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

• Published in: Frontiers in neuroscience Vol. 17; p. 1288433
• Main Authors: Almohammadi, Abdullah, Wang, Yu-Kai
• Format: Journal Article
• Language: English
• Published: Switzerland Frontiers Research Foundation 08.01.2024; Frontiers Media S.A
• Subjects: Accuracy; adjacency matrix; Algorithms; Brain; brain connectivity; Classification; Comparative analysis; Cortex (frontal); Cortex (parietal); Datasets; Deep learning; EEG; Electrodes; Embedding; functional connectivity; Graph Convolutional Neural Network; Graph representations; Mental task performance; Neural networks; Neuroscience; PLV; structural connectivity; Structure-function relationships; brain connectivity; Graph Convolutional Neural Network; adjacency matrix; functional connectivity; structural connectivity; MI-EEG; motor imagery; PLV
• ISSN: 1662-453X; 1662-4548; 1662-453X
• DOI: 10.3389/fnins.2023.1288433

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. The first model, Adjacency-based Convolutional Neural Network Model (Adj-CNNM), utilizes a graph representation based on structural brain connectivity to embed spatial information, distinguishing it from prior spatial filtering approaches dependent on subjects and tasks. Extensive tests on a benchmark dataset-IV-2a demonstrate that an accuracy of 72.77% is achieved by the Adj-CNNM, surpassing baseline and state-of-the-art methods. The second model, Phase Locking Value Convolutional Neural Network Model (PLV-CNNM), incorporates functional connectivity to overcome structural connectivity limitations and identifies connections between distinct brain regions. The PLV-CNNM achieves an overall accuracy of 75.10% across the 1–51 Hz frequency range. In the preferred 8–30 Hz frequency band, known for motor imagery data classification (including α, μ, and β waves), individual accuracies of 91.9%, 90.2%, and 85.8% are attained for α, μ, and β, respectively. Moreover, the model performs admirably with 84.3% accuracy when considering the entire 8–30 Hz band. Notably, the PLV-CNNM reveals robust connections between different brain regions during motor imagery tasks, including the frontal and central cortex and the central and parietal cortex. These findings provide valuable insights into brain connectivity patterns, enriching the comprehension of brain function. Additionally, the study offers a comprehensive comparative analysis of diverse brain connectivity modeling methods.