Sleep stage classification using EEG signal

In MATLAB, we classified sleep stages from EEG signals, laying groundwork for sleep research.

This project focuses on classifying sleep stages using EEG signals, employing MATLAB. EEG data undergo preprocessing to remove noise, followed by feature extraction to capture relevant patterns. The features are then used to train a support vector machine (SVM) classifier for sleep stage classification. Evaluation metrics, including accuracy, assess the performance of the classifier. This methodology contributes to understanding sleep patterns and diagnosing sleep disorders, facilitating advancements in sleep research and clinical practice. Through MATLAB's capabilities in data analysis and machine learning, this project aims to provide a robust framework for automated sleep stage classification, offering insights into sleep physiology and enhancing personalized treatment strategies for sleep-related conditions.