Python API Reference

This is the reference for classes (CamelCase names) and functions (underscore_case names) of MNE-Python, grouped thematically by analysis stage. Functions and classes that are not below a module heading are found in the mne namespace.

MNE-Python also provides multiple command-line scripts that can be called directly from a terminal, see Command line tools using Python.

mne:

MNE software for MEG and EEG data analysis.

Most-used classes

io.Raw(fname[, allow_maxshield, preload, …])	Raw data in FIF format.
Epochs(raw, events[, event_id, tmin, tmax, …])	Epochs extracted from a Raw instance.
Evoked(fname[, condition, proj, kind, …])	Evoked data.
Info(*args, **kwargs)	Measurement information.

Reading raw data

mne.io:

IO module for reading raw data.

anonymize_info(info[, daysback, keep_his, …])	Anonymize measurement information in place.
read_raw(fname, *[, preload, verbose])	Read raw file.
read_raw_artemis123(input_fname[, preload, …])	Read Artemis123 data as raw object.
read_raw_bti(pdf_fname[, config_fname, …])	Raw object from 4D Neuroimaging MagnesWH3600 data.
read_raw_cnt(input_fname[, eog, misc, ecg, …])	Read CNT data as raw object.
read_raw_ctf(directory[, system_clock, …])	Raw object from CTF directory.
read_raw_curry(fname[, preload, verbose])	Read raw data from Curry files.
read_raw_edf(input_fname[, eog, misc, …])	Reader function for EDF or EDF+ files.
read_raw_bdf(input_fname[, eog, misc, …])	Reader function for BDF files.
read_raw_gdf(input_fname[, eog, misc, …])	Reader function for GDF files.
read_raw_kit(input_fname[, mrk, elp, hsp, …])	Reader function for Ricoh/KIT conversion to FIF.
read_raw_nicolet(input_fname, ch_type[, …])	Read Nicolet data as raw object.
read_raw_nirx(fname[, preload, verbose])	Reader for a NIRX fNIRS recording.
read_raw_snirf(fname[, preload, verbose])	Reader for a continuous wave SNIRF data.
read_raw_eeglab(input_fname[, eog, preload, …])	Read an EEGLAB .set file.
read_raw_brainvision(vhdr_fname[, eog, …])	Reader for Brain Vision EEG file.
read_raw_egi(input_fname[, eog, misc, …])	Read EGI simple binary as raw object.
read_raw_fif(fname[, allow_maxshield, …])	Reader function for Raw FIF data.
read_raw_eximia(fname[, preload, verbose])	Reader for an eXimia EEG file.
read_raw_fieldtrip(fname, info[, data_name])	Load continuous (raw) data from a FieldTrip preprocessing structure.
read_raw_persyst(fname[, preload, verbose])	Reader for a Persyst (.lay/.dat) recording.
read_raw_nihon(fname[, preload, verbose])	Reader for an Nihon Kohden EEG file.

Base class:

BaseRaw(info[, preload, first_samps, …])

Base class for Raw data.

mne.io.kit:

KIT module for reading raw data.

read_mrk(fname)

Marker Point Extraction in MEG space directly from sqd.

