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

DC FieldValueLanguage
dc.contributor.authorEhinger, V, Benedikt
dc.contributor.authorDimigen, Olaf
dc.date.accessioned2021-12-23T16:15:58Z-
dc.date.available2021-12-23T16:15:58Z-
dc.date.issued2019
dc.identifier.issn21678359
dc.identifier.urihttps://osnascholar.ub.uni-osnabrueck.de/handle/unios/11664-
dc.description.abstractElectrophysiological 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 non-linear fashion. Examples of paradigms where systematic temporal overlap variations and low-level confounds between conditions cannot be avoided include combined electroencephalogram (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 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.
dc.description.sponsorshipEuropean Commission Horizon (H2020-FETPROACT-2014) [364 641321-socSMCs]; DFGGerman Research Foundation (DFG)European Commission [868]; The project was supported by the European Commission Horizon (H2020-FETPROACT-2014 364 641321-socSMCs). The collection of the face dataset was supported by DFG Research Group 868, project A2. The funders had no role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript.
dc.language.isoen
dc.publisherPEERJ INC
dc.relation.ispartofPEERJ
dc.subjectDECOMPOSITION
dc.subjectDECONVOLUTION
dc.subjectDYNAMICS
dc.subjectEEG
dc.subjectERP
dc.subjectFREE CLUSTER-ENHANCEMENT
dc.subjectGeneralized additive model
dc.subjectLinear modeling of EEG
dc.subjectLOCALIZATION
dc.subjectMultidisciplinary Sciences
dc.subjectNon-linear modeling
dc.subjectOpen source toolbox
dc.subjectOverlap correction
dc.subjectRegression splines
dc.subjectRegression-ERP
dc.subjectRegularization
dc.subjectScience & Technology - Other Topics
dc.subjectSTIMULUS
dc.subjectWAVE-FORMS
dc.titleUnfold: an integrated toolbox for overlap correction, non-linear modeling, and regression-based EEG analysis
dc.typejournal article
dc.identifier.doi10.7717/peerj.7838
dc.identifier.isiISI:000492140300003
dc.description.volume7
dc.contributor.orcid0000-0002-6276-3332
dc.contributor.orcid0000-0002-2507-2823
dc.contributor.researcheridW-5954-2018
dc.contributor.researcheridA-6810-2012
dc.publisher.place341-345 OLD ST, THIRD FLR, LONDON, EC1V 9LL, ENGLAND
dcterms.isPartOf.abbreviationPeerJ
dcterms.oaStatusgold, Green Submitted, Green Published
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