Neuromechanical Assessment ofIntact and Impaired Muscle Control
High-density EMG-informed approach
Time: Fri 2025-05-23 09.00
Location: Kollegiesallen, Room 4301, Brinellvägen 6
Language: English
Subject area: Engineering Mechanics
Doctoral student: Asta Kizyte , Teknisk mekanik, MoveAbility lab
Opponent: Univ.Prof.Dr.rer.nat. Catherine Dißelhorst-Klug, RWTH Aachen University
Supervisor: Ruoli Wang, BioMEx, Teknisk mekanik; Elena Gutierrez-Farewik, Teknisk mekanik, BioMEx
QC 250506
Abstract
Neuromuscular impairments in ankle dorsi-/plantarflexor muscles presentrehabilitation challenges after spinal cord injury (SCI) and stroke. Reliabletorque prediction and characterization of muscle impairments are essential forguiding rehabilitation and monitoring recovery. This thesis aims to assess howhigh-density EMG (HDEMG) improves torque estimation by integrating spatialand neurophysiological data (Studies I & II) and examine motor unit (MU)behavior and corticomuscular connectivity in SCI and stroke (Studies III & IV).Proposed methodologies combine HDEMG with advanced signal processingtechniques. Specifically, Study I uses machine learning (ML) to predict torquefrom bipolar EMG, HDEMG, and extracted features. Study II incorporates acomputational cumulative spike train-driven motoneuron pool model into aneuromusculoskeletal framework to generate neural drive signals. Study IIIuses HDEMG decomposition to analyze MU firing behavior in SCI. Study IVinvestigates MU/EMG–EEG corticomuscular coherence (CMC) to assesscorticospinal disruptions in stroke.Findings from Studies I & II show ML methods predict torque well in staticconditions but face challenges in dynamic movement due to absence ofkinematic constraints. Neuromusculoskeletal modeling provides betterrepresentation of neural and mechanical function by incorporating MU firingproperties. Studies III & IV offer insights into MU-level changes inneuromuscular disorders. Specifically, Study III identifies SCI-related EMG andMU behavior alterations, reflecting compensatory motor control strategies.Study IV introduces MU-level CMC analysis in stroke, revealing that motorneuron parameters do not significantly determine CMC strength, and thefundamental pattern of beta-band coupling over motor areas remainsidentifiable across all subject groups and CMC modalities.Overall, this thesis demonstrates that HDEMG enhances torque estimation andneuromuscular assessment. By integrating spatial EMG features and MU-levelanalyses, it deepens understanding of pathological motor control andneurophysiology, with implications for rehabilitation, assistive devices, andneuromuscular modeling.