EEG Signal Classification with MATLAB Implementation

This MATLAB program is designed for Electroencephalogram (EEG) signal classification. The implementation includes robust feature extraction methods such as time-domain statistics, frequency-domain analysis using FFT, and time-frequency representations through wavelet transforms. The classification module supports various machine learning algorithms including Support Vector Machines (SVM), k-Nearest Neighbors (k-NN), and Neural Networks, allowing users to compare performance metrics across different approaches. Key functions include eeg_feature_extraction() for automated feature calculation, classify_eeg() for model training and prediction, and visualize_results() for graphical representation of classification outcomes. The program incorporates cross-validation techniques to ensure model reliability and provides configurable parameters for feature selection and algorithm tuning. Researchers can leverage this tool to systematically explore optimal classification strategies through iterative experimentation with different feature sets and classifiers. The visualization capabilities generate ROC curves, confusion matrices, and feature importance plots, enabling intuitive interpretation of classification performance. This MATLAB implementation serves as a versatile platform for advancing EEG signal analysis research, offering both predefined workflows and customizable modules for specialized investigations.