cmtklib.interfaces.mne module

The MNE module provides Nipype interfaces for MNE tools missing in Nipype or modified.

CreateBEM

Link to code

Bases: nipype.interfaces.base.core.BaseInterface

Use MNE to create the BEM surfaces.

Examples

>>> from cmtklib.interfaces.mne import CreateBEM
>>> create_bem = CreateBEM()
>>> create_bem.inputs.fs_subject = 'sub-01'
>>> create_bem.inputs.fs_subjects_dir = '/path/to/bids_dataset/derivatives/freesurfer-7.1.1'
>>> create_bem.inputs.out_bem_fname = 'bem.fif'
>>> create_bem.run()

References

fs_subjecta string

FreeSurfer subject ID.

fs_subjects_dira string or os.PathLike object referring to an existing directory

Freesurfer subjects (derivatives) directory.

out_bem_fnamea string

Name of output BEM file in fif format.

bem_filea string or os.PathLike object

Path to output BEM file in fif format.

CreateCov

Link to code

Bases: nipype.interfaces.base.core.BaseInterface

Use MNE to create the noise covariance matrix.

Examples

>>> from cmtklib.interfaces.mne import CreateCov
>>> create_cov = CreateCov()
>>> create_cov.inputs.epochs_file = '/path/to/sub-01_epo.fif'
>>> create_cov.inputs.out_noise_cov_fname = 'sub-01_noisecov.fif'
>>> create_cov.run()

References

epochs_filea string or os.PathLike object referring to an existing file

Eeg * epochs in .set format.

out_noise_cov_fnamea string

Name of output file to save noise covariance matrix in fif format.

noise_cov_filea string or os.PathLike object

Location and name to store noise covariance matrix in fif format.

CreateFwd

Link to code

Bases: nipype.interfaces.base.core.BaseInterface

Use MNE to calculate the forward solution.

Examples

>>> from cmtklib.interfaces.mne import CreateFwd
>>> create_fwd = CreateFwd()
>>> create_fwd.inputs.epochs_file = '/path/to/sub-01_epo.fif'
>>> create_fwd.inputs.out_fwd_fname = 'sub-01_fwd.fif'
>>> create_fwd.inputs.src_file = '/path/to/sub-01_src.fif'
>>> create_fwd.inputs.bem_file = '/path/to/sub-01_bem.fif'
>>> create_fwd.inputs.trans_file = '/path/to/sub-01_trans.fif'
>>> create_fwd.run()

References

bem_filea string or os.PathLike object referring to an existing file

Boundary surfaces for MNE head model in fif format.

epochs_filea string or os.PathLike object referring to an existing file

Eeg * epochs in .fif format, containing information about electrode montage.

src_filea string or os.PathLike object referring to an existing file

Source space file in fif format.

out_fwd_fnamea string

Name of output forward solution file created with MNE.

trans_filea string or os.PathLike object referring to an existing file

Trans.fif file containing co-registration information (electrodes x MRI).

fwd_filea string or os.PathLike object

Path to generated forward solution file in fif format.

CreateSrc

Link to code

Bases: nipype.interfaces.base.core.BaseInterface

Use MNE to set up bilateral hemisphere surface-based source space with subsampling and write source spaces to a file.

Examples

>>> from cmtklib.interfaces.mne import CreateSrc
>>> create_src = CreateSrc()
>>> create_src.inputs.fs_subject = 'sub-01'
>>> create_src.inputs.fs_subjects_dir = '/path/to/bids_dataset/derivatives/freesurfer-7.1.1'
>>> create_src.inputs.out_src_fname = 'sub-01_src.fif'
>>> create_src.run()

References

fs_subjecta string

FreeSurfer subject ID.

fs_subjects_dira string or os.PathLike object referring to an existing directory

Freesurfer subjects (derivatives) directory.

out_src_fnamea string

Name of output source space file created with MNE.

overwritea boolean

Overwrite source space file if already existing.

src_filea string or os.PathLike object

Path to output source space files in fif format.

EEGLAB2fif

Link to code

Bases: nipype.interfaces.base.core.BaseInterface

Use MNE to convert EEG data from EEGlab to MNE format.

Examples

>>> from cmtklib.interfaces.mne import EEGLAB2fif
>>> eeglab2fif = EEGLAB2fif()
>>> eeglab2fif.inputs.eeg_ts_file = ['sub-01_task-faces_desc-preproc_eeg.set']
>>> eeglab2fif.inputs.events_file = ['sub-01_task-faces_events.tsv']
>>> eeglab2fif.inputs.out_epochs_fif_fname = 'sub-01_epo.fif'
>>> eeglab2fif.inputs.electrodes_file = 'sub-01_eeg.xyz'
>>> eeglab2fif.inputs.event_ids = {"SCRAMBLED":0, "FACES":1}
>>> eeglab2fif.inputs.t_min = -0.2
>>> eeglab2fif.inputs.t_max = 0.6
>>> eeglab2fif.run()

References

eeg_ts_filea string or os.PathLike object referring to an existing file

Eeg * epochs in .set format.

events_filea string or os.PathLike object referring to an existing file

Epochs metadata in _behav.txt.

out_epochs_fif_fnamea string

Output filename for eeg * epochs in .fif format, e.g. sub-01_epo.fif.

electrodes_filea string or os.PathLike object referring to an existing file

Positions of EEG electrodes in a txt file.

event_idsa dictionary with keys which are any value and with values which are any value

The id of the events to consider in dict form. The keys of the dict can later be used to access associated events. If None, all events will be used and a dict is created with string integer names corresponding to the event id integers.

t_maxa float

End time of the epochs in seconds, relative to the time-locked event.

t_mina float

Start time of the epochs in seconds, relative to the time-locked event.

epochs_filea string or os.PathLike object referring to an existing file

Eeg * epochs in .fif format.

