Materials and surface HDEMG data for hands-on training on motor unit identification in dynamic and fatiguing muscle contractions (HybridNeuro project)

Murks, Nina; Kutoš, Leon; Divjak, Matjaž; Holobar, Aleš

| | SLO | ENG | Cookies and privacy

Bigger font | Smaller font

First page > Show document

Show document

Title:	Materials and surface HDEMG data for hands-on training on motor unit identification in dynamic and fatiguing muscle contractions (HybridNeuro project)
Authors:	ID Murks, Nina (Author) ID Kutoš, Leon (Author) ID Divjak, Matjaž (Author) ID Holobar, Aleš (Author)
Files:	README.pdf (490,60 KB) MD5: 77942A5A94252BE130B1B4779BADBF53 README.txt (21,12 KB) MD5: 5857D75E756E5F60CF351C0313270C2F metadata.xml (3,62 KB) MD5: BB31887851CC0BEEC17C78AC552BA219 This document has even more files. Complete list of files is available below.
Language:	English
Work type:	Other
Typology:	2.20 - Research data
Organization:	FERI - Faculty of Electrical Engineering and Computer Science
Abstract:	This 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.
Keywords:	HybridNeuro, hands-on-training, teaching materials, surface high density electromyogram (HDEMG), motor unit, EMG decomposition, DEMUSE Tool, dynamic contractions, fatigue, simulated HDEMG, evaluation, dataset, Matlab
Temporal coverage:	January 2025
Place of publishing:	[S. l.
Place of performance:	[S. l.
Publisher:	s. n.
Year of publishing:	2026
Number of pages:	1 spletni vir (več datotek)
PID:	20.500.12556/DKUM-96399
UDC:	004.6
COBISS.SI-ID:	265598467
Data col. methods:	Simulation Measurements and tests
Publication date in DKUM:	21.01.2026
Views:	244
Downloads:	58
Metadata:
Categories:	Misc.
:	Copy citation

Average score:	(0 votes)
Your score:	Voting is allowed only for logged in users.
Share:

Hover the mouse pointer over a document title to show the abstract or click on the title to get all document metadata.

Document is financed by a project

Funder:	EC - European Commission
Funding programme:	HE
Project number:	101079392
Name:	Hybrid neuroscience based on cerebral and muscular information for motor rehabilitation and neuromuscular disorders
Acronym:	HybridNeuro

Funder:	UKRI - UK Research and Innovation
Funding programme:	Horizon Europe Guarantee
Project number:	10052152
Name:	Hybrid neuroscience based on cerebral and muscular information for motor rehabilitation and neuromuscular disorders (HybridNeuro)
Acronym:	HybridNeuro

Licences

License:	CC BY 4.0, Creative Commons Attribution 4.0 International

Link:	http://creativecommons.org/licenses/by/4.0/
Description:	This is the standard Creative Commons license that gives others maximum freedom to do what they want with the work as long as they credit the author.
Licensing start date:	28.12.2025
Applies to:	The HDEMG data files in Matlab MAT format

License:	Apache License 2.0, Apache License, Version 2.0

Link:	https://www.apache.org/licenses/LICENSE-2.0
Description:	A license that allows you much freedom with the software, including an explicit right to a patent. State changes means that you have to include a notice in each file you modified.
Licensing start date:	28.12.2025
Applies to:	Files with code in Matlab programming language: individual_MU_accuracy_evaluation.m, MU_editing_evaluation_tool.m

License:	CC0 1.0, Creative Commons CC0 1.0 Universal

Link:	https://creativecommons.org/publicdomain/zero/1.0/deed.en
Description:	CC Zero enables scientists, educators, artists and other creators and owners of copyright- or database-protected content to waive those interests in their works and thereby place them as completely as possible in the public domain, so that others may freely build upon, enhance and reuse the works for any purposes without restriction under copyright or database law.
Licensing start date:	28.12.2025
Applies to:	metadata

Secondary language

Language:	Slovenian
Keywords:	elektromiografija, HDEMG, praktično usposabljanje, učno gradivo, utrujenost, dinamične kontrakcije, dinamično krčenje, podatkovni niz

Files

Comments

Leave comment

You must log in to leave a comment.

Comments (0)

0 - 0 / 0

There are no comments!

Back