ospEDA: Orthogonal Subspace Projection for Electrodermal Activity Decomposition.

Abstract

Electrodermal activity (EDA) is a widely used physiological signal for assessing sympathetic nervous system activity, such as arousal, stress, and pain. However, reliable decomposition into tonic and phasic components remains challenging, particularly in noisy environments and across diverse experimental datasets. We propose ospEDA, a novel Orthogonal Subspace Projection (OSP)-based method for EDA decomposition. The method integrates (1) tonic estimation via physiologically motivated valley detection for noise robustness; (2) phasic extraction using OSP to accommodate inter-subject variability; and (3) phasic driver estimation through non-negative least squares (NNLS) deconvolution with ridge regularization. We evaluated ospEDA on five real-world datasets and one simulated EDA dataset with ground-truth components, comparing its performance against six existing methods. In simulations with a 20 dB signal-to-noise ratio (SNR), ospEDA demonstrated competitive performance, achieving root mean square error (RMSE) for estimated tonic (0.131) and phasic (0.132) components. Under noisier conditions (10 dB SNR), it maintained strong phasic RMSE (0.293), Pearson correlation (0.782), and R² (0.979) values. Furthermore, ospEDA consistently provided the highest F1-scores (0.573, 0.617, 0.638) for sympathetic nerve activity detection across 10, 20, and 30 dB SNR levels, respectively, compared to existing methods. On the real-world datasets, ospEDA achieved a mean AUROC of 0.765 for baseline vs. stimulus separation and consistently maintained strong effect sizes (ω² > 0.14) across all five datasets. Overall, ospEDA represents a promising framework for EDA decomposition, showing generally consistent performance and reliable phasic driver estimation under the varying noise conditions, with potential utility for real-world physiological monitoring applications.