EEG-Based Brain Disease Classification: A Proposed Model Using CNN-RNN Ensemble Process

Lately, there has been a dire need to enhance EEG classification for early brain disease detection to outweigh the limitations of manual feature engineering in prevailing methods. This paper proposes an innovative approach, integrating CNNs and RNNs in an ensemble framework, with streamlined CNNs for real-time processing. The proposed model mitigates limitations in EEG classification by using CNNs for automatic feature extraction, reducing the need for manual engineering, and improving spatial information capture. RNNs enhance pattern understanding by recognizing temporal dependencies. The ensemble of CNNs and RNNs improves robustness, mitigates overfitting, and enhances generalization. Streamlined CNN architectures enable real-time processing at 100 Hz, allowing near-instantaneous disease pre-emption. This work provides healthcare experts with an efficient tool for EEG-based disease classification, contributing to early detection and improving patient outcomes and healthcare efficiency.