Mne Bids Pipeline -

import yaml, argparse from mne_bids import BIDSPath, read_raw_bids

# Compute evoked for each condition evoked_face = epochs['stimulus/face'].average() evoked_car = epochs['stimulus/car'].average() mne bids pipeline

threshold = 2.0 # t-value threshold cluster_stats = mne.stats.permutation_cluster_test( [X, Y], threshold=threshold, n_permutations=1024, tail=0, # two-tailed n_jobs=-1, ) argparse from mne_bids import BIDSPath

: The final step involves organizing results in a reproducible manner and potentially sharing them through data repositories. # two-tailed n_jobs=-1

from mne_bids import write_anat write_anat(bids_root, subject='001', t1w_anat='sub-001_T1w.nii.gz')

This article outlines a complete, reproducible pipeline using MNE-Python and mne-bids , covering: