Por favor, use este identificador para citar o enlazar este ítem: https://hdl.handle.net/10495/37293
Título : Phenotyping Ex-Combatants From EEG Scalp Connectivity
Autor : Quintero Zea, Andrés
López Hincapié, Jose David
Smith, Keith
Trujillo Orrego, Natalia
Parra Rodríguez, Mario Alfredo
Escudero Ordoñez, Javier Antonio
metadata.dc.subject.*: Electroencefalografía
Electroencephalography
Fenotipo
Phenotype
Aprendizaje Automático
Machine Learning
Teoría de grafos
Graph theory
Cuero cabelludo
Scalp
Ex-combatants
Excombatientes
Fecha de publicación : 2018
Editorial : Institute of Electrical and Electronics Engineers
Citación : A. Quintero-Zea, J. D. LóPez, K. Smith, N. Trujillo, M. A. Parra and J. Escudero, "Phenotyping Ex-Combatants From EEG Scalp Connectivity," in IEEE Access, vol. 6, pp. 55090-55098, 2018, doi: 10.1109/ACCESS.2018.2872765.
Resumen : ABSTRACT: Being involved in war experiences may have severe consequences in mental health. This exposure has been associated in Colombian ex-combatants with risk of proactive aggression modulating emotional processing. However, the extent of the cognitive processes underlying aggressive behavior is still an open issue. In this paper, we propose a support vector machine-based processing pipeline to identify different cognitive phenotypes associated with atypical emotional processing, based on canonical correlation analysis of EEG network features, and cognitive and behavioral evaluations. Results show the existence of cognitive phenotypes associated with differences in the mean value of leaf fraction and diameter of EEG networks across groups. The ability of identifying phenotypes in these otherwise healthy subjects opens up the possibility to aid in the development of specific interventions aimed to reduce expression of proactive aggression in ex-combatants and assessing the efficacy of such interventions.
metadata.dc.identifier.eissn: 2169-3536
metadata.dc.identifier.doi: 10.1109/ACCESS.2018.2872765
Aparece en las colecciones: Artículos de Revista en Ingeniería

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