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

1
Department of Psychology, University of Sheffield, International Faculty, CITY College, 54453 Thessaloniki, Greece
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School of Life and Health Science, Aston University, Birmingham 30511, UK
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Department of Electrical and Computer Engineering, Technical University of Crete, 73100 Chania, Greece
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Department of Clinical Psychology and Psychobiology, The University of Barcelona, 731 33 Barcelona, Spain
5
School of Computing Science, University of Glasgow, Glasgow G12 8QQ, UK
*
Author to whom correspondence should be addressed.
Brain Sci. 2020, 10(5), 278; https://doi.org/10.3390/brainsci10050278
Received: 26 March 2020 / Revised: 27 April 2020 / Accepted: 27 April 2020 / Published: 3 May 2020
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. View Full-Text
Keywords: Big-Five factor model; brain functional connectivity; electroencephalogram signal processing; emotional processing; neuroscience; personality detection Big-Five factor model; brain functional connectivity; electroencephalogram signal processing; emotional processing; neuroscience; personality detection
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Klados, M.A.; Konstantinidi, P.; Dacosta-Aguayo, R.; Kostaridou, V.-D.; Vinciarelli, A.; Zervakis, M. Automatic Recognition of Personality Profiles Using EEG Functional Connectivity during Emotional Processing. Brain Sci. 2020, 10, 278.

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