rERP | EEG visualisation | EEG Simulations | BIDS pipeline | Decode EEG data | Statistical testing |
---|---|---|---|---|---|
A toolbox for visualizations of EEG/ERP data and Unfold.jl models. Based 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
- Design matrices
- 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 to test out alpha/beta versions etc.
TLDR: If you don't 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 can be typos, bug reports, feature requests, speed improvements, new solvers, better code, better documentation.
How to Contribute
You are very welcome to submit issues and start pull requests!
Adding Documentation
- We recommend to write a Literate.jl document and place it in
docs/literate/FOLDER/FILENAME.jl
withFOLDER
beingHowTo
,Explanation
,Tutorial
orReference
(recommended reading on the 4 categories). - Literate.jl converts the
.jl
file to a.md
automatically and places it indocs/src/generated/FOLDER/FILENAME.md
. - Edit make.jl with a reference to
docs/src/generated/FOLDER/FILENAME.md
.
Citation
If you use these visualizations, please cite:
Contributors
Acknowledgements
Funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) β Project-ID 251654672 β TRR 161β / βGefΓΆrdert durch die Deutsche Forschungsgemeinschaft (DFG) β Projektnummer 251654672 β TRR 161.
Funded by Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under GermanyΒ΄s Excellence Strategy β EXC 2075 β 390740016