| Naslov: | Materials for HybridNeuro webinar titled "Validation of results: statistical models and MU identification accuracy" |
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| Avtorji: | ID Holobar, Aleš, ystem Software Laboratory, Faculty of Electrical Engineering and Computer Science, University of Maribor, Slovenia (Avtor)
ID Murks, Nina, ystem Software Laboratory, Faculty of Electrical Engineering and Computer Science, University of Maribor, Slovenia (Avtor) |
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| Datoteke: |  DKUM_introduction.pdf (108,35 KB)
MD5: 5B9AA1C3F0524C7338E03B3150DDC534
 HybridNeuro_Webinar_2024_Holobar_ValidationOfResults_materials_v1.0.0.zip (7,98 MB)
MD5: 2726A2D309FE2C95776059874FEB6448
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| Jezik: | Angleški jezik |
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| Vrsta gradiva: | Neznano |
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| Tipologija: | 2.20 - Zaključena znanstvena zbirka raziskovalnih podatkov |
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| Organizacija: | FERI - Fakulteta za elektrotehniko, računalništvo in informatiko
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| Opis: | 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. |
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| Ključne besede: | HybridNeuro, webinar, teaching materials, statistical models, regression analysis, motor unit identification, matlab, rstudio, statistics, surface high density electromyogram (HDEMG), tibialis anterior, dataset |
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| Založnik: | s. n. |
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| Leto izida: | 2024 |
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| PID: | 20.500.12556/DKUM-88845  |
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| UDK: | 004.6 |
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| COBISS.SI-ID: | 197835267 |
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| Datum objave v DKUM: | 30.05.2024 |
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| Število ogledov: | 221 |
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| Število prenosov: | 26 |
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| Metapodatki: |    |
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| Področja: | Ostalo
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