Acronym: MUCLUSTER

Institution: Faculty of Electrical engineering and Computer science, University of Maribor

Project type: Basic research project (national)

Implementation period: 1.1.2025―31.12.2027

Budget: 300.000,00 €

Project website:  https://lspo.feri.um.si/MUcluster/index-en.php

PROJECT SUMMARY:

In this project, we investigate the effect of motor unit (MU) crosstalk in the pulse trains identified by classical blind source separation algorithms from high-density electromyograms (HDEMG).  We build on the fact that when compared to the several hundreds of MUs active in individual muscles, the number of MUs identified from HDEMG is relatively low. During the decomposition process, only accurately identified MUs are kept for further analysis. Many more MUs that have pulse trains merged with other similar MUs do not meet strict quality measures and get discarded in the process of MU identification. This leads to the relatively low percentage of the energy of HDEMG signals accounted for by decomposition and challenges the representativeness of the identified group of MUs.

We are addressing the following research questions:

1) What are the key factors contributing to MU merging in different skeletal muscles and across different conditions (isometric vs. dynamic vs. elicited contractions), different muscles (distal vs. proximal) and different sexes (female vs. male)?

2) By how much can we increase the number of identified MUs if we take merged MUs into account, also in muscles with traditionally low MU yield?

3) How does the MU merging (e.g. anatomical clustering in HDEMG decomposition) relate to the functional coupling of MUs?

4) What nonlinear techniques, including deep neural network architectures, can be used for functional clustering of MU discharge patterns, including the merged MUs, and how effective they are in different experimental conditions?

5) How does the functional clustering of MUs change across different conditions (isometric vs. dynamic, voluntary vs. elicited contractions), different muscles (distal vs. proximal) and different sexes (female vs. male)?

6) To what extent can the analysis of merged MUs reduce the need for time-consuming manual editing of HDEMG decomposition results and increase the robustness of HDEMG decomposition to noise, the thickness of adipose tissue and different experimental conditions?