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Unfold

Neuroimaging (EEG, fMRI, pupil ...) regression analysis in Julia

https://github.com/unfoldtoolbox/unfold.jl

Science Score: 59.0%

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    Found 6 DOI reference(s) in README
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    Links to: zenodo.org
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    3 of 10 committers (30.0%) from academic institutions
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    Low similarity (9.2%) to scientific vocabulary

Keywords

deconvolution eeg erp event-related-potentials julia mass-univariate-modeling mixed-models modeling overlap regression splines

Keywords from Contributors

mixed-effects statistical-models
Last synced: 6 months ago · JSON representation

Repository

Neuroimaging (EEG, fMRI, pupil ...) regression analysis in Julia

Basic Info
  • Host: GitHub
  • Owner: unfoldtoolbox
  • License: mit
  • Language: Julia
  • Default Branch: main
  • Homepage:
  • Size: 70.8 MB
Statistics
  • Stars: 62
  • Watchers: 4
  • Forks: 14
  • Open Issues: 39
  • Releases: 50
Topics
deconvolution eeg erp event-related-potentials julia mass-univariate-modeling mixed-models modeling overlap regression splines
Created about 6 years ago · Last pushed 6 months ago
Metadata Files
Readme License Code of conduct Zenodo

README.md

Unfold.jl EEG toolbox

Stable Documentation In development documentation Test workflow status Lint workflow Status Docs workflow Status Coverage DOI Contributor Covenant All Contributors

|Estimation|Visualisation|Simulation|BIDS pipeline|Decoding|Statistics|MixedModelling| |---|---|---|---|---|---|---| | Unfold.jl Logo | UnfoldMakie.jl Logo|UnfoldSim.jl Logo|UnfoldBIDS.jl Logo|UnfoldDecode.jl Logo|UnfoldStats.jl Logo|UnfoldMixedModels.jl logo|

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

Click to expand The recommended way to install julia is [juliaup](https://github.com/JuliaLang/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

