Separating individual skin conductance responses in a short interstimulus-interval paradigm

Alexander, David; Trengove, Chris; Johnston, Paul A.; Cooper, Thomas J.; August, J.P.; Gordon, Evian

doi:10.1016/j.jneumeth.2005.02.001

Cited by 141 publications

(151 citation statements)

References 16 publications

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“…different system parameters). This finding raises the question of adequacy of the assumption that mathematical models of the SCR process describe the sudomotor innervation and sweat gland activity (Alexander et al, 2005;Bach et al, 2009Bach et al, , 2010aBach et al, , 2010bBach et al, , 2011Benedek & Kaernbach, 2010). The doubt has been recently expressed by Bach and Fig.…”

Section: Evidence For the Brain Mechanisms Involvement In The Scr Modelmentioning

confidence: 99%

“…The published models differ in the neurobiological interpretation of the system's parameters. While several research groups consider the peripheral sympathetic sudomotor nervous signal as the impulse input in their models of the SCR (Alexander et al, 2005;Bach et al, 2009Bach et al, , 2010aBach et al, , 2010bBach et al, , 2011Benedek & Kaernbach, 2010), we assumed that the initial neural event and feedback regulatory mechanisms that we could comprise with our models took place in central brain structures (Branković, 2008). In the last case we would be able to derive much more information about the central neural processing from the SCR signal than we used to do, and, what is even more important, to infer about the brain mechanisms in a way that is not achievable by any other available method including invasive ones.…”

Section: The Mathematical Model Of the Scr In The Context Of Neurobiomentioning

confidence: 99%

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Assessment of Brain Monoaminergic Signaling Through Mathematical Modeling of Skin Conductance Response

Branković¹

2012

Neuroscience - Dealing With Frontiers

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Section: Evidence For the Brain Mechanisms Involvement In The Scr Modelmentioning

confidence: 99%

Section: The Mathematical Model Of the Scr In The Context Of Neurobiomentioning

confidence: 99%

Assessment of Brain Monoaminergic Signaling Through Mathematical Modeling of Skin Conductance Response

Branković¹

2012

Neuroscience - Dealing With Frontiers

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“…The phasic component is the result of a convolution between the SMNA, p, and an impulse response h(t) shaped like a biexponential Bateman function [52][53][54]:…”

Section: Eda Processing Using Cvxeda Algorithmmentioning

confidence: 99%

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

et al. 2016

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Abstract:The electrodermal activity (EDA) is a reliable physiological signal for monitoring the sympathetic nervous system. 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: endosomatic (internal electrical source) and exosomatic (external electrical source). Even though the exosomatic approach is the most widely used, differences between alternating current (AC) and direct current (DC) methods and their implication in the emotional assessment field have not yet been deeply investigated. This paper aims at investigating how the admittance contribution of EDA, studied at different frequency sources, affects the EDA statistical power in inferring on the subject's arousing level (neutral or aroused). To this extent, 40 healthy subjects underwent visual affective elicitations, including neutral and arousing levels, while EDA was gathered through DC and AC sources from 0 to 1 kHz. Results concern the accuracy of an automatic, EDA feature-based arousal recognition system for each frequency source. We show how the frequency of the external electrical source affects the accuracy of arousal recognition. This suggests a role of skin susceptance in the study of affective stimuli through electrodermal response.

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“…In addition to decomposing the phasic signal into individual SCRs, these models often attempt to estimate the ANS activity by searching for the most likely input signal which could explain the observed output (the measured SC). The first LTI model was presented by Alexander et al [12]. Their method permits the estimation of the sudomotor nerve activity (SMNA) using a model where the SC is the result of a convolution between discrete bursting episodes of the SMNA and a biexponential impulse response function (IRF) assumed known a priori and time invariant.…”

Section: Introductionmentioning

confidence: 99%

“…They adopted a biexponential IRF, called the Bateman function. Although observation noise is not formally modelled in any of these methods [12]- [14], all of three assume its existence. They estimate a noisy SMNA and then recover a filtered phasic component using a low-pass filter and a subsequent heuristic and prefixed peak-detection scheme.…”

Section: Introductionmentioning

confidence: 99%

cvxEDA: a Convex Optimization Approach to Electrodermal Activity Processing

Greco

Valenza

Lanatà

et al. 2016

IEEE Trans. Biomed. Eng.

321

320

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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.

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Separating individual skin conductance responses in a short interstimulus-interval paradigm

Cited by 141 publications

References 16 publications

Assessment of Brain Monoaminergic Signaling Through Mathematical Modeling of Skin Conductance Response

Assessment of Brain Monoaminergic Signaling Through Mathematical Modeling of Skin Conductance Response

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

cvxEDA: a Convex Optimization Approach to Electrodermal Activity Processing

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