Unfold Documentation

Documentation is currently written.

Best to start with installation, then go for the mass-univariate approach lm_mu.md, which should be familiar to you if you did ERPs before. Then the overlap-correction lm_overlap.md, LMM (to-be-done) and non-linear (to-be-done) would be good stations.

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.

Summary

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"))

TOC

• Custom Solvers
• Basis Functions
• Unfold Documentation
• • Summary
  • TOC
  • Installation
• Mass Univariate Linear Models (no overlap correction)
• • Installation
  • Setting up & loading the data
  • Traditional Mass Univariate Analysis
• Mass Univariate Linear Models (no overlap correction)
• • Setting up & loading the data
  • Overlap Correction
  • Timeexpanded / Deconvolved ModelFit
• Overlap Correction with Linear Mixed Models
• • Mass Univariate Mixed Models
• Overlap Correction with Linear Mixed Models
• • Load the data
  • Mass Univariate Mixed Models