Withdrawn paper details GCN-based EEG seizure detection

A research paper, now withdrawn, proposed a framework for detecting epileptic seizures using Graph Convolutional Neural Networks (GCNs) applied to electroencephalogram (EEG) signals. The method involved decomposing EEG signals into five frequency bands and extracting features before feeding them into a GCN to model spatial dependencies. Experiments on the CHB-MIT dataset showed high accuracy, particularly in mid-frequency bands, suggesting improved interpretability and diagnostic precision over traditional broadband methods. AI

RANK_REASON The cluster contains a withdrawn academic paper detailing a novel research methodology. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

• CHB-MIT
• Delta
• electroencephalogram
• Gahangir Hossain
• graph convolutional network
• Graph Convolutional Neural Networks for Predicting Drug-Target Interactions
• Higher beta
• Theta

• paper
• other

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →