Electrodermal Activity Processing

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Electrodermal Activity

The electrodermal activity (EDA) is a reliable physiological signal for monitoring the sympathetic nervous system and manifests itself as a change in electrical properties of the skin. Several studies have demonstrated that EDA can be a source of effective markers for the assessment of emotional states in humans. There are two main methods for measuring EDA: even though the DC approach is the most widely used in the commercial devices, the admittance contribution of EDA, estimated through and AC approach, can affect the EDA statistical power in inferring on the subject’s arousing level.


In addition to an efficient hardware, the analysis of EDA signals needs very powerful tools. The novel cvxEDA tool implemented a model that 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. 

Our researches investigate on several EDA application scenarios, especially related to two specific research fields: emotion recognition and assessment of mood/mental disorder. Indeed, emotions and mental disorders are strictly and intrinsically interrelated; therefore, when emotions are dysregulated, mental health is not guaranteed. Affective experiences accompany all cognitive processes and social activities even in the case of psychopathologies. Moreover, prevalent theories affirm that the emotional processes can have primacy over cognition.

In our research activity, several experimental results were gathered from testing our and other EDA models to robustness to noise, ability to separate and identify exogenous stimuli, and capability of properly describing the activity of the autonomic nervous system in response to specific affective elicitation are reported in detail.

Concerning the affective elicitation paradigm, we show applications of EDA modeling on data gathered from healthy subjects undergoing multimodal affective elicitation, where visual, auditory, olfactory, and tactile stimuli were investigated.

Concerning the mental health scenario, EDA analysis was employed to assess patients with bipolar disorder, who experienced depressive and manic or hypomanic episodes. Data used for this study were acquired in the frame of a European collaborative project called PSYCHE (personalized monitoring systems for care in mental health). 


cvxEDA code: 






Computational Physiology & Biomedical Instruments Group, University of Pisa


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