Program Collection for Common Brain Signal Processing Algorithms

This repository contains a collection of MATLAB-based implementations for common brain signal processing algorithms. The codebase is specifically designed to help students and researchers learn signal visualization methods and techniques through practical implementation. Each algorithm includes MATLAB scripts (.m files) with detailed comments explaining key functions like signal filtering, feature extraction, and visualization commands such as plot() and spectrogram(). Students can study these implementations to gain deep insights into signal processing principles, including time-domain analysis, frequency-domain transformations, and time-frequency representations through algorithms like FFT and wavelet transforms. The collection incorporates real-case examples demonstrating practical applications in neuroscience research, complete with sample EEG/ERP datasets for hands-on experimentation. By working through these code examples, learners can develop stronger programming skills in MATLAB while building foundational knowledge for advanced research and professional applications in neural signal processing. The repository structure organizes algorithms by processing type (preprocessing, feature extraction, classification) with runnable demo scripts that illustrate complete processing pipelines from raw signal input to visualized results.