IEEE

ASA-STGCN: Adaptive Sparse Awareness-Spatiotemporal Graph Convolutional Network for Multi-Class Motor Imagery EEG Classification

Abstract: Graph Convolutional Networks (GCNs) have shown promise in motor imagery electroencephalogram (EEG) signals classification by modeling spatial dynamics and brain connectivity. However, ...
IEEE

Dynamic Synchronicity Graph Learning for Spatiotemporal Graph Convolutional Networks in EEG Source Localization

Abstract: Electroencephalogram (EEG) source locali-zation aims to identify the origins of brain activity from scalp measurements, a critical task for understanding neurological processes and ...
Scientific Research Publishing

Analysis and Interpretation of EEG Signals for the Design of an Automated Diagnosis of Disorders of Consciousness and Vigilance through Artificial Neural Network ()

1 Unité de Recherche d’Automatique et d’Informatique Appliquée (UR-AIA), IUT of Bandjoun, University of Dschang, Dschang, Cameroun. 2 Unité de Recherche de Matière Condensée d’Electronique et de ...
pv magazine International

PV module fault diagnosis uses convolutional neural network

Researchers in China have created a dataset of various PV faults and normalized it to accommodate different array sizes and typologies. After testing the new approach in combination with the 1D-CNN ...