README.md

Estimation	Visualisation	Simulation	BIDS pipeline	Decoding	Statistics	MixedModelling

UnfoldMixedModels.jl is a package to perform hierarchical regression / linear mixed models on biological signals. As an experimental feature, it further allows to perform simultaneous overlap-correction / deconvolution.

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

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

UnfoldMixedModels.jl installation

using Pkg
Pkg.add("UnfoldMixedModels")

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(::UnfoldMixedModel)

Here is a quick overview on what to expect.

What you need

using UnfoldMixedModels

events::DataFrame

# formula with or without random effects

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
	x	fit(UnfoldModel,[Any=>(fLMM,times)),evts,data_epoch]
x	x	fit(UnfoldModel,[Any=>(fLMM,basis)),evts,data]

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

Alexandra M 🐛

Phillip Alday 💻

This project follows the all-contributors specification.

Contributions of any kind welcome!

Citation

For now, please cite

and/or Ehinger & Dimigen

Acknowledgements

This work was initially supported by the Center for Interdisciplinary Research, Bielefeld (ZiF) Cooperation Group "Statistical models for psychological and linguistic data".

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