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.subject = 'sub-01'
>>> create_bem.inputs.bids_dir = '/path/to/bids_dataset'
>>> create_bem.inputs.output_query = {"src": {"suffix": "src", "extension": ["fif"]}}
>>> create_bem.inputs.derivative_list = ['cmp-v3.0.3']
>>> create_bem.run()  

References

• https://mne.tools/stable/generated/mne.bem.make_watershed_bem.html

• https://mne.tools/stable/generated/mne.make_bem_model.html

• https://mne.tools/stable/generated/mne.write_bem_solution.html

bids_dira string

Base directory.

derivative_lista list of items which are any value

List of derivatives to add to the datagrabber.

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

BIDSDataGrabber output_query.

subjecta string

Subject.

derivative_lista list of items which are any value

List of derivatives to add to the datagrabber.

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

BIDSDataGrabber output_query.

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_fif_fname = 'sub-01_epo.fif'
>>> create_cov.inputs.noise_cov_fname = 'sub-01_noisecov.fif'
>>> create_cov.run()  

References

• https://mne.tools/stable/generated/mne.Covariance.html

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

Eeg * epochs in .set format.

noise_cov_fnamea string or os.PathLike object

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

has_runa boolean

If true, covariance matrix has been produced.

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

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_fif_fname = 'sub-01_epo.fif'
>>> create_fwd.inputs.fwd_fname = 'sub-01_fwd.fif'
>>> create_fwd.inputs.src = 'sub-01_src.fif'
>>> create_fwd.inputs.bem = 'sub-01_bem.fif'
>>> create_fwd.inputs.trans_fname = 'sub-01_trans.fif'
>>> create_fwd.run()  

References

• https://mne.tools/stable/generated/mne.make_forward_solution.html

bema list of items which are any value

Boundary surfaces for MNE head model.

epochs_fif_fnamea string or os.PathLike object

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

srca list of items which are any value

Source space created with MNE.

fwd_fnamea string or os.PathLike object

Forward solution created with MNE.

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

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

has_runa boolean

If true, forward solution has been produced.

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.subject = 'sub-01'
>>> create_src.inputs.bids_dir = '/path/to/bids_dataset'
>>> create_src.inputs.output_query = {"src": {"suffix": "src", "extension": ["fif"]}}
>>> create_src.inputs.derivative_list = ['cmp-v3.0.3']
>>> create_src.run()  

References

• https://mne.tools/stable/generated/mne.setup_source_space.html

• https://mne.tools/stable/generated/mne.write_source_spaces.html

bids_dira string

Base directory.

derivative_lista list of items which are any value

List of derivatives to add to the datagrabber.

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

BIDSDataGrabber output_query.

subjecta string

Subject.

derivative_lista list of items which are any value

List of derivatives to add to the datagrabber.

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

BIDSDataGrabber output_query.

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.behav_file = ['sub-01_task-faces_events.tsv']
>>> eeglab2fif.inputs.epochs_fif_fname = 'sub-01_epo.fif'
>>> eeglab2fif.inputs.electrode_positions_file = 'sub-01_eeg.xyz'
>>> eeglab2fif.inputs.EEG_params = {"expe_name":"faces",
                                    EEG_event_IDs": {"SCRAMBLED":0, "FACES":1},
                                    "start_t":-0.2, "end_t":0.6}
>>> eeglab2fif.inputs.output_query = {"src": {"suffix": "src", "extension": ["fif"]}}
>>> eeglab2fif.inputs.derivative_list = ['cmp-v3.0.3']
>>> eeglab2fif.run()  

References

• https://mne.tools/stable/generated/mne.read_epochs_eeglab.html

• https://mne.tools/stable/generated/mne.channels.make_dig_montage.html

• https://mne.tools/stable/generated/mne.Epochs.html?highlight=set_montage#mne.Epochs.set_montage

• https://mne.tools/stable/generated/mne.Epochs.html?highlight=set_montage#mne.Epochs.save

behav_filea list of items which are any value

Epochs metadata in _behav.txt.

derivative_lista list of items which are any value

List of derivatives to add to the datagrabber.

eeg_ts_filea list of items which are any value

Eeg * epochs in .set format.

epochs_fif_fnamea string or os.PathLike object

Eeg * epochs in .fif format.

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

BIDSDataGrabber output_query.

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

Dictionary defining EEG parameters.

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

Positions of EEG electrodes in a txt file.

derivative_lista list of items which are any value

List of derivatives to add to the datagrabber.

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

Eeg * epochs in .fif format.

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

BIDSDataGrabber output_query.

MNEInverseSolution

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 MNEInverseSolution
>>> inv_sol = MNEInverseSolution()
>>> inv_sol.inputs.subject = 'sub-01'
>>> inv_sol.inputs.bids_dir = '/path/to/bids_dataset'
>>> inv_sol.inputs.epochs_fif_fname = 'sub-01_epo.fif'
>>> inv_sol.inputs.src_file = ['sub-01_src.fif']
>>> inv_sol.inputs.bem_file = ['sub-01_bem.fif']
>>> inv_sol.inputs.cov_has_run = 'True'
>>> inv_sol.inputs.noise_cov_fname = 'sub-01_noisecov.fif'
>>> inv_sol.inputs.fwd_has_run = 'True'
>>> inv_sol.inputs.fwd_fname = 'sub-01_fwd.fif'
>>> inv_sol.inputs.inv_fname = 'sub-01_inv.fif'
>>> inv_sol.inputs.parcellation = 'lausanne2018.scale1'
>>> inv_sol.inputs.roi_ts_file = 'sub-01_atlas-L2018_res-scale1_desc-epo_timeseries.npy'
>>> inv_sol.run()  

References

• https://mne.tools/stable/generated/mne.read_forward_solution.html

• https://mne.tools/stable/generated/mne.minimum_norm.make_inverse_operator.html

• https://mne.tools/stable/generated/mne.minimum_norm.apply_inverse_epochs.html

• https://mne.tools/stable/generated/mne.read_labels_from_annot.html

• https://mne.tools/stable/generated/mne.extract_label_time_course.html

bem_filea list of items which are any value

Surfaces for head model.

bids_dira string

Base directory.

cov_has_runa boolean

Indicate if covariance matrix has been produced.

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

Eeg * epochs in .fif format.

fwd_fnamea string or os.PathLike object

Forward solution in fif format.

inv_fnamea string or os.PathLike object

Inverse operator in fif format.

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

Noise covariance matrix in fif format.

src_filea list of items which are any value

Source space created with MNE.

subjecta string

Subject.

fwd_has_runa boolean

Indicate if forward solution has been produced.

parcellationa string

Parcellation scheme.

roi_ts_filea string or os.PathLike object

Rois * time series in .npy format.

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

Forward solution in fif format.

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

Rois * time series in .npy format.