Personality is the characteristic set of an individualâ??s behavioral and emotional patterns\nthat evolve from biological and environmental factors. The recognition of personality profiles is\ncrucial in making humanâ??computer interaction (HCI) applications realistic, more focused, and user\nfriendly. The ability to recognize personality using neuroscientific data underpins the\nneurobiological basis of personality. This paper aims to automatically recognize personality,\ncombining scalp electroencephalogram (EEG) and machine learning techniques. As the resting state\nEEG has not so far been proven efficient for predicting personality, we used EEG recordings elicited\nduring emotion processing. This study was based on data from the AMIGOS dataset reflecting the\nresponse of 37 healthy participants. Brain networks and graph theoretical parameters were\nextracted from cleaned EEG signals, while each trait score was dichotomized into low- and highlevel\nusing the k-means algorithm. A feature selection algorithm was used afterwards to reduce the\nfeature-set size to the best 10 features to describe each trait separately. Support vector machines\n(SVM) were finally employed to classify each instance. Our method achieved a classification\naccuracy of 83.8% for extraversion, 86.5% for agreeableness, 83.8% for conscientiousness, 83.8% for\nneuroticism, and 73% for openness.
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