Decomposition of skin conductance data by means of nonnegative deconvolution

By Mathias Benedek, Christian Kaernbach. Psychophysiology, 2010

615 citations recorded by the source corpus.

Skin conductance (SC) data are usually characterized by a sequence of overlapping phasic skin conductance responses (SCRs) overlying a tonic component. The variability of SCR shapes hereby complicates the proper decomposition of SC data. A method is proposed for full decomposition of SC data into tonic and phasic components. A two-compartment diffusion model was found to adequately describe a standard SCR shape based on the process of sweat diffusion. Nonnegative deconvolution is used to decompose SC data into discrete compact responses and at the same time assess deviations from the standard SCR shape, which could be ascribed to the additional process of pore opening. Based on the result of single non-overlapped SCRs, response parameters can be estimated precisely as shown in a paradigm with varying inter-stimulus intervals.

Subfields: Cognitive Neuroscience, Food Science

View Paper: https://www.semanticscholar.org/paper/59b889ae9ab47e77ddab31d60c07b0ba7faf5bac

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Citations in the corpus

Evaluating the impact of viewing location on view perception using a virtual environment (2020, methodology): “9 toolbox: a MATLAB-based analysis tool for extracting SCL and SCR values from the SC data, using a continuous decomposition analysis method [89,90].”
cvxEDA: A Convex Optimization Approach to Electrodermal Activity Processing (2016, methodology): “These methods are known as nonnegative deconvolution [13] and continuous deconvolution analysis (CDA) [14].”
Assessing Knowledge Retention of an Immersive Serious Game vs. a Traditional Education Method in Aviation Safety (2015, methodology): “From the EDA signal, we extracted skin conductance level (SCL) through decomposition analysis [47].”
A continuous measure of phasic electrodermal activity (2010, methodology): “Alexander et al. (2005) introduced a decomposition method by eans of deconvolution.”
Innovations in Electrodermal Activity Data Collection and Signal Processing: A Systematic Review (2020, methodology): “Another group proposed a non-negative deconvolution method to separate EDA into continuous tonic and phasic components, and obtain discrete compact responses [7,51].”