File I/O

channel_type(info, idx)	Get channel type.
channel_indices_by_type(info[, picks])	Get indices of channels by type.
get_head_surf(subject[, source, …])	Load the subject head surface.
get_meg_helmet_surf(info[, trans, verbose])	Load the MEG helmet associated with the MEG sensors.
get_volume_labels_from_aseg(mgz_fname[, …])	Return a list of names and colors of segmented volumes.
get_volume_labels_from_src(src, subject, …)	Return a list of Label of segmented volumes included in the src space.
parse_config(fname)	Parse a config file (like .ave and .cov files).
read_labels_from_annot(subject[, parc, …])	Read labels from a FreeSurfer annotation file.
read_bem_solution(fname[, verbose])	Read the BEM solution from a file.
read_bem_surfaces(fname[, patch_stats, …])	Read the BEM surfaces from a FIF file.
read_cov(fname[, verbose])	Read a noise covariance from a FIF file.
read_dipole(fname[, verbose])	Read .dip file from Neuromag/xfit or MNE.
read_epochs(fname[, proj, preload, verbose])	Read epochs from a fif file.
read_epochs_kit(input_fname, events[, …])	Reader function for Ricoh/KIT epochs files.
read_epochs_eeglab(input_fname[, events, …])	Reader function for EEGLAB epochs files.
read_epochs_fieldtrip(fname, info[, …])	Load epoched data from a FieldTrip preprocessing structure.
read_events(filename[, include, exclude, …])	Read events from fif or text file.
read_evokeds(fname[, condition, baseline, …])	Read evoked dataset(s).
read_evoked_fieldtrip(fname, info[, …])	Load evoked data from a FieldTrip timelocked structure.
read_freesurfer_lut([fname])	Read a Freesurfer-formatted LUT.
read_forward_solution(fname[, include, …])	Read a forward solution a.k.a.
read_label(filename[, subject, color])	Read FreeSurfer Label file.
read_morph_map(subject_from, subject_to[, …])	Read morph map.
read_proj(fname[, verbose])	Read projections from a FIF file.
read_reject_parameters(fname)	Read rejection parameters from .cov or .ave config file.
read_selection(name[, fname, info, verbose])	Read channel selection from file.
read_source_estimate(fname[, subject])	Read a source estimate object.
read_source_spaces(fname[, patch_stats, verbose])	Read the source spaces from a FIF file.
read_surface(fname[, read_metadata, …])	Load a Freesurfer surface mesh in triangular format.
read_trans(fname[, return_all, verbose])	Read a -trans.fif file.
read_tri(fname_in[, swap, verbose])	Read triangle definitions from an ascii file.
write_labels_to_annot(labels[, subject, …])	Create a FreeSurfer annotation from a list of labels.
write_bem_solution(fname, bem[, overwrite, …])	Write a BEM model with solution.
write_bem_surfaces(fname, surfs[, …])	Write BEM surfaces to a fiff file.
write_cov(fname, cov)	Write a noise covariance matrix.
write_events(filename, event_list)	Write events to file.
write_evokeds(fname, evoked)	Write an evoked dataset to a file.
write_forward_solution(fname, fwd[, …])	Write forward solution to a file.
write_label(filename, label[, verbose])	Write a FreeSurfer label.
write_proj(fname, projs)	Write projections to a FIF file.
write_source_spaces(fname, src[, overwrite, …])	Write source spaces to a file.
write_surface(fname, coords, faces[, …])	Write a triangular Freesurfer surface mesh.
write_trans(fname, trans)	Write a -trans.fif file.
what(fname)	Try to determine the type of the FIF file.
io.read_info(fname[, verbose])	Read measurement info from a file.
io.show_fiff(fname[, indent, read_limit, …])	Show FIFF information.

Base class:

BaseEpochs(info, data, events[, event_id, …])

Abstract base class for Epochs-type classes.

Creating data objects from arrays

EvokedArray(data, info[, tmin, comment, …])	Evoked object from numpy array.
EpochsArray(data, info[, events, tmin, …])	Epochs object from numpy array.
io.RawArray(data, info[, first_samp, copy, …])	Raw object from numpy array.
create_info(ch_names, sfreq[, ch_types, verbose])	Create a basic Info instance suitable for use with create_raw.

Datasets

mne.datasets:

Functions for fetching remote datasets.

See Datasets Overview for more information.

brainstorm.bst_auditory.data_path([path, …])	Get path to local copy of brainstorm (bst_auditory) dataset.
brainstorm.bst_resting.data_path([path, …])	Get path to local copy of brainstorm (bst_resting) dataset.
brainstorm.bst_raw.data_path([path, …])	Get path to local copy of brainstorm (bst_raw) dataset.
eegbci.load_data(subject, runs[, path, …])	Get paths to local copies of EEGBCI dataset files.
eegbci.standardize(raw)	Standardize channel positions and names.
fetch_aparc_sub_parcellation([subjects_dir, …])	Fetch the modified subdivided aparc parcellation.
fetch_fsaverage([subjects_dir, verbose])	Fetch and update fsaverage.
fetch_hcp_mmp_parcellation([subjects_dir, …])	Fetch the HCP-MMP parcellation.
fnirs_motor.data_path([path, force_update, …])	Get path to local copy of fnirs_motor dataset.
hf_sef.data_path([dataset, path, …])	Get path to local copy of the high frequency SEF dataset.
kiloword.data_path([path, force_update, …])	Get path to local copy of the kiloword dataset.
limo.load_data(subject[, path, …])	Fetch subjects epochs data for the LIMO data set.
misc.data_path([path, force_update, …])	Get path to local copy of misc dataset.
mtrf.data_path([path, force_update, …])	Get path to local copy of mtrf dataset.
multimodal.data_path([path, force_update, …])	Get path to local copy of multimodal dataset.
opm.data_path([path, force_update, …])	Get path to local copy of opm dataset.
sleep_physionet.age.fetch_data(subjects[, …])	Get paths to local copies of PhysioNet Polysomnography dataset files.
sleep_physionet.temazepam.fetch_data(subjects)	Get paths to local copies of PhysioNet Polysomnography dataset files.
sample.data_path([path, force_update, …])	Get path to local copy of sample dataset.
somato.data_path([path, force_update, …])	Get path to local copy of somato dataset.
spm_face.data_path([path, force_update, …])	Get path to local copy of spm dataset.
visual_92_categories.data_path([path, …])	Get path to local copy of visual_92_categories dataset.
phantom_4dbti.data_path([path, …])	Get path to local copy of phantom_4dbti dataset.
refmeg_noise.data_path([path, force_update, …])	Get path to local copy of refmeg_noise dataset.

