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 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. It is available as open-source software at http://www.unfoldtoolbox.org.
Highlights
Event-related brain responses in the electroencephalogram (EEG) are traditionally studied in strongly simplified and strictly orthogonal stimulus-response paradigms
We introduce unfold, an open source, easy-to-use, and flexible MATLAB toolbox written to facilitate the use of advanced deconvolution models and spline regression in event-related brain potentials (ERPs) research
A simple simulation example Before we introduce a real dataset, let us first consider a simulated simple EEG/ERP study to illustrate the possibilities of the deconvolution approach
Summary
Event-related brain responses in the electroencephalogram (EEG) are traditionally studied in strongly simplified and strictly orthogonal stimulus-response paradigms. There has been a rising interest in recording brain-electric activity in more complex paradigms and naturalistic situations. Examples include laboratory studies with fast and concurrent streams of visual, auditory, and tactile stimuli (Spitzer, Blankenburg & Summerfield, 2016), experiments that combine EEG recordings with eye-tracking recordings during natural vision (Dimigen et al, 2011), EEG studies in virtual reality (Ehinger et al, 2014) or mobile brain/body imaging studies that investigate real-world interactions of freely moving participants (Gramann et al, 2014). There are two main problems in these types of situations: overlapping neural responses from subsequent events and complex influences of nuisance variables that cannot be fully controlled. Even traditional event-related brain potential (ERP) experiments often contain a mixture of overlapping neural responses, for example, from stimulus onsets, involuntary microsaccades, or manual button presses
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