EEG-learning: an Integrated Python Framework for Experimental Studies Applying Causal Connectivity Inference, Preprocessing and Spectral–temporal Analysis

The proposed Pyp-EEG package is a Python open-source pipeline that makes EEG preprocessing and analysis transparent, modular, and reproducible for practitioners. The pipeline integrates artifact removal using independent component analysis (ICA) with automatic classification via ICLabel, absolute and relative spectral power computation by frequency band and region of interest (ROI), temporal analysis based on fixed windows, and automated execution of basic statistical tests. Additionally, an optional module for directed acyclic network inference using the Multiregression Dynamic Model (MDM) enables effective connectivity learning between brain regions over time, based on the power time series generated.