Unfold : an integrated toolbox for overlapcorrection, non-linear modeling, andregression-based EEG analysis

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Titel: Unfold : an integrated toolbox for overlapcorrection, non-linear modeling, andregression-based EEG analysis
Autor(en): Ehinger, Benedikt V.
Dimigen, Olaf
Zusammenfassung: Electrophysiological research with event-related brain potentials (ERPs) isincreasingly moving from simple, strictly orthogonal stimulation paradigms towardsmore complex, quasi-experimental designs and naturalistic situations that involvefast, 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 numerouscovariates, which influence the measured responses in a linear or non-linear fashion.Examples of paradigms where systematic temporal overlap variations andlow-level confounds between conditions cannot be avoided include combinedelectroencephalogram (EEG)/eye-tracking experiments during natural vision, fastmultisensory stimulation experiments, and mobile brain/body imaging studies.However, even“traditional,”highly controlled ERP datasets often contain a hiddenmix of overlapping activity (e.g., from stimulus onsets, involuntary microsaccades, orbutton presses) and it is helpful or even necessary to disentangle these componentsfor a correct interpretation of the results. In this paper, we introduceunfold,a powerful, yet easy-to-use MATLAB toolbox for regression-based EEG analyses thatcombines existing concepts of massive univariate modeling (“regression-ERPs”),linear deconvolution modeling, and non-linear modeling with the generalizedadditive model into one coherent andflexible analysis framework. The toolbox ismodular, compatible with EEGLAB and can handle even large datasets efficiently.It also includes advanced options for regularization and the use of temporal basisfunctions (e.g., Fourier sets). We illustrate the advantages of this approach forsimulated data as well as data from a standard face recognition experiment.In addition to traditional and non-conventional EEG/ERP designs,unfoldcan also beapplied to other overlapping physiological signals, such as pupillary or electrodermalresponses. It is available as open-source software at http://www.unfoldtoolbox.org.
Bibliografische Angaben: PeerJ 7:e7838 (2019)
URL: https://repositorium.ub.uni-osnabrueck.de/handle/urn:nbn:de:gbv:700-202004202896
Schlagworte: Overlap correction; Generalized additive model; Non-linear modeling; Regressionsplines; Regularization; Regression-ERP; Linear modeling of EEG; ERP; EEG; Open source toolbox
Erscheinungsdatum: 24-Okt-2019
Lizenzbezeichnung: Attribution 3.0 Germany
URL der Lizenz: http://creativecommons.org/licenses/by/3.0/de/
Publikationstyp: Einzelbeitrag in einer wissenschaftlichen Zeitschrift [article]
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