BIDSAlign: a library for automatic merging and preprocessing of multiple EEG repositories.

This study aims to address the challenges associated with data-driven electroencephalographic (EEG) data analysis by introducing a standardised library called BIDSAlign. This library efficiently processes and merges heterogeneous EEG datasets from different sources into a common standard template. The goal of this work is to create an environment that allows to preprocess public datasets in order to provide data for the effective training of deep learning architectures.
Approach. The library can handle both BIDS (Brain Imaging Data Structure) and non-BIDS datasets, allowing the user to easily preprocess multiple public datasets. It unifies the EEG recordings acquired with different settings by defining a common pipeline and a specified channel template. An array of visualisation functions is provided inside the library, together with a user-friendly GUI to assist non-expert users throughout the workflow.
Main results. BIDSAlign enables the effective use of public EEG datasets, providing valuable medical insights, even for non-experts in the field. Results from applying the library to datasets from OpenNeuro demonstrate its ability to extract significant medical knowledge through an end-to-end workflow, facilitating group analysis, visual comparison and statistical testing.
Significance. BIDSAlign solves the lack of large EEG datasets by aligning multiple datasets to a standard template. This unlocks the potential of public EEG data for training deep learning models. It paves the way to promising contributions based on deep learning to clinical and non-clinical EEG research, offering insights that can inform neurological disease diagnosis and treatment strategies.