerning inherent structure of the input multivariate data. Among them, the PCA is the most fundamental multivariate data analysis algorithm that can find a new set of projection directions called principle components (PCs) that explain the maximum variability of the original data.…”

Spatial Filtering for Enhanced High-Density Surface Electromyographic Examination of Neuromuscular Changes and Its Application to Spinal Cord Injury

“…Its basic principle is to preserve the sources of interest and suppress unwanted components from signals [21][22][23][24][25][26]. Various matrix factorization algorithms [15], [16], [27][28][29][30][31][32][33][34][35] relied on different criteria concerning inherent structure of the input multivariate data. Among them, both principle component analysis (PCA) [27][28][29] and nonnegative matrix analysis (NMF) [14], [29], [33][34][35] algorithms have been commonly used due to their signal component separation capability, with successful applications in the field of decoding motor intentions including muscle strengths and patterns [27], [29], [38], [39].…”