Visualization

mne.viz:

Visualization routines.

Brain(subject_id, hemi, surf[, title, …])	Class for visualizing a brain.
ClickableImage(imdata, **kwargs)	Display an image so you can click on it and store x/y positions.
add_background_image(fig, im[, set_ratios])	Add a background image to a plot.
centers_to_edges(*arrays)	Convert center points to edges.
compare_fiff(fname_1, fname_2[, fname_out, …])	Compare the contents of two fiff files using diff and show_fiff.
circular_layout(node_names, node_order[, …])	Create layout arranging nodes on a circle.
iter_topography(info[, layout, on_pick, …])	Create iterator over channel positions.
mne_analyze_colormap([limits, format])	Return a colormap similar to that used by mne_analyze.
plot_bem([subject, subjects_dir, …])	Plot BEM contours on anatomical slices.
plot_brain_colorbar(ax, clim[, colormap, …])	Plot a colorbar that corresponds to a brain activation map.
plot_connectivity_circle(con, node_names[, …])	Visualize connectivity as a circular graph.
plot_cov(cov, info[, exclude, colorbar, …])	Plot Covariance data.
plot_csd(csd[, info, mode, colorbar, cmap, …])	Plot CSD matrices.
plot_dipole_amplitudes(dipoles[, colors, show])	Plot the amplitude traces of a set of dipoles.
plot_dipole_locations(dipoles[, trans, …])	Plot dipole locations.
plot_drop_log(drop_log[, threshold, …])	Show the channel stats based on a drop_log from Epochs.
plot_epochs(epochs[, picks, scalings, …])	Visualize epochs.
plot_epochs_psd_topomap(epochs[, bands, …])	Plot the topomap of the power spectral density across epochs.
plot_events(events[, sfreq, first_samp, …])	Plot events to get a visual display of the paradigm.
plot_evoked(evoked[, picks, exclude, unit, …])	Plot evoked data using butterfly plots.
plot_evoked_image(evoked[, picks, exclude, …])	Plot evoked data as images.
plot_evoked_topo(evoked[, layout, …])	Plot 2D topography of evoked responses.
plot_evoked_topomap(evoked[, times, …])	Plot topographic maps of specific time points of evoked data.
plot_evoked_joint(evoked[, times, title, …])	Plot evoked data as butterfly plot and add topomaps for time points.
plot_evoked_field(evoked, surf_maps[, time, …])	Plot MEG/EEG fields on head surface and helmet in 3D.
plot_evoked_white(evoked, noise_cov[, show, …])	Plot whitened evoked response.
plot_filter(h, sfreq[, freq, gain, title, …])	Plot properties of a filter.
plot_head_positions(pos[, mode, cmap, …])	Plot head positions.
plot_ideal_filter(freq, gain[, axes, title, …])	Plot an ideal filter response.
plot_compare_evokeds(evokeds[, picks, …])	Plot evoked time courses for one or more conditions and/or channels.
plot_ica_sources(ica, inst[, picks, start, …])	Plot estimated latent sources given the unmixing matrix.
plot_ica_components(ica[, picks, ch_type, …])	Project mixing matrix on interpolated sensor topography.
plot_ica_properties(ica, inst[, picks, …])	Display component properties.
plot_ica_scores(ica, scores[, exclude, …])	Plot scores related to detected components.
plot_ica_overlay(ica, inst[, exclude, …])	Overlay of raw and cleaned signals given the unmixing matrix.
plot_epochs_image(epochs[, picks, sigma, …])	Plot Event Related Potential / Fields image.
plot_layout(layout[, picks, show])	Plot the sensor positions.
plot_montage(montage[, scale_factor, …])	Plot a montage.
plot_projs_topomap(projs, info[, cmap, …])	Plot topographic maps of SSP projections.
plot_raw(raw[, events, duration, start, …])	Plot raw data.
plot_raw_psd(raw[, fmin, fmax, tmin, tmax, …])	Plot the power spectral density across channels.
plot_sensors(info[, kind, ch_type, title, …])	Plot sensors positions.
plot_sensors_connectivity(info, con[, picks])	Visualize the sensor connectivity in 3D.
plot_snr_estimate(evoked, inv[, show, axes, …])	Plot a data SNR estimate.
plot_source_estimates(stc[, subject, …])	Plot SourceEstimate.
link_brains(brains[, time, camera, …])	Plot multiple SourceEstimate objects with PyVista.
plot_volume_source_estimates(stc, src[, …])	Plot Nutmeg style volumetric source estimates using nilearn.
plot_vector_source_estimates(stc[, subject, …])	Plot VectorSourceEstimate with PySurfer.
plot_sparse_source_estimates(src, stcs[, …])	Plot source estimates obtained with sparse solver.
plot_tfr_topomap(tfr[, tmin, tmax, fmin, …])	Plot topographic maps of specific time-frequency intervals of TFR data.
plot_topo_image_epochs(epochs[, layout, …])	Plot Event Related Potential / Fields image on topographies.
plot_topomap(data, pos[, vmin, vmax, cmap, …])	Plot a topographic map as image.
plot_alignment([info, trans, subject, …])	Plot head, sensor, and source space alignment in 3D.
snapshot_brain_montage(fig, montage[, …])	Take a snapshot of a Mayavi Scene and project channels onto 2d coords.
plot_arrowmap(data, info_from[, info_to, …])	Plot arrow map.
set_3d_backend(backend_name[, verbose])	Set the backend for MNE.
get_3d_backend()	Return the backend currently used.
use_3d_backend(backend_name)	Create a viz context.
set_3d_options([antialias])	Set 3D rendering options.
set_3d_view(figure[, azimuth, elevation, …])	Configure the view of the given scene.
set_3d_title(figure, title[, size])	Configure the title of the given scene.
create_3d_figure(size[, bgcolor, …])	Return an empty figure based on the current 3d backend.
get_brain_class()	Return the proper Brain class based on the current 3d backend.

