Automatic Recognition of Personality Profiles Using EEG Functional Connectivity During Emotional Processing


Personality is the characteristic set of an individual’s behavioral and emotional patterns that evolve from biological and environmental factors. The recognition of personality profiles is crucial in making human–computer interaction (HCI) applications realistic, more focused, and user friendly. The ability to recognize personality using neuroscientific data underpins the neurobiological basis of personality. This paper aims to automatically recognize personality, combining scalp electroencephalogram (EEG) and machine learning techniques. As the resting state EEG has not so far been proven efficient for predicting personality, we used EEG recordings elicited during emotion processing. This study was based on data from the AMIGOS dataset reflecting the response of 37 healthy participants. Brain networks and graph theoretical parameters were extracted from cleaned EEG signals, while each trait score was dichotomized into low- and high-level using the k-means algorithm. A feature selection algorithm was used afterwards to reduce the feature-set size to the best 10 features to describe each trait separately. Support vector machines (SVM) were finally employed to classify each instance. Our method achieved a classification accuracy of 83.8% for extraversion, 86.5% for agreeableness, 83.8% for conscientiousness, 83.8% for neuroticism, and 73% for openness.

Publication DOI:
Divisions: Life & Health Sciences > Ophthalmic Research Group
Life & Health Sciences
Additional Information: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited
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Related URLs: https://www.mdp ... 6-3425/10/5/278 (Publisher URL)
PURE Output Type: Article
Published Date: 2020-05-03
Accepted Date: 2020-04-27
Authors: Klados, Manousos A. ( 0000-0002-1629-6446)
Konstantinidi, Panagiota
Dacosta-aguayo, Rosalia
Kostaridou, Vasiliki-despoina
Vinciarelli, Alessandro
Zervakis, Michalis



Version: Published Version

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