EEG Brain Signal Analysis Toolkit with ERP and Spectral Analysis Algorithms

This documentation presents an EEG brain signal analysis toolkit designed for extracting event-related potential (ERP) components from neural recordings. The implementation includes robust estimation of ERP waveforms along with associated single-trial latencies and amplitudes through time-domain analysis techniques. Additionally, the toolkit performs spectral analysis of ongoing brain activities using frequency-domain transformation methods. The core algorithms implemented are: - dVCA (dynamic Vector Component Analysis): A multivariate decomposition technique that separates ERP components from background EEG activity using optimized spatial filtering and temporal alignment - AESO (Adaptive Eigenvector Separation Optimization): An adaptive spectral estimation algorithm that identifies oscillatory patterns through eigenvector-based frequency decomposition This resource is particularly valuable for biomedical engineering researchers and students working with electrophysiological data. Beyond the existing codebase, the documentation could be enhanced with theoretical background on EEG signal processing, including the neurophysiological significance of different ERP components (e.g., P300, N400) and interpretation guidelines for spectral analysis results. Additional implementation details should encompass input/output specifications, data format requirements, and practical usage examples with sample datasets to facilitate rapid adoption.