GUI based Modular EEG preprocessing pipeline for HPC

Electroencephalography (EEG) preprocessing is the initial and critical step for accurate brain function and behavior analysis. There is a pressing demand for an automatic pipeline that can process huge volumes of data, yet existing commercial software solutions are expensive, have no batch processing capabilities and offer limited automation. While open-source libraries in Python and R can be used to implement a solution, it demands extensive programming expertise in researchers who very often lack knowledge of programming and batch processing. This project introduces an accessible, scalable, EEG processing pipeline framework tailored for high-performance computing (HPC). It features a graphical user interface (GUI) with node editor based drag and drop pipeline creator with reusable modules, a Python based pipeline runner to run the serialized interpretation of pipeline leveraging MNE library, and SLURM integration for batch processing. The framework provides modules for commonly used preprocessing steps such as filtering, epoching, artifact rejection, baseline correction and advanced techniques like Independent Component Analysis (ICA) and power spectral density analysis. The framework has been supporting diverse EEG research applications including resting state, letter flanker and emotional regulation analysis at BRAINS Lab with ACCRE computing cluster at Vanderbilt University, proving its flexibility and adaptability for real world use cases. By significantly advancing the accessibility and increasing automation of EEG preprocessing, this system holds promise for reducing barriers and accelerating research in neuroscience and related fields.