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...
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doaj-94562e49f6b24802aa67daa2837e282d2020-11-25T02:54:23ZengMDPI AGBrain Sciences2076-34252020-05-011027827810.3390/brainsci10050278Automatic Recognition of Personality Profiles Using EEG Functional Connectivity During Emotional ProcessingManousos A. Klados0Panagiota Konstantinidi1Rosalia Dacosta-Aguayo2Vasiliki-Despoina Kostaridou3Alessandro Vinciarelli4Michalis Zervakis5Department of Psychology, University of Sheffield, International Faculty, CITY College, Thessaloniki 54453, GreeceSchool of Life and Health Science, Aston University, Birmingham 30511, UKDepartment of Clinical Psychology and Psychobiology, The University of Barcelona, 731 33 Barcelona, SpainSchool of Life and Health Science, Aston University, Birmingham 30511, UKSchool of Computing Science, University of Glasgow, Glasgow G12 8QQ, UKDepartment of Electrical and Computer Engineering, Technical University of Crete, Chania 73100, GreecePersonality 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.https://www.mdpi.com/2076-3425/10/5/278Big-Five factor modelbrain functional connectivityelectroencephalogram signal processingemotional processingneurosciencepersonality detection |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Manousos A. Klados Panagiota Konstantinidi Rosalia Dacosta-Aguayo Vasiliki-Despoina Kostaridou Alessandro Vinciarelli Michalis Zervakis |
spellingShingle |
Manousos A. Klados Panagiota Konstantinidi Rosalia Dacosta-Aguayo Vasiliki-Despoina Kostaridou Alessandro Vinciarelli Michalis Zervakis Automatic Recognition of Personality Profiles Using EEG Functional Connectivity During Emotional Processing Brain Sciences Big-Five factor model brain functional connectivity electroencephalogram signal processing emotional processing neuroscience personality detection |
author_facet |
Manousos A. Klados Panagiota Konstantinidi Rosalia Dacosta-Aguayo Vasiliki-Despoina Kostaridou Alessandro Vinciarelli Michalis Zervakis |
author_sort |
Manousos A. Klados |
title |
Automatic Recognition of Personality Profiles Using EEG Functional Connectivity During Emotional Processing |
title_short |
Automatic Recognition of Personality Profiles Using EEG Functional Connectivity During Emotional Processing |
title_full |
Automatic Recognition of Personality Profiles Using EEG Functional Connectivity During Emotional Processing |
title_fullStr |
Automatic Recognition of Personality Profiles Using EEG Functional Connectivity During Emotional Processing |
title_full_unstemmed |
Automatic Recognition of Personality Profiles Using EEG Functional Connectivity During Emotional Processing |
title_sort |
automatic recognition of personality profiles using eeg functional connectivity during emotional processing |
publisher |
MDPI AG |
series |
Brain Sciences |
issn |
2076-3425 |
publishDate |
2020-05-01 |
description |
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. |
topic |
Big-Five factor model brain functional connectivity electroencephalogram signal processing emotional processing neuroscience personality detection |
url |
https://www.mdpi.com/2076-3425/10/5/278 |
work_keys_str_mv |
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