ephypype.pipelines.create_pipeline_time_series_to_spectral_connectivity

ephypype.pipelines.create_pipeline_time_series_to_spectral_connectivity(main_path, pipeline_name='ts_to_conmat', con_method='coh', multi_con=False, export_to_matlab=False, n_windows=[], mode='multitaper', is_sensor_space=True, epoch_window_length=None, gathering_method='mean')

Connectivity pipeline.

Compute spectral connectivity in a given frequency bands.

Parameters:

main_pathstr

the main path of the pipeline

pipeline_name: str (default ‘ts_to_conmat’)

name of the pipeline

con_methodstr

metric computed on time series for connectivity; possible choice: “coh”,”imcoh”,”plv”,”pli”,”wpli”,”pli2_unbiased”,”ppc”,”cohy”, “wpli2_debiased”

multi_conbool (default False)

True if multiple connectivity matrices are exported

export_to_matlabbool (default False)

True if conmat is exported to .mat format as well

n_windowslist

list of start and stop points (tuple of two integers) of temporal windows

modestr (default ‘multipaper’)

mode for computing frequency bands; possible choice: “multitaper”, “cwt_morlet”

epoch_window_lengthfloat

epoched data

is_sensor_spacebool (default True)

True if we compute connectivity on sensor space

gather_methodstr (default “mean”)

how to handle the values over the frequency bands: possible choices: “mean”,”max”, “none”

ts_file (inputnode): str

path to the time series file in .npy format

freq_band (inputnode): float

frequency bands

sfreq (inputnode): float

sampling frequency

labels_file (inputnode): str

path to the file containing a list of labels associated with nodes

Returns:

pipelineinstance of Workflow