1. Materials for HybridNeuro webinar titled "Validation of results: statistical models and MU identification accuracy"Aleš Holobar, Nina Murks, 2024, complete scientific database of research data Abstract: This dataset contains a collection of teaching materials that were used in the HybridNeuro project webinar titled "Validation of results: statistical models and MU identification accuracy". The webinar was presented by Aleš Holobar and covered the complexities of motor unit (MU) identification accuracy, regression analysis and Bayesian models. The primary aim of the webinar was to spark a robust discussion within the scientific community, particularly focusing on the application and implications of linear mixed models and Bayesian regression in the realm of MU identification. The teaching materials include Matlab and R source code for statistical analysis of the included data, as well as three examples of MU identification results in CSV format (from both synthetic and experimental HDEMG signals). The presentation slides in PDF format are also included. The dataset is approximately 9 MB in size.
Keywords: HybridNeuro, webinar, teaching materials, statistical models, regression analysis, motor unit identification, matlab, rstudio, statistics, surface high density electromyogram (HDEMG), tibialis anterior, dataset
Published in DKUM: 30.05.2024; Views: 221; Downloads: 25
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2. Simulated and experimental HDEMG signals of biceps brachii muscle for analysis of motor unit mergingAleš Holobar, Jakob Škarabot, Dario Farina, 2024, complete scientific database of research data Abstract: This dataset contains a collection of simulated and experimental surface HDEMG recordings of the biceps brachii muscle during the isometric elbow flexion. Simulated data contains 50 recordings: 5 subjects and 5 excitation levels, each with and without added noise. Experimental data contains 16 recordings: 2 subjects with 4 excitation levels and 2 repetitions of each level. Synthetic data was simulated using the cylindrical volume conductor model [1] and the motor unit recruitment and firing modulation model proposed in [2]. Each recording is 20 seconds in length with 90 HDEMG channels sampled at 2048 Hz and is stored as a 2D matrix of raw EMG values in Matlab’s MAT format. Experimental surface EMG data was recorded on two volunteers during isometric contractions at constant force level. Each recording is 25 seconds in length with 64 HDEMG channels sampled at 2048 Hz and is also stored as a 2D matrix of raw EMG values in Matlab’s MAT format. The dataset is approximately 1.5 GB in size.
Keywords: surface high density electromyogram (HDEMG), motor unit, spike train, motor unit merging, simulated data, experimental data, biceps brachii, dataset
Published in DKUM: 30.05.2024; Views: 172; Downloads: 23
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