cvxEDA: a Convex Optimization Approach to Electrodermal Activity Processing

Abstract

Abstract-Goal: This paper reports on a novel algorithm for the analysis of electrodermal activity (EDA) using methods of convex optimization. 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). Methods: The proposed 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. Results: 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. Significance: Results are very encouraging, showing good performance of the proposed method and suggesting promising future applicability, e.g., in the field of affective computing.

Combining electroencephalographic activity and instantaneous heart rate for assessing brain–heart dynamics during visual emotional elicitation in healthy subjects

“…We believe that the vanishing effect hereby observed at high arousing elicitation has to be related to the processing of emotions at a CNS level exclusively. At a ANS level, in fact, through the same experimental protocol, our previous findings confirm arousal-specific patterns of skin conductance [83,84], and HRV and respiratory dynamics [85], allowing for a four-class discrimination of all of the arousing visual elicitations. Statistically significant differences between sessions of the same type should be linked to the quantification of the effect of (pleasant/unpleasant) arousing stimuli to subsequent neutral elicitations and resting-state sessions.…”

Skin Admittance Measurement for Emotion Recognition: A Study over Frequency Sweep

“…The EDA signals were analyzed by means of the cvxEDA model [49]. The cvxEDA algorithm is based on the three concepts of sparsity, Bayesian statistics and convex optimization.…”

“…In this section, we first report on the EDA acquisition system prototype and then briefly describe details on the cvxEDA models, which is presented in [49]. Note that this method is able to discern overlapping consecutive electrodermal responses (EDRs), likely to be present in the case of an inter-stimulus interval shorter than the EDR recovery time.…”

“…Taking the Laplace transform of Equation (3) and then its discrete-time approximation with sampling time δ (using a bilinear transformation), we obtain an autoregressive moving average (ARMA) model (see details in [49]) that can be represented in matrix form as…”

“…CvxEDA rewrites the MAP problem as a constrained minimization Quadratic Programming (QP) convex problem (see details in [49,55] subj. to Aq ≥ 0.…”