Dataset Information

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

ABSTRACT: 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.

SUBMITTER: Klados MA

PROVIDER: S-EPMC7288068 | biostudies-literature | 2020 May

REPOSITORIES: biostudies-literature

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

Klados Manousos A MA Konstantinidi Panagiota P Dacosta-Aguayo Rosalia R Kostaridou Vasiliki-Despoina VD Vinciarelli Alessandro A Zervakis Michalis M

Brain sciences 20200503 5

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 electroencephal ...[more]

PMID: 32375222

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Project description:BackgroundAutism spectrum disorder (ASD) is a neurodevelopmental disorder characterized by impairments in social cognition. The biological basis of deficits in social cognition in ASD, and their difficulty in processing emotional face information in particular, remains unclear. Atypical communication within and between brain regions has been reported in ASD. Interregional phase-locking is a neurophysiological mechanism mediating communication among brain areas and is understood to support cognitive functions. In the present study we investigated interregional magnetoencephalographic phase synchronization during the perception of emotional faces in adolescents with ASD.MethodsA total of 22 adolescents with ASD (18 males, mean age =14.2 ± 1.15 years, 22 right-handed) with mild to no cognitive delay and 17 healthy controls (14 males, mean age =14.4 ± 0.33 years, 16 right-handed) performed an implicit emotional processing task requiring perception of happy, angry and neutral faces while we recorded neuromagnetic signals. The faces were presented rapidly (80 ms duration) to the left or right of a central fixation cross and participants responded to a scrambled pattern that was presented concurrently on the opposite side of the fixation point. Task-dependent interregional phase-locking was calculated among source-resolved brain regions.ResultsTask-dependent increases in interregional beta synchronization were observed. Beta-band interregional phase-locking in adolescents with ASD was reduced, relative to controls, during the perception of angry faces in a distributed network involving the right fusiform gyrus and insula. No significant group differences were found for happy or neutral faces, or other analyzed frequency ranges. Significant reductions in task-dependent beta connectivity strength, clustering and eigenvector centrality (all P <0.001) in the right insula were found in adolescents with ASD, relative to controls.ConclusionsReduced beta synchronization may reflect inadequate recruitment of task-relevant networks during emotional face processing in ASD. The right insula, specifically, was a hub of reduced functional connectivity and may play a prominent role in the inability to effectively extract emotional information from faces. These findings suggest that functional disconnection in brain networks mediating emotional processes may contribute to deficits in social cognition in this population.

Dataset Information

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

Publications

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

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