Nombre: DANIEL RIBEIRO CANDEIA
Fecha de publicación: 27/05/2025
Junta de examinadores:
Nombre |
Papel |
|---|---|
| KLAUS FABIAN COCO | Examinador Interno |
| PATRICK MARQUES CIARELLI | Presidente |
| RICHARD JUNIOR MANUEL GODINEZ TELLO | Examinador Externo |
Sumario: Electroencephalogram (EEG) is a non-invasive and cost-effective technique widely used to study brain activity and diagnose neurological disorders. However, visual analysis of EEG signals is complex and requires expertise, highlighting the need for automated diagnostic support systems. In this context, this study proposes a graph-based neural network model for detecting mental disorders using EEG signals, leveraging microstate analysis. The proposed model integrates graph neural networks (GNNs) with microstate analysis, which captures transient and stable patterns of brain activity. The TUH Abnormal EEG Corpus (TUAB) dataset, containing normal and abnormal EEG signals, was used. The process included the extraction of microstates, the construction of graphs based on Spearman correlation between EEG channels, the extraction of features from EEG signals, and the application of Principal Component Analysis (PCA) to reduce the dimensionality of these features. Three GNNs were trained, each associated with signals from each microstate, and their outputs were combined using an ensemble technique. The final model achieved an accuracy of 97.46% on the test set, outperforming existing results of methods in the literature. The results highlight the effectiveness of the proposed approach, demonstrating the potential of GNNs and microstate analysis for detecting mental disorders from EEG signals.
