Emotion Analysis for Personality Inference from EEG Signals
The stable relationship between personality and EEG ensures the feasibility of personality inference from brain activities. In this paper, we recognize an individual's personality traits by analyzing brain waves when he or she watches emotional materials. Thirty seven participants took part in this study and watched 7 standardized film clips that characterize real life emotional experiences and target seven discrete emotions. Features extracted from EEG signals and subjective ratings enter the SVM classifier as inputs to predict five dimensions of personality traits. Our model achieves better classification performance for Extraversion (81.08 percent) Agreeableness (86.11 percent) and Conscientiousness (80.56 percent) when positive emotions are elicited than negative ones, higher classification accuracies for Neuroticism (78.38 81.08 percent) when negative emotions, except disgust, are evoked than positive emotions, and the highest classification accuracy for Openness (83.78 percent) when a disgusting film clip is presented. Additionally, the introduction of features from subjective ratings increases not only classification accuracy in all five personality traits (ranging from 0.43 percent for Conscientiousness to 6.3 percent for Neuroticism) but also the discriminative power of the classification accuracies between five personality traits in each category of emotion. These results demonstrate the advantage of personality inference from EEG signals over state of the art explicit behavioral indicators in terms of classification accuracy.
IEEE Transactions on Affective Computing