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This paper presents a method for inferring the Positive and Negative Affect Schedule (PANAS) and the BigFive personality traits of 35 participants through the analysis of their implicit responses to 16 emotional videos. The employed modalities to record the implicit responses are (i) EEG, (ii) peripheral physiological signals (ECG, GSR), and (iii) facial landmark trajectories. The predictions of personality traits/PANAS are done using linear regression models that are trained independently on each modality. The main findings of this study are that: (i) PANAS and personality traits of individuals can be predicted based on the users' implicit responses to affective video content, (ii) ECG+GSR signals yield 70%±8% F1-score on the distinction between extroverts/introverts, (iii) EEG signals yield 69%±6% F1-score on the distinction between creative/non creative people, and finally (iv) for the prediction of agreeableness, emotional stability, and baseline affective states we achieved significantly higher than chance-level results.

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We present a novel framework for decoding individuals' emotional state and personality traits based on physiological responses to affective movie clips. During watching 36 video clips we used measures of Electrocardiogram (ECG), Galvanic Skin Response (GSR), facial-Electroencephalogram (EEG) and facial emotional responses to decode i) the emotional state of participants and ii) their Big Five personality traits extending previous work that had connected either explicit (user ratings) with some implicit (physiological) affective responses or one of them with selected personality traits.We make the first dataset comprising both affective and personality information publicly available for further research and we further explore different methods and implementations for automated emotion and personality detection for future applications.

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