20.500.12556/DKUM-97210 Hybrid neural interfaces and novel ways of assessing corticomuscular coupling Corticomuscular coupling is commonly assessed through the relationship between electroencephalographic (EEG) and electromyographic (EMG) signals using measures such as coherence, Granger causality, and transfer entropy. Previous studies have demonstrated that coupling strength depends on muscle exertion level and differs across clinical populations, including stroke patients. However, conventional EEG–EMG analysis does not account for the functional organization of motor units within muscles. In the HybridNeuro project, we develop novel biomarkers of corticomuscular coupling based on motor unit discharge patterns identified from high-density surface EMG (HDEMG). Two methodologies were designed. The first constructs motor unit–specific EEG filters that extract cortical components functionally linked to individual motor unit discharge patterns. The second replaces EMG signals with a motor unit activity index that estimates the collective activity of motor units within the detection volume of HDEMG electrodes in a fully automated and computationally efficient manner. Preliminary results in healthy young participants demonstrate increased sensitivity of corticomuscular coupling detection compared to state-of-the-art techniques. The motor unit–based EEG filters enable analysis of coupling within functional motor unit clusters and support cortical topography mapping, while the motor unit activity index identifies a larger number of motor units than conventional decomposition approaches. These findings suggest that motor unit–based methodologies enhance corticomuscular coupling assessment and hold promise for clinical applications, including stroke rehabilitation and other neuromuscular disorders. high-density surface electromyography electroencephalography hybrid neural intervaces corticomuscular coupling coherence motor unit functional clusters elektromiografija HDEMG elektroencefalografija true false true Slovenski jezik Angleški jezik Znanstveno delo 2026-02-23 16:57:55 2026-02-26 09:34:48 2026-02-27 03:17:41 0000-00-00 00:00:00 2024 0 0 2 0000-00-00 Zaloznikova Objavljeno NiDoloceno 0000-00-00 0000-00-00 0000-00-00 004.5 201063683 269713411 Hybrid_neural_interfaces_and_novel_ways_of_assessing_corticomuscular_coupling.pdf Hybrid_neural_interfaces_and_novel_ways_of_assessing_corticomuscular_coupling.pdf 1 5C47D726A9B00F6FA5ED59FC1B8D1261 5c7f9f27d1269f66f5566b1325ccf0697c188ad4704703ff58137704c13c50a4 5fd73585-116c-11f1-a1a6-00155d580e1a https://dk.um.si/Dokument.php?lang=slv&id=203750 https://www.neurocare.si/wp-content/uploads/2024/05/Holobar.pdf 1 4650d014-116c-11f1-a1a6-00155d580e1a https://dk.um.si/Dokument.php?lang=slv&id=203749 Fakulteta za elektrotehniko, računalništvo in informatiko 0 0 0