|Estimation|Visualisation|Simulation|BIDS pipeline|Decoding|Statistics|MixedModelling| |---|---|---|---|---|---|---| | <a href="https://github.com/unfoldtoolbox/Unfold.jl/tree/main"><img src="https://github-production-user-asset-6210df.s3.amazonaws.com/10183650/277623787-757575d0-aeb9-4d94-a5f8-832f13dcd2dd.png" alt="Unfold.jl Logo"></a> | <a href="https://github.com/unfoldtoolbox/UnfoldMakie.jl"><img src="https://github-production-user-asset-6210df.s3.amazonaws.com/10183650/277623793-37af35a0-c99c-4374-827b-40fc37de7c2b.png" alt="UnfoldMakie.jl Logo"></a>|<a href="https://github.com/unfoldtoolbox/UnfoldSim.jl"><img src="https://github-production-user-asset-6210df.s3.amazonaws.com/10183650/277623795-328a4ccd-8860-4b13-9fb6-64d3df9e2091.png" alt="UnfoldSim.jl Logo"></a>|<a href="https://github.com/unfoldtoolbox/UnfoldBIDS.jl"><img src="https://github-production-user-asset-6210df.s3.amazonaws.com/10183650/277622460-2956ca20-9c48-4066-9e50-c5d25c50f0d1.png" alt="UnfoldBIDS.jl Logo"></a>|<a href="https://github.com/unfoldtoolbox/UnfoldDecode.jl"><img src="https://github-production-user-asset-6210df.s3.amazonaws.com/10183650/277622487-802002c0-a1f2-4236-9123-562684d39dcf.png" alt="UnfoldDecode.jl Logo"></a>|<a href="https://github.com/unfoldtoolbox/UnfoldStats.jl"><img src="https://github-production-user-asset-6210df.s3.amazonaws.com/10183650/277623799-4c8f2b5a-ea84-4ee3-82f9-01ef05b4f4c6.png" alt="UnfoldStats.jl Logo"></a>|<a href="https://github.com/unfoldtoolbox/UnfoldMixedModels.jl"><img src="https://github.com/user-attachments/assets/ffb2bba6-3a30-48b7-9849-7d4e7195b297" alt="UnfoldMixedModels.jl logo"></a>|

Package (-family) to perform linear / GAM / hierarchical / deconvolution regression on biological signals.

This kind of modelling is also known as encoding modeling, linear deconvolution, Temporal Response Functions (TRFs), linear system identification, and probably under other names. fMRI models with HRF-basis functions and pupil-dilation bases are also supported.

Getting started

🐍Python User?

We clearly recommend Julia 😉 - but Python users can use juliacall/Unfold directly from python!

Julia installation

<details> <summary>Click to expand</summary>

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

</details>

Unfold.jl installation

using Pkg
Pkg.add("Unfold")

Usage

Please check out the documentation for extensive tutorials, explanations and more!

Tipp on Docs

You can read the docs online: - or use the ?fit, ?effects julia-REPL feature. To filter docs, use e.g. ?fit(::UnfoldModel)

Here is a quick overview on what to expect.

What you need

using Unfold

events::DataFrame

# formula with or without random effects
f = @formula 0~1+condA
fLMM = @formula 0~1+condA+(1|subject) + (1|item)

# in case of [overlap-correction] we need continuous data plus per-eventtype one basisfunction (typically firbasis)
data::Array{Float64,2}
basis = firbasis(τ=(-0.3,0.5),srate=250) # for "timeexpansion" / deconvolution

# in case of [mass univariate] we need to epoch the data into trials, and a accompanying time vector
epochs::Array{Float64,3} # channel x time x epochs (n-epochs == nrows(events))
times = range(0,length=size(epochs,3),step=1/sampling_rate)

To fit any of the models, Unfold.jl offers a unified syntax:

| Overlap-Correction | Mixed Modelling | julia syntax | |:---:|:---:|---| | | | fit(UnfoldModel,[Any=>(f,times)),evts,data_epoch] | | x | | fit(UnfoldModel,[Any=>(f,basis)),evts,data] | | | x | fit(UnfoldModel,[Any=>(fLMM,times)),evts,data_epoch] | | x | x | fit(UnfoldModel,[Any=>(fLMM,basis)),evts,data] |

Comparison to Unfold (matlab)

<details> <summary>Click to expand</summary>

The matlab version is still maintained, but active development happens in Julia.

| Feature | Unfold | unmixed (defunct) | Unfold.jl | |-------------------------|--------|---------|-----------| | overlap correction | x | x | x | | non-linear splines | x | x | x | | speed | | 🐌 | ⚡ 2-100x | | GPU support | | | 🚀| | plotting tools | x | | UnfoldMakie.jl | | Interactive plotting | | | stay tuned - coming soon! | | simulation tools | x | | UnfoldSim.jl | | BIDS support | x | | alpha: UnfoldBIDS.jl) | | sanity checks | x | | x | | tutorials | x | | x | | unittests | x | | x | | Alternative bases e.g. HRF (fMRI) | | | x | | mix different basisfunctions | | | x | | different timewindows per event | | | x | | mixed models | | x | x | | item & subject effects | | (x) | x | | decoding | | | UnfoldDecode.jl | | outlier-robust fits | | | many options (but slower) | | 🐍Python support | | | via juliacall|

</details>

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.

Contributors

<!-- ALL-CONTRIBUTORS-LIST:START - Do not remove or modify this section --> <!-- prettier-ignore-start --> <!-- markdownlint-disable --> <table> <tbody> <tr> <td align="center" valign="top" width="14.28%"><a href="https://github.com/jschepers"><img src="https://avatars.githubusercontent.com/u/22366977?v=4?s=100" width="100px;" alt="Judith Schepers"/><br />_{<b>Judith Schepers</b>}</a><br /><a href="#bug-jschepers" title="Bug reports">🐛</a> <a href="#code-jschepers" title="Code">💻</a> <a href="#doc-jschepers" title="Documentation">📖</a> <a href="#tutorial-jschepers" title="Tutorials">✅</a> <a href="#ideas-jschepers" title="Ideas, Planning, & Feedback">🤔</a> <a href="#test-jschepers" title="Tests">⚠️</a></td> <td align="center" valign="top" width="14.28%"><a href="http://www.benediktehinger.de"><img src="https://avatars.githubusercontent.com/u/10183650?v=4?s=100" width="100px;" alt="Benedikt Ehinger"/><br />_{<b>Benedikt Ehinger</b>}</a><br /><a href="#bug-behinger" title="Bug reports">🐛</a> <a href="#code-behinger" title="Code">💻</a> <a href="#doc-behinger" title="Documentation">📖</a> <a href="#tutorial-behinger" title="Tutorials">✅</a> <a href="#ideas-behinger" title="Ideas, Planning, & Feedback">🤔</a> <a href="#test-behinger" title="Tests">⚠️</a> <a href="#infra-behinger" title="Infrastructure (Hos