Résumé
SelfEEG is an open-source Python library developed to assist researchers in conducting Self-
Supervised Learning (SSL) experiments on electroencephalography (EEG) data. Its primary
objective is to offer a user-friendly but highly customizable environment, enabling users to
efficiently design and execute self-supervised learning tasks on EEG data.
SelfEEG covers all the stages of a typical SSL pipeline, ranging from data import to model
design and training. It includes modules specifically designed to: split data at various granularity
levels (e.g., session-, subject-, or dataset-based splits); effectively manage data stored with
different configurations (e.g., file extensions, data types) during mini-batch construction;
provide a wide range of standard deep learning models, data augmentations and SSL baseline
methods applied to EEG data.
Most of the functionality offered by selfEEG can be executed both on GPUs and CPUs,
expanding its usability beyond the self-supervised learning area. Additionally, selfEEG can
be employed for the analysis of other biomedical signals often coupled with EEGs, such as
electromyography or electrocardiography data.
These features make selfEEG a versatile deep learning tool for biomedical applications and a
useful resource in SSL, one of the currently most active fields of artificial intelligence.