A multi-dimensional CNN-Bi-GRU for IoT-based brain–computer interface in early epileptic seizure detection
Abstract
The study focuses on seizure detection using EEG data from Mendeley. An early-alert IoT-BCI system is designed to simulate real-time support for patients during seizures. The proposed Multi-Dimensional CNN-Bi-GRU (MDCBG) outperforms hybrid deep learning models, achieving 97.43% accuracy, surpassing baseline EEGNet (92.17%) and CTNET (85.11%), along with models evaluated through ablation studies on seizure vs. non-seizure prediction. The proposed model, along with other models like Bi-GRU with attention, Bi-LSTM-GRU, and XGBoost, also performs well on classifying various types of seizures. SHAP analysis shows Channel 5 contributes most to predictions. An IoT-based automation system is simulated on seizure detection for triggering micro devices near the patient's environment. This approach supports early seizure warning and guides home-automation strategies to assist patients.
Fields of Study & Subjects
EEG and Brain-Computer Interfaces
Epilepsy research and treatment
ECG Monitoring and Analysis
Publication details
- Journal / source
- Biology Methods and Protocols
- Publisher
- Oxford University Press (OUP)
- Volume
- 11
- Issue
- 1
- Pages
- bpag010–bpag010
- Publication date
- 1 January 2026
- Language
- EN