Preprocessing

Projections:

Projection	Projection vector.
compute_proj_epochs(epochs[, n_grad, n_mag, …])	Compute SSP (spatial space projection) vectors on Epochs.
compute_proj_evoked(evoked[, n_grad, n_mag, …])	Compute SSP (spatial space projection) vectors on Evoked.
compute_proj_raw(raw[, start, stop, …])	Compute SSP (spatial space projection) vectors on Raw.
read_proj(fname[, verbose])	Read projections from a FIF file.
write_proj(fname, projs)	Write projections to a FIF file.

mne.channels:

Module dedicated to manipulation of channels.

Can be used for setting of sensor locations used for processing and plotting.

Layout(box, pos, names, ids, kind)	Sensor layouts.
DigMontage([dev_head_t, dig, ch_names])	Montage for digitized electrode and headshape position data.
compute_native_head_t(montage)	Compute the native-to-head transformation for a montage.
fix_mag_coil_types(info[, use_cal])	Fix magnetometer coil types.
read_polhemus_fastscan(fname[, unit])	Read Polhemus FastSCAN digitizer data from a .txt file.
get_builtin_montages()	Get a list of all builtin montages.
make_dig_montage([ch_pos, nasion, lpa, rpa, …])	Make montage from arrays.
read_dig_polhemus_isotrak(fname[, ch_names, …])	Read Polhemus digitizer data from a file.
read_dig_captrak(fname)	Read electrode locations from CapTrak Brain Products system.
read_dig_dat(fname)	Read electrode positions from a *.dat file.
read_dig_egi(fname)	Read electrode locations from EGI system.
read_dig_fif(fname)	Read digitized points from a .fif file.
read_dig_hpts(fname[, unit])	Read historical .hpts mne-c files.
make_standard_montage(kind[, head_size])	Read a generic (built-in) montage.
read_custom_montage(fname[, head_size, …])	Read a montage from a file.
compute_dev_head_t(montage)	Compute device to head transform from a DigMontage.
read_layout(kind[, path, scale])	Read layout from a file.
find_layout(info[, ch_type, exclude])	Choose a layout based on the channels in the info ‘chs’ field.
make_eeg_layout(info[, radius, width, …])	Create .lout file from EEG electrode digitization.
make_grid_layout(info[, picks, n_col])	Generate .lout file for custom data, i.e., ICA sources.
find_ch_adjacency(info, ch_type)	Find the adjacency matrix for the given channels.
read_ch_adjacency(fname[, picks])	Parse FieldTrip neighbors .mat file.
equalize_channels(instances[, copy, verbose])	Equalize channel picks and ordering across multiple MNE-Python objects.
rename_channels(info, mapping)	Rename channels.
generate_2d_layout(xy[, w, h, pad, …])	Generate a custom 2D layout from xy points.
make_1020_channel_selections(info[, midline])	Return dict mapping from ROI names to lists of picks for 10/20 setups.
combine_channels(inst, groups[, method, …])	Combine channels based on specified channel grouping.

mne.preprocessing:

Preprocessing with artifact detection, SSP, and ICA.

