Update README.md

README.md

@@ -1,8 +1,13 @@
-# UnfoldDecode
+# [![logo_UnfoldDecode jl_120px](https://github.com/unfoldtoolbox/UnfoldDecode.jl/assets/57703446/965b93aa-33e1-420e-a707-1fe8d7e3bcbe)](https://github.com/unfoldtoolbox/UnfoldDecode.jl/tree/main)
+
 [![Dev](https://img.shields.io/badge/docs-dev-blue.svg)](https://behinger.github.io/UnfoldDecode.jl/dev/)
 [![Build Status](https://github.com/behinger/UnfoldDecode.jl/actions/workflows/CI.yml/badge.svg?branch=main)](https://github.com/behinger/UnfoldDecode.jl/actions/workflows/CI.yml?query=branch%3Amain)
 [![Coverage](https://codecov.io/gh/behinger/UnfoldDecode.jl/branch/main/graph/badge.svg)](https://codecov.io/gh/behinger/UnfoldDecode.jl)
 
+|rERP|EEG visualisation|EEG Simulations|BIDS pipeline|Decode EEG data|Statistical testing|
+|---|---|---|---|---|---|
+| <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"></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"></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"></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"></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"></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"></a>|
+
 Beta-stage toolbox to decode ERPs with overlap, e.g. from eye-tracking experiments.
 
 > [!WARNING]
@@ -13,37 +18,79 @@ Currently the following algorithms are implemented:
 - [back-to-back regession](https://doi.org/10.1016/j.neuroimage.2020.117028) (`solver_b2b`, [tutorial how to use](https://unfoldtoolbox.github.io/Unfold.jl/dev/HowTo/custom_solvers/#Back2Back-regression)) 
 - overlap corrected LDA¹ proposed by [Gal Vishne, Leon Deouell et al.](https://doi.org/10.1101/2023.06.28.546397) is implemented, but more to follow.
 
-¹ actually any MLJ supported classification/regressoin model is already supported
+¹ actually any MLJ supported classification/regression model is already supported (beta)
 
-## Quickstart
+## Install
 
-```julia
-LDA = @load LDA pkg=MultivariateStats
+### Julia
+<details>
+<summary>Click to expand</summary>
 
-des = Dict("fixation" => (@formula(0~1+condition+continuous),firbasis((-0.1,1.),100)));
-uf_lda = fit(UnfoldDecodingModel,des,evt,dat,LDA(),"fixation"=>:condition)
-```
+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
+</details>
+
+### UnfoldDecode
 
-Does the trick - you should probably do an Unfold.jl tutorial first though!
-## Installation
 Not yet registered thus you have to do:
 ```julia
 using Pkg
 Pkg.add(url="https://github.com/behinger/UnfoldDecode.jl")
 using UnfoldDecode
 ```
 once it is registered, this will simplify to `Pkg.add("UnfoldDecode")`
+
+## Quickstart
+
+```julia
+LDA = @load LDA pkg=MultivariateStats
+
+des = Dict("fixation" => (@formula(0~1+condition+continuous),firbasis((-0.1,1.),100)));
+uf_lda = fit(UnfoldDecodingModel,des,evt,dat,LDA(),"fixation"=>:condition)
+```
+
+Does the trick - you should probably do an Unfold.jl tutorial first though!
+
 ## Loading Data
 have a look at PyMNE.jl to read the data. You need a data-matrix + DataFrames.jl event table (similar to EEGlabs EEG.events)
 
 ## Limitations
 - Not thoroughly tested, no unit-tests yet!
 - Missing features: e.g. No time generalization is available, but straight forward to implement with the current tooling.
 
-## Citing
+## 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](https://documentation.divio.com/)).
+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](https://github.com/unfoldtoolbox/Unfold.jl/blob/main/docs/make.jl) with a reference to `docs/src/generated/FOLDER/FILENAME.md`.
+
+
+## Citation
 
 If you use this code, please cite this code + the appropriate paper/algorithm
 
+## Acknowledgements
+
+Funded by Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany´s Excellence Strategy – EXC 2075 – 390740016
+
 ## Contributions
 
 <!-- ALL-CONTRIBUTORS-LIST:START - Do not remove or modify this section -->
@@ -61,3 +108,4 @@ This project follows the [all-contributors](https://allcontributors.org/docs/en/
 
 Contributions of any kind welcome!
 You can find the emoji key for the contributors [here](https://github.com/unfoldtoolbox/Unfold.jl/blob/main/docs/contrib-emoji.md).
+