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Unfold 1.1 - 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 2019 (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, peerJ, https://peerj.com/articles/7838/

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
  • Temporal Response Functions (TRFs)

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

  • MATLAB 2015a+
  • Statistics Toolbox
  • Continuous data in EEGLAB 12+ format
  • Unfold toolbox Download it on GitHub

Getting Started

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