ICA([n_components, max_pca_components, …])	M/EEG signal decomposition using Independent Component Analysis (ICA).
Xdawn([n_components, signal_cov, …])	Implementation of the Xdawn Algorithm.
annotate_flat(raw[, bad_percent, …])	Annotate flat segments of raw data (or add to a bad channel list).
annotate_movement(raw, pos[, …])	Detect segments with movement.
annotate_muscle_zscore(raw[, threshold, …])	Create annotations for segments that likely contain muscle artifacts.
compute_average_dev_head_t(raw, pos)	Get new device to head transform based on good segments.
compute_current_source_density(inst[, …])	Get the current source density (CSD) transformation.
compute_fine_calibration(raw[, n_imbalance, …])	Compute fine calibration from empty-room data.
compute_proj_ecg(raw[, raw_event, tmin, …])	Compute SSP/PCA projections for ECG artifacts.
compute_proj_eog(raw[, raw_event, tmin, …])	Compute SSP/PCA projections for EOG artifacts.
create_ecg_epochs(raw[, ch_name, event_id, …])	Conveniently generate epochs around ECG artifact events.
create_eog_epochs(raw[, ch_name, event_id, …])	Conveniently generate epochs around EOG artifact events.
find_bad_channels_maxwell(raw[, limit, …])	Find bad channels using Maxwell filtering.
find_ecg_events(raw[, event_id, ch_name, …])	Find ECG peaks.
find_eog_events(raw[, event_id, l_freq, …])	Locate EOG artifacts.
fix_stim_artifact(inst[, events, event_id, …])	Eliminate stimulation’s artifacts from instance.
ica_find_ecg_events(raw, ecg_source[, …])	Find ECG peaks from one selected ICA source.
ica_find_eog_events(raw[, eog_source, …])	Locate EOG artifacts from one selected ICA source.
infomax(data[, weights, l_rate, block, …])	Run (extended) Infomax ICA decomposition on raw data.
maxwell_filter(raw[, origin, int_order, …])	Maxwell filter data using multipole moments.
oversampled_temporal_projection(raw[, …])	Denoise MEG channels using leave-one-out temporal projection.
peak_finder(x0[, thresh, extrema, verbose])	Noise-tolerant fast peak-finding algorithm.
read_ica(fname[, verbose])	Restore ICA solution from fif file.
regress_artifact(inst[, picks, …])	Regress artifacts using reference channels.
corrmap(icas, template[, threshold, label, …])	Find similar Independent Components across subjects by map similarity.
read_ica_eeglab(fname)	Load ICA information saved in an EEGLAB .set file.
read_fine_calibration(fname)	Read fine calibration information from a .dat file.
write_fine_calibration(fname, calibration)	Write fine calibration information to a .dat file.

mne.preprocessing.nirs:

NIRS specific preprocessing functions.

optical_density(raw)	Convert NIRS raw data to optical density.
beer_lambert_law(raw[, ppf])	Convert NIRS optical density data to haemoglobin concentration.
source_detector_distances(info[, picks])	Determine the distance between NIRS source and detectors.
short_channels(info[, threshold])	Determine which NIRS channels are short.
scalp_coupling_index(raw[, l_freq, h_freq, …])	Calculate scalp coupling index.
temporal_derivative_distribution_repair(raw)	Apply temporal derivative distribution repair to data.

EEG referencing:

add_reference_channels(inst, ref_channels[, …])	Add reference channels to data that consists of all zeros.
set_bipolar_reference(inst, anode, cathode)	Re-reference selected channels using a bipolar referencing scheme.
set_eeg_reference(inst[, ref_channels, …])	Specify which reference to use for EEG data.

mne.filter:

IIR and FIR filtering and resampling functions.

construct_iir_filter(iir_params[, f_pass, …])	Use IIR parameters to get filtering coefficients.
create_filter(data, sfreq, l_freq, h_freq[, …])	Create a FIR or IIR filter.
estimate_ringing_samples(system[, max_try])	Estimate filter ringing.
filter_data(data, sfreq, l_freq, h_freq[, …])	Filter a subset of channels.
notch_filter(x, Fs, freqs[, filter_length, …])	Notch filter for the signal x.
resample(x[, up, down, npad, axis, window, …])	Resample an array.

mne.chpi

Functions for fitting head positions with (c)HPI coils.

compute_chpi_amplitudes(raw[, t_step_min, …])	Compute time-varying cHPI amplitudes.
compute_chpi_locs(info, chpi_amplitudes[, …])	Compute locations of each cHPI coils over time.
compute_head_pos(info, chpi_locs[, …])	Compute time-varying head positions.
extract_chpi_locs_ctf(raw[, verbose])	Extract cHPI locations from CTF data.
filter_chpi(raw[, include_line, t_step, …])	Remove cHPI and line noise from data.
head_pos_to_trans_rot_t(quats)	Convert Maxfilter-formatted head position quaternions.
read_head_pos(fname)	Read MaxFilter-formatted head position parameters.
write_head_pos(fname, pos)	Write MaxFilter-formatted head position parameters.

mne.transforms

Helpers for various transformations.

