2023年12月28日 · In this approach, the graph is built by computing the similarities between the features extracted from the EEG data of each sample, thus the GNN is used to propagate the …

In this study, we address these challenges by (1) representing the spatiotemporal dependencies in EEGs using a graph neural network (GNN) and proposing two EEG graph structures that …

2023年11月1日 · The embedding from each GNN is extracted according to the following: The input before getting processed by the GNN is referred to as F (0) ∈ R 19 × 13 where each row …

Graph neural networks (GNN) are increasingly used to classify EEG for tasks such as emotion recognition, motor imagery and neurological diseases and disorders. A wide range of methods …

2023年9月27日 · To further facilitate research in this direction, we introduce GNN4EEG, a versatile and user-friendly toolkit for GNN-based modeling of EEG signals. GNN4EEG …

2025年3月7日 · Electroencephalography (EEG) can non-invasively measure neuronal events and reflect brain activity at different locations on the scalp. Early studies for EEG signal processing …

2025年3月15日 · In this study, we employed PCC, MSC, and PLV to calculate connectivity between EEG channels and construct graph structures as input to the GNN. These three …

To further facilitate research in this direction, we introduce GNN4EEG, a versatile and user-friendly toolkit for GNN-based modeling of EEG signals. GNN4EEG comprises three …

2024年5月1日 · Yet, existing GNN studies on EEG data present a significant limitation: they either assume a static graph structure based solely on EEG sensor distances or generate dynamic …

EEG-GNN properly maps the network of the brain as a graph, where each electrode used to collect EEG data according to intl. 10-5 system represents a node in the graph and time …