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Unfold - EEG Deconvolution Toolbox

A toolbox for deconvolution of overlapping EEG (Pupil, LFP etc.) signals and (non)-linear modeling

Reference Papers

Download our reference paper Ehinger & Dimigen 2018 from bioRxiv (accepted in peerJ).

We recently published a new preprint on the analysis of Eyetracking/EEG data, with unfold playing a prominent role Dimigen & Ehinger 2019

If you use the toolbox, please cite us as: Ehinger BV & Dimigen O, Unfold: An integrated toolbox for overlap correction, non-linear modeling, and regression-based EEG analysis, bioRxiv, https://doi.org/10.1101/360156

Why deconvolution and non-linear modeling?

Find a twitter thread explaining the general idea here or have a look at Figure 1 of our paper

What can you do with unfold?

  • Adjust for overlap between subsequent potentials using linear deconvolution
  • Massive-Univarite Modeling (rERP) using R-style formulas, e.g. EEG~1+face+age
  • Non-linear effects using regression splines (GAM), e.g. EEG~1+face+spl(age,10)
  • Model multiple events, e.g. Stimulus, Response and Fixation
  • Use temporal basis functions (Fourier & Splines)
  • (Optional) regularization using glmnet

Unmixed - Unfold addon for mixed Models

A new addon unmixed, allowing to use mixed models can be found in its alpha version version here

Requirements

Getting Started

To get started, best is to start with the 2x2 ANOVA-Design tutorial Quickstart: 2x2 ANOVA