Transform(fro, to[, trans])	A transform.
quat_to_rot(quat)	Convert a set of quaternions to rotations.
rot_to_quat(rot)	Convert a set of rotations to quaternions.
read_ras_mni_t(subject[, subjects_dir])	Read a subject’s RAS to MNI transform.

Events

Annotations(onset, duration, description[, …])	Annotation object for annotating segments of raw data.
AcqParserFIF(info)	Parser for Elekta data acquisition settings.
concatenate_events(events, first_samps, …)	Concatenate event lists to be compatible with concatenate_raws.
find_events(raw[, stim_channel, output, …])	Find events from raw file.
find_stim_steps(raw[, pad_start, pad_stop, …])	Find all steps in data from a stim channel.
make_fixed_length_events(raw[, id, start, …])	Make a set of events separated by a fixed duration.
make_fixed_length_epochs(raw[, duration, …])	Divide continuous raw data into equal-sized consecutive epochs.
merge_events(events, ids, new_id[, …])	Merge a set of events.
parse_config(fname)	Parse a config file (like .ave and .cov files).
pick_events(events[, include, exclude, step])	Select some events.
read_annotations(fname[, sfreq, uint16_codec])	Read annotations from a file.
read_events(filename[, include, exclude, …])	Read events from fif or text file.
write_events(filename, event_list)	Write events to file.
concatenate_epochs(epochs_list[, add_offset])	Concatenate a list of epochs into one epochs object.
events_from_annotations(raw[, event_id, …])	Get events and event_id from an Annotations object.
annotations_from_events(events, sfreq[, …])	Convert an event array to an Annotations object.

mne.event:

IO with fif files containing events.

define_target_events(events, reference_id, …)	Define new events by co-occurrence of existing events.
shift_time_events(events, ids, tshift, sfreq)	Shift an event.

mne.epochs:

Tools for working with epoched data.

add_channels_epochs(epochs_list[, verbose])	Concatenate channels, info and data from two Epochs objects.
average_movements(epochs[, head_pos, …])	Average data using Maxwell filtering, transforming using head positions.
combine_event_ids(epochs, old_event_ids, …)	Collapse event_ids from an epochs instance into a new event_id.
equalize_epoch_counts(epochs_list[, method])	Equalize the number of trials in multiple Epoch instances.

Sensor Space Data

combine_evoked(all_evoked, weights)	Merge evoked data by weighted addition or subtraction.
concatenate_raws(raws[, preload, …])	Concatenate raw instances as if they were continuous.
equalize_channels(instances[, copy, verbose])	Equalize channel picks and ordering across multiple MNE-Python objects.
grand_average(all_inst[, interpolate_bads, …])	Make grand average of a list of Evoked or AverageTFR data.
pick_channels(ch_names, include[, exclude, …])	Pick channels by names.
pick_channels_cov(orig[, include, exclude, …])	Pick channels from covariance matrix.
pick_channels_forward(orig[, include, …])	Pick channels from forward operator.
pick_channels_regexp(ch_names, regexp)	Pick channels using regular expression.
pick_types(info[, meg, eeg, stim, eog, ecg, …])	Pick channels by type and names.
pick_types_forward(orig[, meg, eeg, …])	Pick by channel type and names from a forward operator.
pick_info(info[, sel, copy, verbose])	Restrict an info structure to a selection of channels.
read_epochs(fname[, proj, preload, verbose])	Read epochs from a fif file.
read_reject_parameters(fname)	Read rejection parameters from .cov or .ave config file.
read_selection(name[, fname, info, verbose])	Read channel selection from file.
rename_channels(info, mapping)	Rename channels.

mne.baseline:

Utility functions to baseline-correct data.

rescale(data, times, baseline[, mode, copy, …])

Rescale (baseline correct) data.

Covariance computation

Covariance(data, names, bads, projs, nfree)	Noise covariance matrix.
compute_covariance(epochs[, …])	Estimate noise covariance matrix from epochs.
compute_raw_covariance(raw[, tmin, tmax, …])	Estimate noise covariance matrix from a continuous segment of raw data.
cov.compute_whitener(noise_cov[, info, …])	Compute whitening matrix.
cov.prepare_noise_cov(noise_cov, info[, …])	Prepare noise covariance matrix.
cov.regularize(cov, info[, mag, grad, eeg, …])	Regularize noise covariance matrix.
compute_rank(inst[, rank, scalings, info, …])	Compute the rank of data or noise covariance.
make_ad_hoc_cov(info[, std, verbose])	Create an ad hoc noise covariance.
read_cov(fname[, verbose])	Read a noise covariance from a FIF file.
write_cov(fname, cov)	Write a noise covariance matrix.

