EEGBoostNet Ensemble for IoT-Based Brain–Computer Interface in Early Epileptic Seizure Detection

The goal of this study is seizure detection in four class datasets for different seizure stages in epileptic patients. An early notification system is created to simulate the behavior of the patient experiencing a seizure receiving emergency assistance from caregivers, and a dataset acquired from Mendeley is used to train different models. The proposed EEGNet-ET-XGB model’s (EEGBoostNet) effectiveness against hybrid deep learning models is demonstrated by the outcome of 95.88% and 94.41% mean accuracy on stratified cross validation. On the full dataset, Bi-GRU with attention, bidirectional LSTM-GRU models, and conventional ensemble techniques like XGBoost can all do remarkably well. Channel 9 data is the most important feature, according to the SHAP interpretability analysis, which is conducted on several models with the aid of SHAP plots. The IoT-BCI cloud modeling is adapted to make early notifications for emergency systems. This method is essential for categorizing different seizure types according to occurrences in order to provide early warning and for developing a home automation strategy that will help victims.