MNE Sample Data: M/EEG combined processing¶
Demonstrated features¶
| Feature | This example |
|---|---|
| MEG processing | ✅ |
| EEG processing | ✅ |
| Maxwell filter | ✅ |
| Frequency filter | ✅ |
| Artifact regression | ❌ |
| SSP | ✅ |
| ICA | ❌ |
| Evoked contrasts | ✅ |
| Time-by-time decoding | ✅ |
| Time-generalization decoding | ❌ |
| CSP decoding | ❌ |
| Time-frequency analysis | ✅ |
| BEM surface creation | ✅ |
| Template MRI | ❌ |
Dataset source¶
This dataset was acquired from https://openneuro.org/datasets/ds000248
How to download this dataset
Run in your terminal:
Run in your terminal
openneuro-py download \
--dataset=ds000248 \
--include=sub-01 \
--include=sub-emptyroom \
--include=derivatives/freesurfer/subjects \
--exclude=derivatives/freesurfer/subjects/fsaverage/mri/aparc.a2005s+aseg.mgz \
--exclude=derivatives/freesurfer/subjects/fsaverage/mri/aparc+aseg.mgz \
--exclude=derivatives/freesurfer/subjects/fsaverage/mri/aparc.a2009s+aseg.mgz \
--exclude=derivatives/freesurfer/subjects/fsaverage/xhemi/mri/aparc+aseg.mgz \
--exclude=derivatives/freesurfer/subjects/sub-01/mri/aparc+aseg.mgz \
--exclude=derivatives/freesurfer/subjects/sub-01/mri/aparc.DKTatlas+aseg.mgz \
--exclude=derivatives/freesurfer/subjects/sub-01/mri/aparc.DKTatlas+aseg.mgz \
--exclude=derivatives/freesurfer/subjects/sub-01/mri/aparc.a2009s+aseg.mgz
Note that we have to explicitly exclude files due to a problem with OpenNeuro's storage.
Configuration¶
Click to expand
Python
.
import mne
bids_root = "~/mne_data/ds000248"
deriv_root = "~/mne_data/derivatives/mne-bids-pipeline/ds000248_base"
subjects_dir = f"{bids_root}/derivatives/freesurfer/subjects"
subjects = ["01"]
rename_events = {"Smiley": "Emoji", "Button": "Switch"}
conditions = ["Auditory", "Visual", "Auditory/Left", "Auditory/Right"]
epochs_metadata_query = "index > 0" # Just for testing!
contrasts = [("Visual", "Auditory"), ("Auditory/Right", "Auditory/Left")]
time_frequency_conditions = ["Auditory", "Visual"]
ch_types = ["meg", "eeg"]
mf_reference_run = "01"
find_flat_channels_meg = True
find_noisy_channels_meg = True
use_maxwell_filter = True
def noise_cov(bp):
Estimate the noise covariance.
# Use pre-stimulus period as noise source
bp = bp.copy().update(suffix="epo")
if not bp.fpath.exists():
bp.update(split="01")
epo = mne.read_epochs(bp)
cov = mne.compute_covariance(epo, rank="info", tmax=0)
return cov
spatial_filter = "ssp"
n_proj_eog = dict(n_mag=1, n_grad=1, n_eeg=1)
n_proj_ecg = dict(n_mag=1, n_grad=1, n_eeg=0)
ssp_meg = "combined"
ecg_proj_from_average = True
eog_proj_from_average = False
epochs_decim = 4
bem_mri_images = "FLASH"
recreate_bem = True
n_jobs = 2
def mri_t1_path_generator(bids_path):
Return the path to a T1 image.
# don't really do any modifications – just for testing!
return bids_path