EEG Signal Processing with MATLAB Source Code

This document presents MATLAB source code implementation for EEG signal processing, focusing on acquisition techniques and feature extraction methodologies. The codebase incorporates digital signal processing algorithms including Fourier transforms for frequency analysis, wavelet decomposition for multi-resolution examination, and independent component analysis (ICA) for artifact removal. Key MATLAB functions such as fft(), wavedec(), and fastica() are utilized to process raw EEG data through sequential stages: preprocessing with bandpass filtering (0.5-40 Hz), epoch segmentation, feature extraction using power spectral density calculations, and pattern classification. The implementation details signal quality assessment metrics and visualization techniques through MATLAB's plotting functions for time-domain and frequency-domain representations. This systematic approach enables researchers to objectively analyze neural oscillations, event-related potentials, and cognitive state biomarkers, providing foundational methodologies for neuroscience investigations and brain-computer interface development. The processed features can subsequently facilitate machine learning applications for neurological disorder diagnosis and cognitive workload assessment.