Personality Perception Using Scenario Based Stimulation and Physiological Signals.

Previous studies on automatic personality perception have primarily focused on a limited number of personality traits. However, in real-world situations, humans exhibit a wide range of personality traits. To overcome this limitation, a new methodology for automatic personality perception is proposed in this paper. This revised approach can predict various personality traits (17 traits) with satisfactory performance by utilizing physiological signals. The underlying concept is to stimulate participants with different emotional stimuli to elicit physiological responses in a specific scenario. Biomarkers such as Electroencephalogram (EEG), Skin Conductance, Blood Volume Pulse, and Pupil Dilation reflect an individual's personality traits. Two experiments are conducted with different scenarios, including Image/Video Stimulation and Driving Simulation, to support this study. Based on the collection of data and validation of supervised learning models, Naive Bayes outperforms other classifiers explored in this research. EEG is the most effective signal for predicting personality, although combining other signals may produce similar results. Our method accurately predicts the 17 personality traits, demonstrating significant potential for clinical research.