cvxEDA: A Convex Optimization Approach to Electrodermal Activity Processing

By Alberto Greco, Gaetano Valenza, Antonio Lanata, Enzo Pasquale Scilingo, Luca Citi. IEEE Transactions on Biomedical Engineering, 2016

594 citations recorded by the source corpus.

Subfields: Cardiology and Cardiovascular Medicine, Cognitive Neuroscience, Experimental and Cognitive Psychology

View Paper: https://www.semanticscholar.org/paper/103f89ac39605700f3806311c378d949beeac180

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Cardiology and Cardiovascular Medicine

1. Hugo F. Posada-Quintero
2. Ki H. Chon
3. Cem Ersoy
4. Deniz Ekiz
5. Niaz Chalabianloo
6. Yekta Said Can

In Cognitive Neuroscience

1. Hugo F. Posada-Quintero
2. Ki H. Chon

In Experimental and Cognitive Psychology

1. Cem Ersoy
2. Deniz Ekiz
3. Niaz Chalabianloo
4. Yekta Said Can

Citations in the corpus

• Innovations in Electrodermal Activity Data Collection and Signal Processing: A Systematic Review (2020, methodology): “Based on these assumptions, they extracted the tonic and phasic components of the EDA using a convex optimization problem, which was constrained by non-negativity and sparsity of the sudomotor nerve activity [57].”
• Continuous Stress Detection Using Wearable Sensors in Real Life: Algorithmic Programming Contest Case Study (2019, methodology): “We decomposed the EDA signal by applying the cvxEDA tool [51] on the EDA signal, which makes use of a convex optimization approach to decompose the EDA signal.”