Nonlinear EEG Signal Processing with MATLAB Implementation

This MATLAB-implemented source code processes EEG data organized in two columns and incorporates sophisticated nonlinear feature extraction algorithms including Renyi entropy, wavelet entropy, and Lempel-Ziv complexity (LZC). The implementation features efficient computation methods for each algorithm: Renyi entropy calculation utilizes probability distribution estimation of EEG signals, wavelet entropy employs multi-resolution decomposition through wavelet transforms, while LZC complexity implements sequence complexity analysis using binary conversion and dictionary-based compression principles. The package includes a comprehensive topographical brain mapping tool that generates 2D scalp visualizations using interpolation methods and electrode position data. Designed specifically for EEG analysis, this codebase enables researchers to extract multiple nonlinear characteristics from neural signals and presents the analytical results through intuitive topographic representations. The MATLAB implementation provides researchers with robust tools to gain deeper insights into EEG patterns and extract clinically relevant information from brain signal data.