EEG-based Graph Neural Network Classification of Alzheimer’s Disease: An Empirical Evaluation of Functional Connectivity Methods

Abstract

Alzheimer's disease (AD) is the leading form of dementia in the world. AD disrupts neuronal pathways and thus is commonly viewed as a network disorder. Many studies demonstrate the power of functional connectivity (FC) graph-based biomarkers for automated diagnosis of AD using electroencephalography (EEG). However, various FC measures are commonly utilised as each aims to quantify an unique aspect of brain coupling. Graph neural networks (GNN) provide a powerful framework for learning on graphs. While there is a growing number of studies using GNN to classify EEG brain graphs, it is unclear which method should be utilised to estimate the brain graph. We use eight FC measures to estimate FC brain graphs from sensor-level EEG signals. GNN models are trained in order to compare the performance of the selected FC measures. Additionally, three baseline models based on literature are trained for comparison. We show that GNN models perform significantly better than the other baseline models. Moreover, using FC measures to estimate brain graphs improves the performance of GNN compared to models trained using a fixed graph based on the spatial distance between the EEG sensors. However, no FC measure performs consistently better than the other measures. The best GNN reaches 0.984 area under sensitivity-specificity curve (AUC) and 92% accuracy whereas the best baseline model, a convolutional neural network, has 0.924 AUC and 84.7% accuracy.

EMPT: a sparsity Transformer for EEG-based motor imagery recognition

“…In recent years, deep neural networks have largely been applied to achieve state-of-the-art performance. Various deep learning models have been successfully employed to decode EEG signals for good performance ( Roth et al, 2016 ; Dutta, 2019 ; Jiang et al, 2021 ; Klepl et al, 2022 ). EEGNet is a compact convolutional neural network consisting of deep and spatio-temporally separated convolutions.…”

The Effect of Graph Frequencies on Dynamic Structures in Graph Signal Processing

“…A recent trend has focused on incorporating the graph structure into the data analysis, for example by using graph signal processing [5] or graph neural networks [6]. In this work, we explore the relevance of the spatial information for AD classification by balancing it against spectral and temporal information.…”