rERP	EEG visualisation	EEG Simulations	BIDS pipeline	Decode EEG data	Statistical testing

A toolbox for visualizations of EEG/ERP data and Unfold.jl models. Building on the Unfold and Makie, it grants users high performance, and highly customizable plots.

We currently support:

• ERP plots
• Butterfly plots
• Topography plots
• Topography time series
• ERP grid
• ERP images
• Channel images
• Parallel coordinates
• Designmatrices
• Circular topoplots

Install

Installing Julia

Click to expand

The recommended way to install julia is juliaup. It allows you to, e.g., easily update Julia at a later point, but also test out alpha/beta versions etc.

TL:DR; If you dont want to read the explicit instructions, just copy the following command

Windows

AppStore -> JuliaUp, or winget install julia -s msstore in CMD

Mac & Linux

curl -fsSL https://install.julialang.org | sh in any shell

Installing Unfold

using Pkg
Pkg.add("UnfoldMakie")

Quickstart

using UnfoldMakie
using CairoMakie # backend
using Unfold,UnfoldSim # Fit / Simulation

data, evts = UnfoldSim.predef_eeg(; noiselevel = 12, return_epoched = true)
data = reshape(data,1,size(data)...) # fake a single channel
	
times = range(0, step = 1 / 100, length = size(data, 2))
m = fit(UnfoldModel,@formula(0~1+condition),evts,data,times)

plot_erp(coeftable(m))

Contributions

Contributions are very welcome. These could be typos, bugreports, feature-requests, speed-optimization, new solvers, better code, better documentation.

How-to Contribute

You are very welcome to raise issues and start pull requests!

Adding Documentation

1. We recommend to write a Literate.jl document and place it in docs/literate/FOLDER/FILENAME.jl with FOLDER being HowTo, Explanation, Tutorial or Reference (recommended reading on the 4 categories).
2. Literate.jl converts the .jl file to a .md automatically and places it in docs/src/generated/FOLDER/FILENAME.md.
3. Edit make.jl with a reference to docs/src/generated/FOLDER/FILENAME.md.

Citation

If you make use of theses visualizations, please cite:

Contributors (alphabetically)

• Daniel Baumgartner
• Benedikt Ehinger
• Sören Döring
• Niklas Gärtner
• Furkan Lokman
• Vladimir Mikheev

Acknowledgements

Funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) – project ID 251654672 – TRR 161 (project D05)

Funded by Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany´s Excellence Strategy – EXC 2075 – 390740016