MRI Processing

Step by step instructions for using gui.coregistration():

• Coregistration for subjects with structural MRI

• Scaling a template MRI for subjects for which no MRI is available

coreg.get_mni_fiducials(subject[, …])	Estimate fiducials for a subject.
gui.coregistration([tabbed, split, width, …])	Coregister an MRI with a subject’s head shape.
gui.fiducials([subject, fid_file, subjects_dir])	Set the fiducials for an MRI subject.
create_default_subject([fs_home, update, …])	Create an average brain subject for subjects without structural MRI.
scale_mri(subject_from, subject_to, scale[, …])	Create a scaled copy of an MRI subject.
scale_bem(subject_to, bem_name[, …])	Scale a bem file.
scale_labels(subject_to[, pattern, …])	Scale labels to match a brain that was previously created by scaling.
scale_source_space(subject_to, src_name[, …])	Scale a source space for an mri created with scale_mri().

Forward Modeling

Forward	Forward class to represent info from forward solution.
SourceSpaces(source_spaces[, info])	Represent a list of source space.
add_source_space_distances(src[, …])	Compute inter-source distances along the cortical surface.
apply_forward(fwd, stc, info[, start, stop, …])	Project source space currents to sensor space using a forward operator.
apply_forward_raw(fwd, stc, info[, start, …])	Project source space currents to sensor space using a forward operator.
average_forward_solutions(fwds[, weights, …])	Average forward solutions.
convert_forward_solution(fwd[, surf_ori, …])	Convert forward solution between different source orientations.
decimate_surface(points, triangles, n_triangles)	Decimate surface data.
dig_mri_distances(info, trans, subject[, …])	Compute distances between head shape points and the scalp surface.
forward.compute_depth_prior(forward, info[, …])	Compute depth prior for depth weighting.
forward.compute_orient_prior(forward[, …])	Compute orientation prior.
forward.restrict_forward_to_label(fwd, labels)	Restrict forward operator to labels.
forward.restrict_forward_to_stc(fwd, stc[, …])	Restrict forward operator to active sources in a source estimate.
make_bem_model(subject[, ico, conductivity, …])	Create a BEM model for a subject.
make_bem_solution(surfs[, verbose])	Create a BEM solution using the linear collocation approach.
make_forward_dipole(dipole, bem, info[, …])	Convert dipole object to source estimate and calculate forward operator.
make_forward_solution(info, trans, src, bem)	Calculate a forward solution for a subject.
make_field_map(evoked[, trans, subject, …])	Compute surface maps used for field display in 3D.
make_sphere_model([r0, head_radius, info, …])	Create a spherical model for forward solution calculation.
morph_source_spaces(src_from, subject_to[, …])	Morph an existing source space to a different subject.
read_bem_surfaces(fname[, patch_stats, …])	Read the BEM surfaces from a FIF file.
read_forward_solution(fname[, include, …])	Read a forward solution a.k.a.
read_trans(fname[, return_all, verbose])	Read a -trans.fif file.
read_source_spaces(fname[, patch_stats, verbose])	Read the source spaces from a FIF file.
read_surface(fname[, read_metadata, …])	Load a Freesurfer surface mesh in triangular format.
sensitivity_map(fwd[, projs, ch_type, mode, …])	Compute sensitivity map.
setup_source_space(subject[, spacing, …])	Set up bilateral hemisphere surface-based source space with subsampling.
setup_volume_source_space([subject, pos, …])	Set up a volume source space with grid spacing or discrete source space.
surface.complete_surface_info(surf[, …])	Complete surface information.
surface.read_curvature(filepath[, binary])	Load in curvature values from the ?h.curv file.
use_coil_def(fname)	Use a custom coil definition file.
write_bem_surfaces(fname, surfs[, …])	Write BEM surfaces to a fiff file.
write_trans(fname, trans)	Write a -trans.fif file.

mne.bem:

ConductorModel	BEM or sphere model.
fit_sphere_to_headshape(info[, dig_kinds, …])	Fit a sphere to the headshape points to determine head center.
get_fitting_dig(info[, dig_kinds, …])	Get digitization points suitable for sphere fitting.
make_watershed_bem(subject[, subjects_dir, …])	Create BEM surfaces using the FreeSurfer watershed algorithm.
make_flash_bem(subject[, overwrite, show, …])	Create 3-Layer BEM model from prepared flash MRI images.
convert_flash_mris(subject[, flash30, …])	Convert DICOM files for use with make_flash_bem.

