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