  Diagnosing epilepsy requires accurate seizure detection and classification,
but traditional manual EEG signal analysis is resource-intensive. Meanwhile,
automated algorithms often overlook EEG's geometric and semantic properties
critical for interpreting brain activity. This paper introduces NeuroGNN, a
dynamic Graph Neural Network (GNN) framework that captures the dynamic
interplay between the EEG electrode locations and the semantics of their
corresponding brain regions. The specific brain region where an electrode is
placed critically shapes the nature of captured EEG signals. Each brain region
governs distinct cognitive functions, emotions, and sensory processing,
influencing both the semantic and spatial relationships within the EEG data.
Understanding and modeling these intricate brain relationships are essential
for accurate and meaningful insights into brain activity. This is precisely
where the proposed NeuroGNN framework excels by dynamically constructing a
graph that encapsulates these evolving spatial, temporal, semantic, and
taxonomic correlations to improve precision in seizure detection and
classification. Our extensive experiments with real-world data demonstrate that
NeuroGNN significantly outperforms existing state-of-the-art models.
