Welcome to NeuroPype: A Python-based pipeline for advanced MEG and EEG connectivity analyses

With the exponential increase in data dimension and methodological complexities, conducting brain network analyses using MEG and EEG is becoming an increasingly challenging and time-consuming endeavor. To date, most of the MEG/EEG processing is done by combining software packages and custom tools which often hinders reproducibility of the experimental findings. Here we describe NeuroPype, which is a free open-source Python package we developed for efficient multi-thread processing of MEG and EEG studies. The proposed package is largely based on the NiPype framework and the MNE-Python software and benefits from standard Python packages such as NumPy and SciPy. It also incorporates several existing wrappers, such as a Freesurfer Python-wrapper for multi-subject MRI segmentation. The NeuroPype project includes three different packages: I Neuropype-ephy includes pipelines for electrophysiology analysis; current implementations allow for MEG/EEG data import, data pre-processing and cleaning by an automatic removal of eyes and heart related artefacts, sensor or source-level connectivity analyses II Neuropype-graph: functional connectivity exploiting graph-theoretical metrics including modular partitions III Neuropype-gui: a graphical interface wrapping the definition of parameters. NeuroPype provides a common and fast framework to develop workflows for advanced MEG/EEG analyses (but also fMRI and iEEG). Several pipelines have already been developed with NeuroPype to analyze different MEG and EEG datasets: e.g. EEG sleep data, MEG resting state measurements and MEG recordings in Autism. NeuroPype will be be made available via Github. Current developments will increase its compatibility with existing Python packages of interest such as machine learning tools.