An improved algorithm for model-based analysis of evoked skin conductance responses.
Model-based analysis of psychophysiological signals is more robust to noise - compared to standard approaches - and may furnish better predictors of psychological state, given a physiological signal. We have previously established the improved predictive validity of model-based analysis of evoked skin conductance responses to brief stimuli, relative to standard approaches. Here, we consider some technical aspects of the underlying generative model and demonstrate further improvements. Most importantly, harvesting between-subject variability in response shape can improve predictive validity, but only under constraints on plausible response forms. A further improvement is achieved by conditioning the physiological signal with high pass filtering. A general conclusion is that precise modelling of physiological time series does not markedly increase predictive validity; instead, it appears that a more constrained model and optimised data features provide better results, probably through a suppression of physiological fluctuation that is not caused by the experiment.
Wellcome Trust Centre for Neuroimaging, University College London, United Kingdom; Berlin School of Mind and Brain, Humboldt University Berlin, Germany; Zurich University Hospital for Psychiatry, Switzerland. Electronic address: dominik.bach@uzh.ch.
Dominik R
DR
Bach
Karl J
KJ
Friston
Raymond J
RJ
Dolan
D000465:Algorithms; D004644:Emotions / Q000502:physiology; D005712:Galvanic Skin Response / Q000502:physiology*; D006801:Humans; D011603:Psychophysiology; D012984:Software
Electrodermal activity (EDA); Galvanic skin response (GSR); General linear convolution model (GLM); Generative model; Model inversion; Skin conductance responses (SCR)