UnfoldMakie.jl Documentation

Welcome to UnfoldMakie.jl: a Julia package for visualizations of EEG/ERP data and Unfold.jl models.

Key features

🎯 Focused: Specialized for (r)ERP visualization.
🎨 Customizable: Full control over colors, lines, layouts via Makie.jl flexibility.
⚡ Fast: Complex figures (e.g., 50 topoplots) generated ~20× faster than EEGLAB.
🔄 Interactive: Partial support for Observables.jl, enabling dynamic plots.
🗺️ Smart aesthetics: Scientific color maps by default (no misleading rainbows!).
📚 Well-documented: Extensive examples and user guides.

For more highlights visit this page.

Installation

julia> using Pkg; Pkg.add("UnfoldMakie")

For more detailed instructions please refer to Installing Julia & Unfold Packages.

Usage example

Start with ERP plot and topopplot series.

using UnfoldMakie, CairoMakie, Unfold
results = Unfold.coeftable(UnfoldMakie.example_data("UnfoldLinearModel"))
f = Figure(size=(550, 300))
plot_erp!(f,
    results,
    mapping = (; col = :coefname, color = :coefname => "Conditions"),
    axis = (; xlabel = "Time [s]"),
    stderror = true,
)
f

using UnfoldMakie, CairoMakie
dat, positions = UnfoldMakie.example_data()
plot_topoplotseries(
    dat; bin_num = 9, nrows = 3,
    positions = positions,
    visual = (; label_scatter = false, contours = false),
    axis = (; xlabel = "Time windows [s]"),
    topolabels_rounding = (; digits = 2),
)

Where to start: Learning roadmap

1. First step

📌 Goal: Check why we need multiple plot types for Event-related potentials and what kind of plot types exist. 🔗 Plot types | Complex figures

2. Intermediate topics

📌 Goal: Check the most popular plots. 🔗 ERP plot | Topoplot

3. Advanced topics

📌 Goal: Learn about advanced customization 🔗 Visualize uncertainty in topoplot series

UnfoldMakie.jl is a Julia package for visualization of electroencephalography (EEG) data, with a focus on event-related potentials (ERPs) and regression-ERPs (rERPs). UnfoldMakie.jl fills a gap as one of the only dedicated EEG visualization libraries and offers ease of use, customization, speed, and detailed documentation. It allows for both explorative analysis (zooming/panning) and export to publication-ready vector graphics. This is achieved via multiple backends of Makie.jl: vector graphics with CairoMakie.jl, GPU-accelerated interactive graphics with GLMakie.jl, and the browser-based WGLMakie.jl.

In an earlier study (Mikheev et al., 2024), we asked novice and expert practitioners for insights into their ERP visualization practices. The results of this survey were used to develop and improve UnfoldMakie.jl. Further, UnfoldMakie.jl is agnostic (independent) of any specific analysis framework, but it nicely accompanies the rERP analysis package Unfold.jl (Ehinger & Dimigen, 2019).

The target audience of this package is anyone working with EEG, MEG, ERP, iEEG or other time-series data.