gmsEDA: Decomposition of Electrodermal Activity Signals Using Matrix Separation

[2608.14732] gmsEDA: Decomposition of Electrodermal Activity Signals Using Matrix Separation Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Electrical Engineering and Systems Science > Signal Processing arXiv:2608.14732 (eess) [Submitted on 13 Aug 2026] Title: gmsEDA: Decomposition of Electrodermal Activity Signals Using Matrix Separation Authors: Xuemei Chen , David MacQueen , Wendy Donlin Washington , Mark Lammers , Owen Deen , Sean Carey , Margot Ledford View a PDF of the paper titled gmsEDA: Decomposition of Electrodermal Activity Signals Using Matrix Separation, by Xuemei Chen and 6 other authors View PDF HTML (experimental) 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. Comments: 25 pages Subjects: Signal Processing (eess.SP) Cite as: arXiv:2608.14732 [eess.SP] (or arXiv:2608.14732v1 [eess.SP] for this version) https://doi.org/10.48550/arXiv.2608.14732 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Xuemei Chen [ view email ] [v1] Thu, 13 Aug 2026 02:49:37 UTC (6,739 KB) Full-text links: Access Paper: View a PDF of the paper titled gmsEDA: Decomposition of Electrodermal Activity Signals Using Matrix Separation, by Xuemei Chen and 6 other authors View PDF HTML (experimental) TeX Source view license Current browse context: eess.SP < prev | next > new | recent | 2026-08 Change to browse by: eess References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps? ) scite.ai Toggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs . Which authors of this paper are endorsers? | Disable MathJax ( What is MathJax? ) We gratefully acknowledge support from our major funders , member institutions , , and all contributors. About · Help · Contact · Subscribe · Copyright · Privacy · Accessibility · Operational Status (opens in new tab) Major funding support from

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