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Automatic recognition of personality profiles using EEG functional connectivity during emotional processing

Dacosta-Aguayo Rosalia, Klados Manousos A., Konstantinidi Panagiota, Kostaridou Vasiliki-Despoina, Vinciarelli, Alessandro, Zervakis Michail

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URIhttp://purl.tuc.gr/dl/dias/FBB15CB0-4FB7-48ED-A400-334B5A6A699A-
Identifierhttps://doi.org/10.3390/brainsci10050278-
Identifierhttps://www.mdpi.com/2076-3425/10/5/278/htm-
Languageen-
Extent15 pagesen
Extent1,46 megabytesen
TitleAutomatic recognition of personality profiles using EEG functional connectivity during emotional processingen
CreatorDacosta-Aguayo Rosaliaen
CreatorKlados Manousos A.en
CreatorKonstantinidi Panagiotaen
CreatorKostaridou Vasiliki-Despoinaen
CreatorVinciarelli, Alessandroen
CreatorZervakis Michailen
CreatorΖερβακης Μιχαηλel
PublisherMDPIen
Content SummaryPersonality 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. en
Type of ItemPeer-Reviewed Journal Publicationen
Type of ItemΔημοσίευση σε Περιοδικό με Κριτέςel
Licensehttp://creativecommons.org/licenses/by/4.0/en
Date of Item2021-08-06-
Date of Publication2020-
SubjectBig-Five factor modelen
SubjectBrain functional connectivityen
SubjectElectroencephalogram signal processingen
SubjectEmotional processingen
SubjectNeuroscienceen
SubjectPersonality detectionen
Bibliographic CitationM. A. Klados, P. Konstantinidi, R. Dacosta-Aguayo, V.-D. Kostaridou, A. Vinciarelli, and M. Zervakis, “Automatic recognition of personality profiles using EEG functional connectivity during emotional processing,” Brain Sci., vol. 10, no. 5, May 2020. doi: 10.3390/brainsci10050278en

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