LMM+EEG Workshop @ CuttingGardens2023


Link to exercise .jl

LME4 vs. MixedModels.jl speed demo

MixedModels.jl is 100x faster


For a non-interactive, rendered html view, follow this link

The main workshop content are Pluto.jl notebook files. To start them

1) install Julia

2) run: using Pkg;Pkg.add("Pluto")

3) using Pluto;

A browser should pop up in which you can paste either the link to the notebook, or download it and select it from your computer.


This is the abstract I used to advertise for the workshop:

Linear mixed models are versatile and increasingly popular in cognitive psychology to analyze behavioural datasets with within-subject trial-repetitions. Some brave researchers have already applied these hierarchical models to EEG data, typically on averaged space/time region of interest. In this 2 h tutorial, I will revisit some of the basics and intuitions behind mixed models and extend them to the mass-univariate EEG case: fitting mixed models to all time-point and channels using the Unfold.jl & MixedModels.jl toolboxes in JuliaLang. You will be provided with interactive notebooks that run in your browser. We will explore together some implications and challenges of running such mass univariate LMMs: baseline periods, identifiability of random and item effects, and “usefulness”. I want to warn you though, I cannot provide a satisfactory answer to many of the issues that we will discuss in the workshop. My goal is rather to leave you with a sense of scientific curiosity, and the simulation- and analysis-tools to explore some of those issues yourself


Code is under MIT, slide content under CC-By.