Unfold.jl installation

julia 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: Stable Documentation - 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

```julia 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)

Click to expand 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](https://unfoldtoolbox.github.io/UnfoldDocs/UnfoldMakie.jl/dev/) | | Interactive plotting | | | stay tuned - coming soon! | | simulation tools | x | | [UnfoldSim.jl](https://unfoldtoolbox.github.io/UnfoldDocs/UnfoldSim.jl) | | BIDS support | x | | alpha: [UnfoldBIDS.jl](https://unfoldtoolbox.github.io/UnfoldDocs/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](https://unfoldtoolbox.github.io/UnfoldDocs/UnfoldDecode.jl) | | outlier-robust fits | | | [many options (but slower)](https://unfoldtoolbox.github.io/UnfoldDocs/Unfold.jl/dev/HowTo/custom_solvers/#Robust-Solvers) | | 🐍Python support | | | [via juliacall](https://unfoldtoolbox.github.io/UnfoldDocs/Unfold.jl/dev/generated/HowTo/juliacall_unfold/)|

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

Judith Schepers
Judith Schepers
🐛 💻 📖 ✅ 🤔 ⚠️		 Benedikt Ehinger
Benedikt Ehinger
🐛 💻 📖 ✅ 🤔 ⚠️ 🚇 ⚠️ 🚧 👀 💬		 René Skukies
René Skukies
🐛 📖 ✅ 💻 🤔		 Manpa Barman
Manpa Barman
🚇		 Phillip Alday
Phillip Alday
💻 🚇		 Dave Kleinschmidt
Dave Kleinschmidt
📖		 Saket Saurabh
Saket Saurabh
🐛
suddha-bpn
suddha-bpn
🐛		 Vladimir Mikheev
Vladimir Mikheev
🐛 📖		 carmenamme
carmenamme
📖		 Maximilien Van Migem
Maximilien Van Migem
🐛		 Till Prölß
Till Prölß
📖 🐛		 Leon von Haugwitz
Leon von Haugwitz
🐛

This project follows the all-contributors specification.

Contributions of any kind welcome!

Citation

For now, please cite

DOI 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

Owner

  • Name: unfoldtoolbox
  • Login: unfoldtoolbox
  • Kind: organization
  • Email: info@unfoldtoolbox.org

Unfold your potentials...

GitHub Events

Total
  • Create event: 48
  • Release event: 11
  • Issues event: 14
  • Watch event: 12
  • Delete event: 7
  • Issue comment event: 59
  • Push event: 201
  • Pull request review event: 15
  • Pull request review comment event: 29
  • Pull request event: 75
  • Fork event: 4
Last Year
  • Create event: 48
  • Release event: 11
  • Issues event: 14
  • Watch event: 12
  • Delete event: 7
  • Issue comment event: 59
  • Push event: 201
  • Pull request review event: 15
  • Pull request review comment event: 29
  • Pull request event: 75
  • Fork event: 4

Committers

Last synced: almost 3 years ago

All Time
  • Total Commits: 514
  • Total Committers: 10
  • Avg Commits per committer: 51.4
  • Development Distribution Score (DDS): 0.296
Top Committers
Name Email Commits
behinger (s-ccs 001) b****r@v****e 362
Benedikt Ehinger b****r@u****e 132
Phillip Alday me@p****m 11
Judith Schepers j****s@u****e 2
Phillip Alday p****y@u****m 2
ReneSkukies 5****s@u****m 1
Felix Schroder f****x@s****e 1
Yo Yehudi y****i@p****k 1
Dave Kleinschmidt d****t@g****m 1
Vladimir d****m@b****u 1
Committer Domains (Top 20 + Academic)
bk.ru: 1 postgrad.manchester.ac.uk: 1 schroder.de: 1 uni-osnabrueck.de: 1 phillipalday.com: 1 uos.de: 1 vis.uni-stuttgart.de: 1

Issues and Pull Requests

Last synced: 6 months ago

All Time
  • Total issues: 99
  • Total pull requests: 187
  • Average time to close issues: 7 months
  • Average time to close pull requests: 28 days
  • Total issue authors: 13
  • Total pull request authors: 15
  • Average comments per issue: 2.6
  • Average comments per pull request: 0.45
  • Merged pull requests: 161
  • Bot issues: 0
  • Bot pull requests: 55
Past Year
  • Issues: 11
  • Pull requests: 75
  • Average time to close issues: 11 days
  • Average time to close pull requests: 12 days
  • Issue authors: 5
  • Pull request authors: 8
  • Average comments per issue: 0.45
  • Average comments per pull request: 0.37
  • Merged pull requests: 63
  • Bot issues: 0
  • Bot pull requests: 44
View more stats
Top Authors
Issue Authors
  • behinger (68)
  • jschepers (9)
  • vladdez (7)
  • ReneSkukies (5)
  • ssaket (3)
  • LeonvonHaugwitz (1)
  • maxvanmigem (1)
  • chsquare (1)
  • suddhasourav (1)
  • suddha-bpn (1)
  • duodenum96 (1)
  • JuliaTagBot (1)
  • Till223 (1)
Pull Request Authors
  • behinger (131)
  • github-actions[bot] (33)
  • allcontributors[bot] (18)
  • vladdez (11)
  • dependabot[bot] (10)
  • jschepers (10)
  • ReneSkukies (8)
  • palday (4)
  • carmenamme (2)
  • Till223 (2)
  • xuyg16 (2)
  • Zebrakopf (1)
  • lokmanfl (1)
  • yochannah (1)
Top Labels
Issue Labels
enhancement (13) bug (9) documentation (4)
Pull Request Labels
run benchmark (13) chore (13) dependencies (10) dont run tests (2) sync darus (1)

Packages

  • Total packages: 1
  • Total downloads:
    • julia 8 total
  • Total dependent packages: 2
  • Total dependent repositories: 0
  • Total versions: 45
juliahub.com: Unfold

Neuroimaging (EEG, fMRI, pupil ...) regression analysis in Julia

  • Versions: 45
  • Dependent Packages: 2
  • Dependent Repositories: 0
  • Downloads: 8 Total
Rankings
Forks count: 22.3%
Average: 22.8%
Stargazers count: 23.2%
Last synced: 6 months ago

Dependencies

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.github/workflows/documenter.yml actions
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