Unfold Documentation

Documentation is currently written.

If you want to follow the tutorials, stat with the installation tutorial, else/then go for the mass-univariate approach, which should be familiar to you if you did ERPs before. Then the overlap-correction tutorial, mixed mass univariate, mixed overlap (tricky!). If you are then not satisfied, check out more advanced topics: effects-interface (aka what to do after fitting), or non-linear effects.

In case you want to understand the tools better, check out our explanations.

Once you are familiar with the tools, check out further how-to guides for specific applications.

In case you want to understand the toolbox better, we plan to offer technical references. This includes Benchmarks & Explorations.

Quick start

There are four different model types currently "fitable"

  1. Timeexpansion No, Mixed No : fit(UnfoldModel,Dict(Any=>(f,-0.1:0.01:0.5)),evts,data_epoch)
  2. Timeexpansion Yes, Mixed No : fit(UnfoldModel,Dict(Any=>(f,basisfunction)),evts,data)
  3. Timeexpansion No, Mixed Yes : fit(UnfoldModel,Dict(Any=>(fLMM,-0.1:0.01:0.5)),evts,data_epoch)
  4. Timeexpansion Yes, Mixed Yes: fit(UnfoldModel,Dict(Any=>(fLMM,basisfunction)),evts,data)

With

f = @formula 0~1+condition
fLMM = @formula 0~1+condition+(1|subject) + (1|item)
basisfunction = firbasis(τ=(-0.1,0.5),sfreq=100"))