cvxEDA: a Convex Optimization Approach to Electrodermal Activity Processing
- Submitting institution
-
The University of Essex
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 1093
- Type
- D - Journal article
- DOI
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10.1109/TBME.2015.2474131
- Title of journal
- IEEE Transactions on Biomedical Engineering
- Article number
- 4
- First page
- 797
- Volume
- 63
- Issue
- 4
- ISSN
- 0018-9294
- Open access status
- Out of scope for open access requirements
- Month of publication
- August
- Year of publication
- 2015
- URL
-
-
- Supplementary information
-
-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- Yes
- Number of additional authors
-
4
- Research group(s)
-
B - Brain Computer Interfaces and Neural Engineering (BCI-NE)
- Citation count
- 106
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This highly-cited paper developed the first algorithm for the analysis of electrodermal activity using methods of Bayesian statistics and convex optimization. Our robust model was derived from physiologically grounded assumptions followed by a rigorous statistical derivation based on Bayesian statistics. There is a growing interest in electrodermal activity applications (including affective computing/smart fitness watches) and openly releasing our code has resulted (evidenced via Google search!) in integration within NeuroKit (http://neurokit.readthedocs.io/en/latest/documentation.html), a toolbox for Neurophysiological Signal Processing developed at Sorbonne University and adaption for the Adiutis (http://adiutis.com/) device for emotion and stress monitoring, developed for the European Space Agency.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -