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SelfEEG is an open-source Python library developed to assist researchers inconducting Self-Supervised Learning (SSL) experiments on electroencephalography(EEG) data. Its primary objective is to offer a user-friendly but highlycustomizable environment, enabling users to efficiently design and executeself-supervised learning tasks on EEG data.  SelfEEG covers all the stages of a typical SSL pipeline, ranging from dataimport to model design and training. It includes modules specifically designedto: split data at various granularity levels (e.g., session-, subject-, ordataset-based splits); effectively manage data stored with differentconfigurations (e.g., file extensions, data types) during mini-batchconstruction; provide a wide range of standard deep learning models, dataaugmentations and SSL baseline methods applied to EEG data.  Most of the functionalities offered by selfEEG can be executed both on GPUsand CPUs, expanding its usability beyond the self-supervised learning area.Additionally, these functionalities can be employed for the analysis of otherbiomedical signals often coupled with EEGs, such as electromyography orelectrocardiography data.  These features make selfEEG a versatile deep learning tool for biomedicalapplications and a useful resource in SSL, one of the currently most activefields of Artificial Intelligence.

SelfEEG: A Python library for Self-Supervised Learning in Electroencephalography