Materials and surface HDEMG data for hands-on training on motor unit identification in dynamic and fatiguing muscle contractions (HybridNeuro project)Murks, Nina (Avtor) Kutoš, Leon (Avtor) Divjak, Matjaž (Avtor) Holobar, Aleš (Avtor) HybridNeurohands-on-trainingteaching materialssurface high density electromyogram (HDEMG)motor unitEMG decompositionDEMUSE Tooldynamic contractionsfatiguesimulated HDEMGevaluationdatasetMatlabThis dataset was prepared in the context of the HybridNeuro project (https://www.hybridneuro.feri.um.si/). It contains a collection of teaching materials and data that were used in the Hands-on Training on motor unit identification in dynamic and fatiguing muscle contractions, which was part of the Workshop on Invasive Interfaces (WSII25) in Gothenburg, Sweden, in January 2025. The Hands-on Training covered the practical aspects of motor unit identification in various types of muscle contractions. The teaching materials include a curated set of HDEMG signals, with simulated motor unit discharge patterns and Motor Unit Action Potentials (MUAPs) recorded from healthy volunteers in dynamic (biceps brachii) and fatiguing (abductor pollicis brevis) contractions. The HDEMG signals were decomposed using the DEMUSE software [1] (https://demuse.feri.um.si/). A dedicated MATLAB software was implemented for the accuracy assessment and visual comparison of automatic or manually edited HDEMG decomposition results and is included in the dataset. Total dataset size is 1 GB.s. n.20262025-12-28 22:03:51Drugo96399slJanuary 2025