EEG-SSL: A Framework for Self-Supervised Learning on EEG

The rise of open-source neuroimaging data sharing has created new opportunities for advancing machine learning in neuroscience. However, this trend also highlights the need for a framework capable of leveraging these large-scale, publicly available datasets for advanced machine learning algorithms, particularly deep learning techniques. In this paper, we introduce EEG-SSL, a framework designed to work with large heterogenous EEG neuroimaging datasets shared in the standardized Brain Imaging Data Structure (BIDS) format using Self-Supervised Learning (SSL). SSL is a powerful deep-learning approach that benefits from large-scale datasets, even when unlabeled or minimally labeled. We show how information can be extracted from standardized EEG BIDS-formated data and discuss different considerations while processing at scale in our framework. We specify the software components that are needed to apply SSL to EEG and provide several standard implementations for each of the components. We also describe how each component can be extended with custom implementation while leveraging the supporting functions of the framework. We applied the framework to a large-scale BIDS-compliant dataset (ds004186) comprising resting-state EEG data from over 2,000 subjects, showcasing how SSL can be structured and deployed for potential big data in EEG.