Decomposition of skin conductance data by means of nonnegative deconvolution

TLDR

Skin conductance data consist of overlapping phasic responses superimposed on a tonic component, and the variability of SCR shapes complicates their proper decomposition. The study proposes a method for fully decomposing SC data into tonic and phasic components. Nonnegative deconvolution is employed to separate SC into discrete compact responses while detecting deviations from a standard SCR shape that may arise from pore opening. A two‑compartment diffusion model accurately captures a standard SCR shape via sweat diffusion, and precise response parameters can be estimated from isolated SCRs in a paradigm with varying inter‑stimulus intervals.

Abstract

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.