EEG-based functional connectivity analysis for cognitive impairment classification
Understanding how mild cognitive impairment affects global neural networks may explain changes in brain electrophysiology. Using graph theory and the visual oddball paradigm, we evaluated the functional connectivity of neuronal networks in brain lobes. The study involved 30 participants: 14 with mil...
- Autores:
-
Isabel Echeverri-Ocampo
Echeverri Ocampo, Isabel Cristina
Ardila, Karen
José Molina-Mateo
Molina-Mateo, Jose
Padilla-Buritica, J. I.
Carceller, Héctor
Barceló-Martinez, Ernesto A.
Llamur, S. I.
Maria de la Iglesia-Vaya
de la Iglesia Vaya, Maria
- Tipo de recurso:
- Article of investigation
- Fecha de publicación:
- 2023
- Institución:
- Corporación Universidad de la Costa
- Repositorio:
- REDICUC - Repositorio CUC
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.cuc.edu.co:11323/13551
- Acceso en línea:
- https://hdl.handle.net/11323/13551
https://repositorio.cuc.edu.co/
- Palabra clave:
- Brain networks
Computational modeling
EEG
Neurodegenerative disease
Machine learning
Change point detection
- Rights
- openAccess
- License
- Atribución 4.0 Internacional (CC BY 4.0)
| Summary: | Understanding how mild cognitive impairment affects global neural networks may explain changes in brain electrophysiology. Using graph theory and the visual oddball paradigm, we evaluated the functional connectivity of neuronal networks in brain lobes. The study involved 30 participants: 14 with mild cognitive impairment (MCI) and 16 healthy control (HC) participants. We conducted an examination using the visual oddball paradigm, focusing on electroencephalography signals with targeted stimuli. Our analysis employed functional connectivity utilizing the change point detection method. Additionally, we implemented training for linear discriminant analysis, K-nearest neighbor, and decision tree techniques to classify brain activity, distinguishing between subjects with mild cognitive impairment and those in the healthy control group. Our results demonstrate the efficacy of combining functional connectivity measurements derived from electroencephalography with machine learning for cognitive impairment classification. This research opens avenues for further exploration, including the potential for real-time detection of cognitive decline in complex real-world scenarios. |
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