Neural components project

The aim of this project was to investigate the relationship between low-dimensional components of motor unit activity and oscillations in muscle force during repetitive isometric tasks with highly similar force outputs. We further examined the consistency of these components across trials both for individual (tibialis anterior, TA; first dorsal interosseous, FDI) and synergistic muscles (vastus medialis, VM; vastus lateralis, VL). Participants performed fifteen trials of a force-matching learning task, and, following the learning phase, three consecutive trials were analyzed to ensure high similarity in force oscillations. Motor units were decomposed from high-density surface electromyograms using this algorithm, tracked across these trials, and their smoothed discharge rates were linearly decomposed into low-dimensional components using principal component analysis and factor analysis. To further characterize the coupling among motor unit activity across trials, we employed a recently developed non-linear framework combining information and network theoretic tools to decompose neural signals and characterize their network structure.

For further details, please refer to the READ ME file.