PT  - JOURNAL ARTICLE
AU  - Benedikt V. Ehinger
AU  - Olaf Dimigen
TI  - Unfold: An integrated toolbox for overlap correction, non-linear modeling, and regression-based EEG analysis
AID  - 10.1101/360156
DP  - 2018 Jan 01
TA  - bioRxiv
PG  - 360156
4099  - http://biorxiv.org/content/early/2018/07/04/360156.short
4100  - http://biorxiv.org/content/early/2018/07/04/360156.full
AB  - 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 overlap with each other. In addition, the recorded neural activity is often 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 experiment, or 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 the existing concepts of massive univariate modeling (“regression ERPs”), linear deconvolution modeling, and generalized additive modeling 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.