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

The main objective of the present study was to investigate the effect of preceding pictorial stimulus on the emotional autonomic responses of the subsequent one.

To this effect, physiological signals, including Electrocardiogram (ECG), Pulse Rate (PR), and Galvanic Skin Response (GSR) were collected. As these signals have random and chaotic nature, nonlinear dynamics of these physiological signals were evaluated with the methods of nonlinear system theory. Considering the hypothesis that emotional responses are usually associated with previous experiences of a subject, the subjective ratings of 4 emotional states were also evaluated. Four nonlinear characteristics (including Detrended Fluctuation Analysis (DFA), based parameters, Lyapunov exponent, and approximate entropy) were implemented. Nine standard features (including mean, standard deviation, minimum, maximum, median, mode, the second, third, and fourth moment) were also extracted.

To evaluate the ability of features in discriminating different types of emotions, some classification approaches were appraised, of them, Probabilistic Neural Network (PNN) led to the 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.

Lower discrimination was attained when the sequence occurred in the diagonal line of valence-arousal coordinates (for instance, positive valence and positive arousal versus negative valence and negative arousal). By employing self-assessment ranks, no obvious improvement was achieved.