Integrating Physiological Signals with Dynamical Attention Networks for Personality Trait Analysis

In the expanding field of personality psychology, the assessment and analysis of personality traits are of paramount importance. This paper introduces a novel deep learning network designed to predict the OCEAN personality traits (openness, conscientiousness, extraversion, agreeableness, and neuroticism), leveraging physiological signals such as electroencephalogram (EEG), electrocardiogram (ECG), and galvanic skin response (GSR). The model incorporates convolutional layers and is augmented with specifically engineered attention modules that meticulously process EEG, ECG, and GSR signals. By integrating a novel blend of component-wise attention alongside gating mechanisms, the model significantly enhances feature selection and representation. This is further complemented by the inclusion of a temporal attention module that emphasizes significant timesteps in the data, ensuring a more dynamic and responsive analysis. The model’s architecture culminates in a robust and integrated feature representation, encapsulating the nuanced interplay of the processed physiological signals. This composite representation is subsequently flattened and fed into a dense module, designed for the accurate prediction of personality traits. Our approach not only marks a significant stride in understanding the intricate relationship between physiological signals and personality traits but also lays the groundwork for diverse applications. These range from advancements in psychological research and personalized health monitoring to refining human-computer interaction paradigms. By bridging the gap between physiological data and psychological traits, this research contributes to the burgeoning field of affective computing and offers a new vista in the personalized assessment of individual traits.