ارائه يك مدل يادگيري ماشين براي ارزيابي صفات شخصيتي پنج عاملي با استفاده از سيگنال EEG در پاسخ به محرك‌هاي ويدئويي

Personality, as one of the most fundamental constructs in psychology, determines the distinct an‎d stable patterns of thought, emotion, an‎d behavior in each individual. In this context, the Five-Factor Model (Big Five) is widely accepted as a comprehensive an‎d consensual framework for describing personality structure. Despite the significance of this model, traditional assessment methods, often based on self-report questionnaires, are susceptible to bias an‎d social desirability due to their subjective nature, challenging the objectivity of the results. These limitations underscore the necessity of developing objective an‎d physiology-based methods for more accurate an‎d reliable personality assessment. In recent years, electroencephalography (EEG) signals, as direct an‎d non-invasive indicators of brain activity, have drawn particular attention for the objective assessment of personality traits. However, resting-state brain patterns associated with personality may not be sufficiently distinct. Therefore, in this study, to accentuate brain responses, the assessment of Big Five personality traits was investigated using EEG signals recorded during the observation of video stimuli. The aim of this research was to present a machine learning model for the binary classification of each of the five personality traits, based on extracting time-domain, frequency-domain, an‎d entropy features from EEG signals. The results showed that the proposed model achieved high accuracy in trait classification, with accuracies of 90.91% for Openness to Experience, 93.94% for Emotional Stability, 93.94% for Conscientiousness, 90.91% for Extraversion, an‎d 87.88% for Agreeableness. These findings confirm that EEG signals within a specific emotional context contain sufficient information to differentiate personality traits an‎d can address the limitations of traditional questionnaires. The best classification performance was obtained for Openness to Experience an‎d Emotional Stability, emphasizing the importance of employing emotional stimuli in personality assessment.

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