Revealing brain connectivity: graph embeddings for EEG representation learning and comparative analysis of structural and functional connectivity.

ABSTRACT: This study employs deep learning techniques to present a compelling approach for modeling brain connectivity in EEG motor imagery classification through graph embedding. The compelling aspect of this study lies in its combination of graph embedding, deep learning, and different brain connectivity types, which not only enhances classification accuracy but also enriches the understanding of brain function. The approach yields high accuracy, providing valuable insights into brain connections and has potential applications in understanding neurological conditions. The proposed models consist of two distinct graph-based convolutional neural networks, each leveraging different types of brain connectivities to enhance classification performance and gain a deeper understanding of brain connections

SUBMITTER: Almohammadi A 

PROVIDER: S-EPMC10804888 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature