NeuroGNN is a state-of-the-art framework for precise seizure detection and classification from EEG data. It employs dynamic Graph Neural Networks (GNNs) to capture intricate spatial, temporal, semantic, and taxonomic correlations between EEG electrode locations and brain regions, resulting in improved accuracy. Presented at PAKDD '24.
python machine-learning bioinformatics ml neuroscience health eeg healthcare brain correlations gnns graph-neural-networks pakdd disease-prediction gnn seizure-detection w4h seizure-classification dynamic-gnns pakdd-24
-
Updated
Aug 7, 2024 - Jupyter Notebook