gmsEDA: Decomposition of Electrodermal Activity Signals Using Matrix Separation

Abstract

Electrodermal activity (EDA) signals, which reflect sympathetic nervous system arousal through changes in skin conductance, are widely used in psychological and behavioral research. Decomposing an observed EDA signal into its slowly varying tonic baseline and stimulus-driven phasic component is an important preprocessing step; however, existing methods process signals in isolation and remain highly sensitive to noise and motion artifacts. This work introduces gmsEDA, a new decomposition method based on generalized matrix separation whose model is designed to cope with noise and motion artifacts. Our method analyzes multiple recordings jointly rather than one at a time, taking advantage of patterns shared across signals to produce more accurate and robust results. Numerical experiments on both simulated and real data shows that this approach outperforms existing standard tools.