In a quest to unravel the complexities of motor unit (MU) identification accuracy, regression analysis, and Bayesian models, Professor Aleš Holobar recently hosted a webinar. The primary aim of this enlightening session 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.

Access the webinar content showcased here:

During the webinar, a voluntary questionnaire was offered. Answering it was entirely optional, and all responses were anonymized. Check out the results below.

Further reading:

• Winter, B., 2013. A very basic tutorial for performing linear mixed effects analyses. arXiv preprint arXiv:1308.5499, pp.1-22.
• G. K. Hajduk, Introduction to linear mixed models, https://ourcodingclub.github.io/tutorials/mixedmodels
• H. Schielzeth et al. Robustness of linear mixed-effects models to violations of distributional assumptions, https://doi.org/10.1111/2041-210X.13434
• S. A. Baldwin et al., An introduction to using Bayesian linear regression with clinical data, https://doi.org/10.1016/j.brat.2016.12.016
• M. Franke et al. A tutorial on contrast coding for (Bayesian) regression, https://michaelfranke.github.io/Bayesian-Regression/practice-sheets/01e-contrast-coding-tutorial.html
• E. Makalic et al. High-Dimensional Bayesian Regularised Regression with the BayesReg Package, arXiv:1611.06649 [stat.CO] Version 1.9.1.0 (105 KB) by Statovic
• Stan https://mc-stan.org/ (different programming languages supported, R, Matlab…)
• https://ourcodingclub.github.io/tutorials/brms/
• John K. Kruschke: Doing Bayesian Data Analysis: Bayesian assessment of null values, http://doingbayesiandataanalysis.blogspot.com/2016/12/bayesian-assessment-of-null-values.html , December 21, 2016