MNEInverseSolutionROI

Link to code

Bases: nipype.interfaces.base.core.BaseInterface

Use MNE to convert EEG data from EEGlab to MNE format.

Examples

>>> from cmtklib.interfaces.mne import MNEInverseSolutionROI
>>> inv_sol = MNEInverseSolutionROI()
>>> inv_sol.inputs.esi_method_snr = 3.0
>>> inv_sol.inputs.fs_subject = 'sub-01'
>>> inv_sol.inputs.fs_subjects_dir = '/path/to/bids_dataset/derivatives/freesurfer-7.1.1'
>>> inv_sol.inputs.epochs_file = '/path/to/sub-01_epo.fif'
>>> inv_sol.inputs.src_file = '/path/to/sub-01_src.fif'
>>> inv_sol.inputs.bem_file = '/path/to/sub-01_bem.fif'
>>> inv_sol.inputs.noise_cov_file = '/path/to/sub-01_noisecov.fif'
>>> inv_sol.inputs.fwd_file = '/path/to/sub-01_fwd.fif'
>>> inv_sol.inputs.atlas_annot = 'lausanne2018.scale1'
>>> inv_sol.inputs.out_roi_ts_fname_prefix = 'sub-01_atlas-L2018_res-scale1_desc-epo_timeseries'
>>> inv_sol.inputs.out_inv_fname = 'sub-01_inv.fif'
>>> inv_sol.run()

References

bem_filea string or os.PathLike object referring to an existing file

Surfaces for head model in fif format.

epochs_filea string or os.PathLike object referring to an existing file

Eeg * epochs in .fif format.

fs_subjecta string

FreeSurfer subject ID.

fs_subjects_dira string or os.PathLike object referring to an existing directory

Freesurfer subjects (derivatives) directory.

fwd_filea string or os.PathLike object

Forward solution in fif format.

noise_cov_filea string or os.PathLike object referring to an existing file

Noise covariance matrix in fif format.

out_inv_fnamea string

Output filename for inverse operator in fif format.

src_filea string or os.PathLike object referring to an existing file

Source space created with MNE in fif format.

atlas_annot‘aparc’ or ‘lausanne2018.scale1’ or ‘lausanne2018.scale2’ or ‘lausanne2018.scale3’ or ‘lausanne2018.scale4’ or ‘lausanne2018.scale5’

The parcellation to use, e.g., ‘aparc’, ‘lausanne2018.scale1’, ‘lausanne2018.scale2’, ‘lausanne2018.scale3’, ‘lausanne2018.scale4’ or’lausanne2018.scale5’.

esi_method‘sLORETA’ or ‘eLORETA’ or ‘MNE’ or ‘dSPM’

Use minimum norm 1, dSPM 2, sLORETA (default) 3, or eLORETA 4.

esi_method_snra float

SNR value such as the ESI method regularization weight lambda2 is set to 1.0 / esi_method_snr ** 2.

out_roi_ts_fname_prefixa string

Output filename prefix (no extension) for rois * time series in .npy and .mat formats.

inv_filea string or os.PathLike object

Path to output inverse operator file in fif format.

roi_ts_mat_filea string or os.PathLike object

Path to output ROI time series file in .mat format.

roi_ts_npy_filea string or os.PathLike object

Path to output ROI time series file in .npy format.

MNESpectralConnectivity

Link to code

Bases: nipype.interfaces.base.core.BaseInterface

Use MNE to compute frequency- and time-frequency-domain connectivity measures.

Examples

>>> from cmtklib.interfaces.mne import MNESpectralConnectivity
>>> eeg_cmat = MNESpectralConnectivity()
>>> eeg_cmat.inputs.fs_subject = 'sub-01'
>>> eeg_cmat.inputs.fs_subjects_dir = '/path/to/bids_dataset/derivatives/freesurfer-7.1.1'
>>> eeg_cmat.inputs.atlas_annot = 'lausanne2018.scale1'
>>> eeg_cmat.inputs.connectivity_metrics = ['imcoh', 'pli', 'wpli']
>>> eeg_cmat.inputs.output_types = ['tsv', 'gpickle', 'mat', 'graphml']
>>> eeg_cmat.inputs.epochs_file = '/path/to/sub-01_epo.fif'
>>> eeg_cmat.inputs.roi_ts_file = '/path/to/sub-01_timeseries.npy'
>>> eeg_cmat.run()

References

fs_subjecta string

FreeSurfer subject ID.

fs_subjects_dira string or os.PathLike object referring to an existing directory

Freesurfer subjects (derivatives) directory.

atlas_annot‘aparc’ or ‘lausanne2018.scale1’ or ‘lausanne2018.scale2’ or ‘lausanne2018.scale3’ or ‘lausanne2018.scale4’ or ‘lausanne2018.scale5’

The parcellation to use, e.g., ‘aparc’, ‘lausanne2018.scale1’, ‘lausanne2018.scale2’, ‘lausanne2018.scale3’, ‘lausanne2018.scale4’ or’lausanne2018.scale5’.

connectivity_metricsa list of items which are any value

Set of frequency- and time-frequency-domain connectivity metrics to compute.

epochs_filea pathlike object or string representing an existing file

Epochs file in fif format.

out_cmat_fnamea string

Basename of output connectome file (without any extension).

output_typesa list of items which are any value

Set of format to save output connectome files.

roi_ts_filea pathlike object or string representing an existing file

Extracted ROI time courses from ESI in .npy format.

roi_volume_tsv_filea pathlike object or string representing an existing file

Index / label atlas mapping file in .tsv format accordingly to BIDS.

connectivity_matricesa list of items which are a pathlike object or string representing a file

Connectivity matrices.