Improved Hybrid Deep Convolutional Attention Neural Networks with Fibroblast Optimization for Automated Epileptic Seizure Detection in EEG Signals

This study proposes an advanced machine learning architecture combining deep convolutional neural networks with graph attention mechanisms and fibroblast optimization algorithms for detecting epileptic seizures from EEG signals. The method uses preprocessing and feature extraction techniques to reduce noise and capture time-frequency characteristics, aiming to improve accuracy and generalization over traditional approaches.