class eelbrain.pipeline.MneExperiment(root=None, find_subjects=True, **state)

Analyze an MEG experiment (gradiometer only) with MNE

root : str | None

the root directory for the experiment (usually the directory containing the ‘meg’ and ‘mri’ directories). The experiment can be initialized without the root for testing purposes.

find_subjects : bool

Automatically look for subjects in the MEG-directory (default True). Set find_subjects=False to initialize the experiment without any files.

Initial state parameters.


See also

Guide on using The MneExperiment Pipeline.


clear_cache(self[, level]) Remove cached files.
copy(self, temp[, dst_root, inclusive, …]) Copy files to a different root folder
expand_template(self, temp[, keep]) Expand all constant variables in a template
find_keys(self, temp[, root]) Find all terminal field names that are relevant for a template.
format(self, string[, vmatch]) Format a string (i.e., replace any ‘{xxx}’ fields with their values)
get(self, temp[, fmatch, vmatch, match, …]) Retrieve a formatted template
get_field_values(self, field[, exclude]) Find values for a field taking into account exclusion
glob(self, temp[, inclusive]) Find all files matching a certain pattern
iter(self[, fields, exclude, values, group, …]) Cycle the experiment’s state through all values on the given fields
iter_range(self[, start, stop, field]) Iterate through a range on a field with ordered values.
iter_temp(self, temp[, exclude, values]) Iterate through all paths conforming to a template given in temp.
label_events(self, ds) Add event labels to events loaded from raw files
label_groups(self, subject, groups) Generate Factor for group membership
label_subjects(self, ds) Label the subjects in ds
load_annot(self, **state) Load a parcellation (from an annot file)
load_bad_channels(self, **kwargs) Load bad channels
load_cov(self, **kwargs) Load the covariance matrix
load_edf(self, **kwargs) Load the edf file (“edf-file” template)
load_epochs(self[, subjects, baseline, …]) Load a Dataset with epochs for a given epoch definition
load_epochs_stc(self[, subjects, baseline, …]) Load a Dataset with stcs for single epochs
load_epochs_stf(self[, subjects, baseline, …]) Load frequency space single trial data
load_events(self[, subject, add_bads, data_raw]) Load events from a raw file.
load_evoked(self[, subjects, baseline, …]) Load a Dataset with the evoked responses for each subject.
load_evoked_stc(self[, subjects, baseline, …]) Load evoked source estimates.
load_evoked_stf(self[, subjects, baseline, …]) Load frequency space evoked data
load_fwd(self[, surf_ori, ndvar, mask]) Load the forward solution
load_ica(self[, make]) Load the mne-python ICA object
load_induced_stc(self[, subjects, …]) Morlet wavelet induced power and phase in source space.
load_inv(self[, fiff, ndvar, mask]) Load the inverse operator
load_label(self, label, **kwargs) Retrieve a label as mne Label object
load_morph_matrix(self, **state) Load the morph matrix from mrisubject to common_brain
load_neighbor_correlation(self[, subjects, …]) Load sensor neighbor correlation
load_raw(self[, add_bads, preload, ndvar, decim]) Load a raw file as mne Raw object.
load_selected_events(self[, subjects, …]) Load events and return a subset based on epoch and rejection
load_src(self[, add_geom, ndvar]) Load the current source space
load_test(self, test[, tstart, tstop, pmin, …]) Create and load spatio-temporal cluster test results
make_annot(self, **state) Make sure the annot files for both hemispheres exist
make_bad_channels(self[, bad_chs, redo]) Write the bad channel definition file for a raw file
make_bad_channels_auto(self[, flat, redo]) Automatically detect bad channels
make_bad_channels_neighbor_correlation(self, r) Exclude bad channels based on low average neighbor-correlation
make_besa_evt(self[, redo]) Make the trigger and event files needed for besa
make_copy(self, temp, field, src, dst[, redo]) Make a copy of a file to a new path by substituting one field value
make_cov(self) Make a noise covariance (cov) file
make_epoch_selection(self[, decim, auto, …]) Open gui.select_epochs() for manual epoch selection
make_fwd(self) Make the forward model
make_ica(self[, make]) Compute ICA decomposition for a pipeline.RawICA preprocessing step
make_ica_selection(self[, epoch, decim, session]) Select ICA components to remove through a GUI
make_link(self, temp, field, src, dst[, redo]) Make a hard link
make_mov_ga_dspm(self[, subjects, baseline, …]) Make a grand average movie from dSPM values (requires PySurfer 0.6)
make_mov_ttest(self[, subjects, model, c1, …]) Make a t-test movie (requires PySurfer 0.6)
make_mrat_evoked(self, **kwargs) Produce the sensor data fiff files needed for MRAT sensor analysis
make_mrat_stcs(self, **kwargs) Produce the STC files needed for the MRAT analysis tool
make_plot_annot(self[, surf, redo]) Create a figure for the contents of an annotation file
make_plot_label(self, label[, surf, redo])
make_plots_labels(self[, surf, redo])
make_raw(self, **kwargs) Make a raw file
make_report(self, test[, parc, mask, pmin, …]) Create an HTML report on spatio-temporal clusters
make_report_coreg(self[, file_name]) Create HTML report with plots of the MEG/MRI coregistration
make_report_rois(self, test[, parc, pmin, …]) Create an HTML report on ROI time courses
make_src(self, **kwargs) Make the source space
merge_bad_channels(self) Merge bad channel definitions for different sessions
move(self, temp[, dst_root, inclusive, …]) Move files to a different root folder
next(self[, field]) Change field to the next value
plot_annot(self[, parc, surf, views, hemi, …]) Plot the annot file on which the current parcellation is based
plot_brain(self[, common_brain]) Plot the brain model
plot_coreg(self[, dig, parallel]) Plot the coregistration (Head shape and MEG helmet)
plot_evoked(self[, subjects, separate, …]) Plot evoked sensor data
plot_label(self, label[, surf, views, w]) Plot a label
plot_raw(self[, decim, xlim, add_bads, …]) Plot raw sensor data
plot_whitened_gfp(self[, s_start, s_stop, run]) Plot the GFP of the whitened evoked to evaluate the the covariance matrix
rename(self, old, new[, exclude]) Rename all files corresponding to a pattern (or template)
rename_field(self, temp, field, old, new[, …]) Change the value of one field in paths corresponding to a template
reset(self) Reset all field values to the state at initialization
rm(self, temp[, inclusive, confirm]) Remove all files corresponding to a template
run_mne_analyze(self[, modal]) Run mne_analyze
run_mne_browse_raw(self[, modal]) Run mne_analyze
set(self[, subject, match, allow_asterisk]) Set variable values.
set_inv(self[, ori, snr, method, depth, …]) Set the type of inverse solution used for source estimation
show_bad_channels(self[, sessions]) List bad channels
show_fields(self[, str_out]) Generate a table for all iterable fields and ther values.
show_file_status(self, temp[, col, row]) Compile a table about the existence of files
show_file_status_mult(self, files, fields[, …]) Compile a table about the existence of multiple files
show_in_finder(self, temp, **kwargs) Reveal the file corresponding to the temp template in the Finder.
show_input_tree(self) Print a tree of the files needed as input
show_raw_info(self, **state) Display the selected pipeline for raw processing
show_reg_params(self[, asds]) Show the covariance matrix regularization parameters
show_rej_info(self[, flagp, asds, bads]) Information about artifact rejection
show_state(self[, temp, empty, hide]) List all top-level fields and their values
show_subjects(self[, mri, mrisubject, …]) Create a Dataset with subject information
show_tree(self[, root, fields]) Print a tree of the filehierarchy implicit in the templates