![Institutional Repository [SANDBOX]](images/dias.logo.png "Institutional Repository [SANDBOX]"){kind=logo}
Institutional Repository [SANDBOX]
Technical University of Crete
Technical University of Crete
EN
|
EL
| URI | http://purl.tuc.gr/dl/dias/FBB15CB0-4FB7-48ED-A400-334B5A6A699A | - |
| Identifier | https://doi.org/10.3390/brainsci10050278 | - |
| Identifier | https://www.mdpi.com/2076-3425/10/5/278/htm | - |
| Language | en | - |
| Extent | 15 pages | en |
| Extent | 1,46 megabytes | en |
| Title | Automatic recognition of personality profiles using EEG functional connectivity during emotional processing | en |
| Creator | Dacosta-Aguayo Rosalia | en |
| Creator | Klados Manousos A. | en |
| Creator | Konstantinidi Panagiota | en |
| Creator | Kostaridou Vasiliki-Despoina | en |
| Creator | Vinciarelli, Alessandro | en |
| Creator | Zervakis Michail | en |
| Creator | Ζερβακης Μιχαηλ | el |
| Publisher | MDPI | en |
| Content Summary | 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. | en |
| Type of Item | Peer-Reviewed Journal Publication | en |
| Type of Item | Δημοσίευση σε Περιοδικό με Κριτές | el |
| License | http://creativecommons.org/licenses/by/4.0/ | en |
| Date of Item | 2021-08-06 | - |
| Date of Publication | 2020 | - |
| Subject | Big-Five factor model | en |
| Subject | Brain functional connectivity | en |
| Subject | Electroencephalogram signal processing | en |
| Subject | Emotional processing | en |
| Subject | Neuroscience | en |
| Subject | Personality detection | en |
| Bibliographic Citation | M. 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/brainsci10050278 | en |
Copyright © DIAS 2013
