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...

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Bibliographic Details
Published in:Electronics (Basel) 2023-11, Vol.12 (21), p.4432
Main Authors: Echeverri-Ocampo, Isabel, Ardila, Karen, Molina-Mateo, José, Padilla-Buritica, J. I., Carceller, Héctor, Barceló-Martinez, Ernesto A., Llamur, S. I., Iglesia-Vaya, Maria de la
Format: Article
Language:English
Subjects:
Aging
Brain
Brain research
Classification
Cognition disorders
Cognitive ability
Connectivity analysis
Consent
Decision analysis
Decision trees
Dementia
Diagnosis
Discriminant analysis
Electrodes
Electroencephalography
Electrophysiology
Graph theory
Impairment
Machine learning
Neural networks
Visual signals
Visual stimuli
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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.
ISSN:2079-9292
2079-9292
DOI:10.3390/electronics12214432