A toolbox for deconvolution of overlapping EEG signals and (non)-linear modeling
- Linear deconvolution
- Model specification using R-style formulas (EEG~1+face+age)
- Programmed in a modular fashion
- Spline regression
- Regularization (using glmnet)
- Temporal basis functions (Fourier & Splines)
- Estimate temporal response functions (TRFs) for time-continuous predictors
- Cross-validation
📢 Try out our discussion forum - we often get questions via email, a more transparent and open way is to use the github discussions feature
git clone https://github.com/unfoldtoolbox/unfold
git submodule update --init --recursive --remote
run('init_unfold.m')
Check out the toolbox tutorials for more information!
EEG = tutorial_simulate_data('2x2')
EEG = uf_designmat(EEG,'eventtypes',{'fixation'},'formula','y ~ 1+ cat(stimulusType)*cat(color)')
EEG = uf_timeexpandDesignmat(EEG,'timelimits',[-0.5 1])
EEG = uf_glmfit(EEG)
% (strictly speaking optional, but recommended)
ufresult = uf_condense(EEG)
ax = uf_plotParam(ufresult,'channel',1);
Please cite as:
Ehinger BV, Dimigen O: "Unfold: An integrated toolbox for overlap correction, non-linear modeling, and regression-based EEG analysis", peerJ 2019, https://doi.org/10.7717/peerj.7838
In addition, consider also citing the following reference, which illustrates the possibilites and options of unfold for a specific application example:
Dimigen O, Ehinger BV: "Regression-based analysis of combined EEG and eye-tracking data: Theory and applications. Journal of Vision, 21(1), 3-3", https://jov.arvojournals.org/article.aspx?articleid=2772164
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