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Unfold: An integrated toolbox for overlap correction, non-linear modeling, and regression-based EEG analysis

View ORCID ProfileBenedikt V. Ehinger, Olaf Dimigen
doi: https://doi.org/10.1101/360156
Benedikt V. Ehinger
1Institute of Cognitive Science, Universität Osnabrück
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Olaf Dimigen
2Department of Psychology, Humboldt-Universität zu Berlin
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ABSTRACT

Electrophysiological research with event-related brain potentials (ERPs) is increasingly moving from simple, strictly orthogonal stimulation paradigms towards more complex, quasi-experimental designs and naturalistic situations that involve fast, multisensory stimulation and complex motor behavior. As a result, electrophysiological responses from subsequent events often overlap with each other. In addition, the recorded neural activity is typically modulated by numerous covariates, which influence the measured responses in a linear or nonlinear fashion. Examples of paradigms where systematic temporal overlap variations and low-level confounds between conditions cannot be avoided include combined EEG/eye-tracking experiments during natural vision, fast multisensory stimulation experiments, and mobile brain/body imaging studies. However, even “traditional”, highly controlled ERP datasets often contain a hidden mix of overlapping activity (e.g. from stimulus onsets, involuntary microsaccades, or button presses) and it is helpful or even necessary to disentangle these components for a correct interpretation of the results. In this paper, we introduce unfold, a powerful, yet easy-to-use MATLAB toolbox for regression-based EEG analyses that combines existing concepts of massive univariate modeling (“regression ERPs”), linear deconvolution modeling, and non-linear modeling with the generalized additive model (GAM) into one coherent and flexible analysis framework. The toolbox is modular, compatible with EEGLAB and can handle even large datasets efficiently. It also includes advanced options for regularization and the use of temporal basis functions (e.g. Fourier sets). We illustrate the advantages of this approach for simulated data as well as data from a standard face recognition experiment. In addition to traditional and non-conventional EEG/ERP designs, unfold can also be applied to other overlapping physiological signals, such as pupillary or electrodermal responses. It is available as open-source software at http://www.unfoldtoolbox.org.

Footnotes

  • Benedikt Ehinger (behinger{at}uos.de) Wachsbleiche 27 49089 Osnabrück Tel. +49 541 969-2245 Fax +49 541 969-2596 Olaf Dimigen (olaf.dimigen{at}hu-berlin.de) Unter den Linden 6 10099 Berlin Tel. +49 30 2093-4849 Fax +49 30 2093-4910

  • Author note: The authors would like to thank Bernhard Spitzer for helpful discussions about deconvolution models, as well as Peter König, Guang Ouyang, Ashima Keshava, and Scott Makeig for comments on earlier versions of this paper.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-ND 4.0 International license.
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Posted December 21, 2018.
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Unfold: An integrated toolbox for overlap correction, non-linear modeling, and regression-based EEG analysis
Benedikt V. Ehinger, Olaf Dimigen
bioRxiv 360156; doi: https://doi.org/10.1101/360156
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Unfold: An integrated toolbox for overlap correction, non-linear modeling, and regression-based EEG analysis
Benedikt V. Ehinger, Olaf Dimigen
bioRxiv 360156; doi: https://doi.org/10.1101/360156

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