Affective Visual Stimuli: Characterization of the Picture Sequences Impacts by Means of Nonlinear Approaches

introduction: the main objective of the present study was to investigate the effect of precedingpictorial stimulus on the emotional autonomic responses of the subsequent one. methods: to this effect, physiological signals, including electrocardiogram (ecg), pulserate (pr), and galvanic skin response (gsr) were collected. as these signals have randomand chaotic nature, nonlinear dynamics of these physiological signals were evaluated with themethods of nonlinear system theory. considering the hypothesis that emotional responses areusually associated with previous experiences of a subject, the subjective ratings of 4 emotionalstates were also evaluated. four nonlinear characteristics (including detrended fluctuationanalysis (dfa), based parameters, lyapunov exponent, and approximate entropy) wereimplemented. nine standard features (including mean, standard deviation, minimum, maximum,median, mode, the second, third, and fourth moment) were also extracted. results: to evaluate the ability of features in discriminating different types of emotions, someclassification approaches were appraised, of them, probabilistic neural network (pnn) led tothe best classification rate of 100%. the results show that considering the emotional sequences,gsr is the best candidate for the representation of the physiological changes. discussion: lower discrimination was attained when the sequence occurred in the diagonal lineof valence-arousal coordinates (for instance, positive valence and positive arousal versus negativevalence and negative arousal). by employing self-assessment ranks, no obvious improvementwas achieved.