Unfold.jl
Beta Toolbox to perform linear regression on biological signals.
This tool combines mass-univariate linear (mixed) models with overlap correction.
This kind of overlap correction is also known as encoding modeling, linear deconvolution, Temporal Response Functions (TRFs) and probably under other names. fMRI models with HRF-basis functions are also supported.
Relation to Unfold (matlab)
The matlab toolbox is recommended for research work. It is richer in features, better documented and tested.
The julia toolbox is a type of research-playground, but offers LinearMixedModel support.
| Feature | Unfold | unmixed | Unfold.jl |
|---|---|---|---|
| overlap correction | x | x | x |
| non-linear splines | x | x | x |
| plotting tools | x | UnfoldMakie.jl - beta | |
| sanity checks | x | ||
| tutorials | x | x | |
| speed | x | x | |
| unittests | x | x | |
| HRF (fMRI) basis | x | ||
| mix different basisfunctions | x | ||
| different timewindows per event | x | ||
| mixed models | x | x | |
| item & subject effects | x | x | |
| decoding | back2back regression |
Install
]add Unfold
Usage
For a quickstart:
f = @formula 0~1+condA
fLMM = @formula 0~1+condA+(1|subject) + (1|item)
events::DataFrame
data::Array{Float64,2}
epochs::Array{Float64,3} # channel x time x epochs (n-epochs == nrows(events))
times = range(0,length=size(epochs,3),step=1/sampling_rate)
basisfunction::Unfold.BasisFunction
basis = firbasis(τ=(-0.3,0.5),srate=250)
- Timeexpansion No, Mixed No :
fit(UnfoldModel,Dict(Any=>(f,times)),evts,data_epoch) - Timeexpansion Yes, Mixed No :
fit(UnfoldModel,Dict(Any=>(f,basis)),evts,data) - Timeexpansion No, Mixed Yes :
fit(UnfoldModel,Dict(Any=>(fLMM,times)),evts,data_epoch) - Timeexpansion Yes, Mixed Yes:
fit(UnfoldModel,Dict(Any=>(fLMM,basis)),evts,data)
Documentation
Most functions have documentation, e.g. ?Unfold.fit
Tutorials see the documentation
Contributors (alphabetically)
- Phillip Alday
- Benedikt Ehinger
- Dave Kleinschmidt
- Judith Schepers
- Felix Schröder
- René Skukies
Acknowledgements
This work was 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
Research notice
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