EEG Functional Connectivity Measures for Emotional Processing Analysis (2019)
ABSTRACT: Emotional processing (EP) is necessary for the analysis of everyday situations and for the expression of social cognition and behavior (SCB) patterns. In ex-combatants, EP is affected by chronic exposure to violent events. For a successful reintegration into society, it is necessary to characterize their brain responses to emotional stimuli, as a ﬁrst stage to develop interventions in mental health. In the present work, we present three approaches to assess emotional processing and its relation with SCB dimensions, such as aggression and social skills, in a sample of 50 subjects, 30 ex-combatants from illegally armed groups in Colombia and 20 controls without combat experience. To achieve this objective, we use EEG data from an emotion recognition task for faces and words. In the ﬁrst approach, we implement a SVM classiﬁer using features extracted from event-related potentials. Classiﬁcation rate is improved by incorporating SCB features. For the second approach, we extract features from functional connectivity network(FCN)todiscriminatetheneuralreorganizationinex-combatantsfromcontrols. Inthis approach, we use a feature fusion scheme based on canonical correlation analysis for integrating SCB scores. Finally, we perform a canonical correlation analysis to explore relations among FCN and behavioral variables related to performance in the task. In general, the proposed approaches provide new empirical knowledge on the atypical EP in ex-combatants elicited by a neural reorganization.