better readme

README.md

@@ -1,10 +1,34 @@
 # UnfoldDecode
-
 [![Stable](https://img.shields.io/badge/docs-stable-blue.svg)](https://behinger.github.io/UnfoldDecode.jl/stable/)
 [![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)
 
+
+
+Toolbox to decode ERPs with overlap, e.g. from eye-tracking experiments.
+
+Currently only the 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
+
+## 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
+No time generalization is available, but straight forward to implement with the current tooling.
+
 ## Citing
 
-See [`CITATION.bib`](CITATION.bib) for the relevant reference(s).
+If you use this code, please cite this code + the appropriate paper/algorithm