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cvxEDA

version 1.0.0.0 (21.3 KB) by Luca Citi
Algorithm for the analysis of electrodermal activity (EDA) using convex optimization

532 Downloads

Updated 16 Feb 2017

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This program implements the cvxEDA algorithm for the analysis of electrodermal activity (EDA) using methods of convex optimization, described in:
A Greco, G Valenza, A Lanata, EP Scilingo, and L Citi
"cvxEDA: a Convex Optimization Approach to Electrodermal Activity Processing"
IEEE Transactions on Biomedical Engineering, 2015
DOI: 10.1109/TBME.2015.2474131

It is based on a model which describes EDA as the sum of three terms: the phasic component, the tonic component, and an additive white Gaussian noise term incorporating model prediction errors as well as measurement errors and artifacts.
This model is physiologically inspired and fully explains EDA through a rigorous methodology based on Bayesian statistics, mathematical convex optimization and sparsity.

Cite As

Luca Citi (2021). cvxEDA (https://github.com/lciti/cvxEDA), GitHub. Retrieved .

MATLAB Release Compatibility
Created with R2015a
Compatible with any release
Platform Compatibility
Windows macOS Linux

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To view or report issues in this GitHub add-on, visit the GitHub Repository.
To view or report issues in this GitHub add-on, visit the GitHub Repository.