EEG-Classification-model

In this project, the objective is to construct a classification model designed to analyze EEG data and categorize it into distinct classes. This centers on developing a precise EEG classification model exclusively utilizing the CHB-MIT EEG Database. Dedicated to enhancing epilepsy diagnosis, the dataset features diverse seizure types and non-seizure instances. Through meticulous data preprocessing, we address nuances such as missing values, ensuring data integrity. Employing feature extraction techniques, including time and frequency domains, we aim to distill crucial information from EEG signals. The chosen deep learning model, potentially a Convolutional Neural Network (CNN) or Recurrent Neural Network (RNN), undergoes rigorous training with a focus on mitigating overfitting. Evaluation metrics such as accuracy, precision, recall, and F1-score guide our model optimization, contributing significantly to advancements in neuroscientific research and medical diagnostics.