Emotion Elicitation Using Film Clips: Effect of Age Groups on Movie Choice and Emotion Rating

Colored photographic pictures that varied widely across the affective dimensions of valence (pleasant-unpleasant) and arousal (excited-calm) were each viewed for a 6-s period while facial electromyographic (zygomatic and corrugator muscle activity) and visceral (heart rate and skin conductance) reactions were measured. Judgments relating to pleasure, arousal, interest, and emotional state were measured, as was choice viewing time. Significant covariation was obtained between (a) facial expression and affective valence judgments and (b) skin conductance magnitude and arousal ratings. Interest ratings and viewing time were also associated with arousal. Although differences due to the subject's gender and cognitive style were obtained, affective responses were largely independent of the personality factors investigated. Response specificity, particularly facial expressiveness, supported the view that specific affects have unique patterns of reactivity. The consistency of the dimensional relationships between evaluative judgments (i.e., pleasure and arousal) and physiological response, however, emphasizes that emotion is fundamentally organized by these motivational parameters.

Little attention has been paid so far to physiological signals for emotion recognition compared to audiovisual emotion channels such as facial expression or speech. This paper investigates the potential of physiological signals as reliable channels for emotion recognition. All essential stages of an automatic recognition system are discussed, from the recording of a physiological dataset to a feature-based multiclass classification. In order to collect a physiological dataset from multiple subjects over many weeks, we used a musical induction method which spontaneously leads subjects to real emotional states, without any deliberate lab setting. Four-channel biosensors were used to measure electromyogram, electrocardiogram, skin conductivity and respiration changes. A wide range of physiological features from various analysis domains, including time/frequency, entropy, geometric analysis, subband spectra, multiscale entropy, etc., is proposed in order to find the best emotion-relevant features and to correlate them with emotional states. The best features extracted are specified in detail and their effectiveness is proven by classification results. Classification of four musical emotions (positive/high arousal, negative/high arousal, negative/low arousal, positive/low arousal) is performed by using an extended linear discriminant analysis (pLDA). Furthermore, by exploiting a dichotomic property of the 2D emotion model, we develop a novel scheme of emotion-specific multilevel dichotomous classification (EMDC) and compare its performance with direct multiclass classification using the pLDA. Improved recognition accuracy of 95\% and 70\% for subject-dependent and subject-independent classification, respectively, is achieved by using the EMDC scheme.