ephypype.interfaces.mne.InverseSolution¶

class ephypype.interfaces.mne.InverseSolution(from_file=None, resource_monitor=None, ignore_exception=False, **inputs)[source]¶

Compute the inverse solution on raw or epoch data.

This class is considering N_r regions in source space based on a FreeSurfer cortical parcellation.

Parameters:

sbj_idstr: Subject name
subjects_dirstr: Freesurfer directory
raw_filenamestr: Filename of the raw data
cov_filenamestr: Filename of the noise covariance matrix
fwd_filenamestr: Filename of the forward operator
is_epochedbool: If True and events_id = None the input data are epoch data in the format -epo.fif if True and events_id is not None, the raw data are epoched according to events_id and t_min and t_max values
events_id: dict: The dict of events
condition: list: List of events
t_min, t_max: int (defualt None): Define the time interval in which to epoch the raw data
is_evoked: bool: If True the raw data will be averaged according to the events contained in the dict events_id
is_ave: bool: If True the input data is an evoked dataset
is_fixedbool: If True we use fixed orientation
inv_methodstr: The inverse method to use; possible choices: MNE, dSPM, sLORETA
snrfloat: The SNR value used to define the regularization parameter
parc: str: The parcellation defining the ROIs atlas in the source space
aseg: bool: If True a mixed source space will be created and the sub cortical regions defined in aseg_labels will be added to the source space
aseg_labels: list: List of substructures we want to include in the mixed source space
all_src_space: bool: If True we compute the inverse for all points of the s0urce space
ROIs_mean: bool: If True we compute the mean of estimated time series on ROIs

Returns:

__init__(from_file=None, resource_monitor=None, ignore_exception=False, **inputs)¶: Subclasses must implement __init__

Methods

`__init__`([from_file, resource_monitor, ...])	Subclasses must implement __init__
`aggregate_outputs`([runtime, needed_outputs])	Collate expected outputs and apply output traits validation.
`help`([returnhelp])	Prints class help
`load_inputs_from_json`(json_file[, overwrite])	A convenient way to load pre-set inputs from a JSON file.
`run`([cwd, ignore_exception])	Execute this interface.
`save_inputs_to_json`(json_file)	A convenient way to save current inputs to a JSON file.

Attributes

`always_run`	Should the interface be always run even if the inputs were not changed? Only applies to interfaces being run within a workflow context.
`can_resume`	Defines if the interface can reuse partial results after interruption.
`resource_monitor`
`version`	interfaces should implement a version property