Abstract
Abstract In this work, the feasibility of time-frequency methods, namely short-time Fourier transform, Choi Williams distribution, and smoothed pseudo-Wigner-Ville distribution in the classification of happy and sad emotional states using Electrodermal activity signals have been explored. For this, the annotated happy and sad signals are obtained from an online public database and decomposed into phasic components. The time-frequency analysis has been performed on the phasic components using three different methods. Four statistical features, namely mean, variance, kurtosis, and skewness are extracted from each method. Four classifiers, namely logistic regression, Naive Bayes, random forest, and support vector machine, have been used for the classification. The combination of the smoothed pseudo-Wigner-Ville distribution and random forest yields the highest F-measure of 68.74% for classifying happy and sad emotional states. Thus, it appears that the suggested technique could be helpful in the diagnosis of clinical conditions linked to happy and sad emotional states.
Highlights
Emotion is a complex state of feeling that impacts decisionmaking, behaviour, and social interaction [1]
The representative phasic components obtained using the cvxEDA algorithm for happy and sad emotional states are shown in Fig. 2(a) and Fig. 2(e) correspondingly
A higher amplitude is observed in the phasic component of happy stimulus-response than sad stimulus-response, which may be attributed to increased sweat gland activity during happiness
Summary
Emotion is a complex state of feeling that impacts decisionmaking, behaviour, and social interaction [1]. The circumplex model of affect classifies emotions in two dimensions, namely arousal and valence. Arousal describes the intensity of emotion, whereas valence describes how pleasant or unpleasant emotion is [6]. According to this model, happiness is characterized by high arousal and valence, whereas sadness is characterized by low arousal and valence [7]. Emotions are recognized using different modalities, such as Electrodermal Activity (EDA), electrocardiogram, heart rate, electroencephalogram, blood pressure, and skin temperature. These modalities have attracted more attention since the participants do not control them. EDA is one of the most popular psychophysiological signals used for emotion recognition [8]
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