MNE-BIDS: Organizing electrophysiological data into the BIDS format and facilitating their analysis

The development of the Brain Imaging Data Structure (BIDS; Gorgolewski et al., 2016) gave the neuroscientific community a standard to organize and share data. BIDS prescribes file naming conventions and a folder structure to store data in a set of already existing file formats. Next to rules about organization of the data itself, BIDS provides standardized templates to store associated metadata in the form of Javascript Object Notation (JSON) and tab separated value (TSV) files. It thus facilitates data sharing, eases metadata querying, and enables automatic data analysis pipelines. BIDS is a rich system to curate, aggregate, and annotate neuroimaging databases. While BIDS was originally intended for magnetic resonance imaging (MRI) data, it has extensions for other data modalities including: magnetoencephalography (MEG; Niso et al., 2018), electroencephalography (EEG; Pernet et al., 2019), and intracranial encephalography (iEEG; Holdgraf et al., 2019). Software packages analyzing MEG, EEG, and iEEG are now starting to support data organized using the BIDS standard, thereby becoming “BIDS compatible”. Within the Python ecosystem, MNE-Python (Gramfort et al., 2013) is a major software package for electrophysiology data analysis, and extending its functionality to support BIDS would be a great benefit for its growing user base. For this reason, we developed a dedicated Python software package MNE-BIDS with the goal of providing a programmable interface for BIDS datasets in electrophysiology with MNE-Python. MNE-BIDS allows users to re-organize data into BIDS formats, store associated metadata after anonymization, extract information necessary for preprocessing, and read the data into MNE-Python objects, ready for source localization. Starting with a single directory full of data files with arbitrary names, MNE-BIDS can be used to extract existing metadata, reorganize the files into the BIDS format, and write additional