unfold

unfold performs regression-based deconvolution and non-linear modeling in MATLAB to correct overlapping event-related potentials (ERPs) and quantify covariate effects in EEG, pupillary, and electrodermal recordings.

Key Features:

• Overlap Correction: Deconvolves overlapping electrophysiological signals including EEG, pupil, and electrodermal responses to separate temporally overlapping neural and physiological activity.
• Non-linear and Linear Modeling: Supports linear models and generalized additive models (GAMs) to model non-linear covariate effects on neural responses.
• Regression-based EEG Analysis: Integrates massive univariate "regression-ERP" concepts with linear deconvolution modeling to perform regression-based analysis of EEG data.
• Modular Design: Provides compatibility with EEGLAB, handles large datasets, and includes advanced options for regularization and temporal basis functions such as Fourier sets.
• Versatility Across Experimental Designs: Applicable to traditional ERP studies and non-conventional designs, and extendable to overlapping physiological signals across diverse experimental contexts.

Scientific Applications:

• Simulated and Empirical Validation: Applied to simulated data and empirical experiments such as a standard face recognition study to validate overlap correction and modeling approaches.
• Combined EEG/Eye-Tracking during Natural Vision: Used to analyze complex datasets from combined EEG and eye-tracking experiments recorded during natural vision.
• Mobile Brain/Body Imaging: Applied in mobile brain/body imaging contexts to separate overlapping signals from rapid or naturalistic behavior.
• Disentangling Overlapping Components: Enables separation of overlapping ERP components arising from stimulus onsets, involuntary microsaccades, or rapid successive events.

Methodology:

Implements R-style formula model specification (e.g., EEG~1+face+age), regression-ERPs (massive univariate), linear deconvolution modeling, linear and generalized additive models (GAMs), temporal basis functions such as Fourier sets, regularization, and deconvolution of overlapping EEG, pupil, and electrodermal signals.