Several Useful Algorithm Programs for Feature Extraction in EEG Processing

In EEG signal processing, feature extraction represents a crucial stage of analysis. During my research, I have developed and validated several valuable algorithm implementations, including wavelet entropy, LZC complexity analysis for EEG signals, and mutual information methods. These algorithms have been thoroughly tested and optimized for effective EEG feature extraction. The wavelet entropy implementation uses multi-resolution analysis to quantify signal complexity across different frequency bands, employing Daubechies wavelets for optimal time-frequency localization. The LZC algorithm calculates the complexity of EEG signals by analyzing the number of distinct patterns in the temporal sequence, implementing the original Lempel-Ziv parsing approach with efficient string matching techniques. The mutual information method computes statistical dependencies between different EEG channels using probability density estimation and logarithmic operations. These programs feature robust pre-processing steps including signal filtering, normalization, and artifact handling to ensure reliable feature extraction. I am sharing these implementations to facilitate EEG signal analysis for other researchers, hoping these resources will prove beneficial for the research community. Each algorithm includes configurable parameters and detailed documentation for adaptation to various EEG datasets and research requirements.