Alberto Greco


Alberto Greco, Ph.D., is currently a Research Fellow of Bioengineering at the University of Pisa, Pisa, Italy. He received the Graduate degree in biomedical engineering and the Ph.D. degree in automatics, robotics, and bioengineering from the University of Pisa, Pisa, Italy, in 2010 and 2015, respectively. In 2014, He has been a Visiting Fellow at the University of Essex, U.K. His research interests include statistical biomedical signal processing, machine learning, physiological modeling, wearable systems for physiological monitoring and eye-tracking systems. Applications include the assessment of autonomic nervous system and central nervous system, the affective computing and the assessment of mood and consciousness disorders. He is author of several international scientific contributions in these fields published in peer-reviewed international journals, conference proceedings, and book. He has been involved in several European research projects.




Department of Information Engineering

Faculty of Engineering, University of Pisa

Via G. Caruso 5 - 56122 - Pisa -  Italy

Tel.+39 050 2217462


cvxEDA Model: a  Convex Optimization Approach to Electrodermal Activity Processing

EDA can be considered one of the most common observation channels of sympathetic nervous system activity, and manifests itself as a change in electrical properties of the skin, such as skin conductance (SC). The proposed cvxEDA model describes SC 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. The algorithm was evaluated in three different experimental sessions to test its robustness to noise, its ability to separate and identify stimulus inputs, and its capability of properly describing the activity of the autonomic nervous system in response to strong affective stimulation. Results are very encouraging, showing good performance of the proposed method and suggesting promising future applicability, e.g., in the field of affective computing.

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