Inverse Solutions

mne.minimum_norm:

Linear inverse solvers based on L2 Minimum Norm Estimates (MNE).

InverseOperator	InverseOperator class to represent info from inverse operator.
apply_inverse(evoked, inverse_operator[, …])	Apply inverse operator to evoked data.
apply_inverse_cov(cov, info, inverse_operator)	Apply inverse operator to covariance data.
apply_inverse_epochs(epochs, …[, method, …])	Apply inverse operator to Epochs.
apply_inverse_raw(raw, inverse_operator, lambda2)	Apply inverse operator to Raw data.
compute_source_psd(raw, inverse_operator[, …])	Compute source power spectral density (PSD).
compute_source_psd_epochs(epochs, …[, …])	Compute source power spectral density (PSD) from Epochs.
compute_rank_inverse(inv)	Compute the rank of a linear inverse operator (MNE, dSPM, etc.).
estimate_snr(evoked, inv[, verbose])	Estimate the SNR as a function of time for evoked data.
make_inverse_operator(info, forward, noise_cov)	Assemble inverse operator.
prepare_inverse_operator(orig, nave, lambda2)	Prepare an inverse operator for actually computing the inverse.
read_inverse_operator(fname[, verbose])	Read the inverse operator decomposition from a FIF file.
source_band_induced_power(epochs, …[, …])	Compute source space induced power in given frequency bands.
source_induced_power(epochs, …[, label, …])	Compute induced power and phase lock.
write_inverse_operator(fname, inv[, verbose])	Write an inverse operator to a FIF file.
make_inverse_resolution_matrix(forward, …)	Compute resolution matrix for linear inverse operator.
resolution_metrics(resmat, src[, function, …])	Compute spatial resolution metrics for linear solvers.
get_cross_talk(resmat, src, idx[, norm])	Get cross-talk (CTFs) function for vertices.
get_point_spread(resmat, src, idx[, norm])	Get point-spread (PSFs) functions for vertices.

mne.inverse_sparse:

Non-Linear sparse inverse solvers.

mixed_norm(evoked, forward, noise_cov, alpha)	Mixed-norm estimate (MxNE) and iterative reweighted MxNE (irMxNE).
tf_mixed_norm(evoked, forward, noise_cov[, …])	Time-Frequency Mixed-norm estimate (TF-MxNE).
gamma_map(evoked, forward, noise_cov, alpha)	Hierarchical Bayes (Gamma-MAP) sparse source localization method.
make_stc_from_dipoles(dipoles, src[, verbose])	Convert a list of spatio-temporal dipoles into a SourceEstimate.

mne.beamformer:

Beamformers for source localization.

Beamformer	A computed beamformer.
read_beamformer(fname)	Read a beamformer filter.
make_lcmv(info, forward, data_cov[, reg, …])	Compute LCMV spatial filter.
apply_lcmv(evoked, filters[, max_ori_out, …])	Apply Linearly Constrained Minimum Variance (LCMV) beamformer weights.
apply_lcmv_epochs(epochs, filters[, …])	Apply Linearly Constrained Minimum Variance (LCMV) beamformer weights.
apply_lcmv_raw(raw, filters[, start, stop, …])	Apply Linearly Constrained Minimum Variance (LCMV) beamformer weights.
apply_lcmv_cov(data_cov, filters[, verbose])	Apply Linearly Constrained Minimum Variance (LCMV) beamformer weights.
make_dics(info, forward, csd[, reg, …])	Compute a Dynamic Imaging of Coherent Sources (DICS) spatial filter.
apply_dics(evoked, filters[, verbose])	Apply Dynamic Imaging of Coherent Sources (DICS) beamformer weights.
apply_dics_csd(csd, filters[, verbose])	Apply Dynamic Imaging of Coherent Sources (DICS) beamformer weights.
apply_dics_epochs(epochs, filters[, …])	Apply Dynamic Imaging of Coherent Sources (DICS) beamformer weights.
rap_music(evoked, forward, noise_cov[, …])	RAP-MUSIC source localization method.
tf_dics(epochs, forward, noise_csds, tmin, …)	5D time-frequency beamforming based on DICS.
tf_lcmv(epochs, forward, noise_covs, tmin, …)	Warning DEPRECATED: tf_lcmv is deprecated and will be removed in 0.22, use LCMV with a covariances computed on band-passed data or DICS instead
make_lcmv_resolution_matrix(filters, …)	Compute resolution matrix for LCMV beamformer.

Dipole(times, pos, amplitude, ori, gof[, …])	Dipole class for sequential dipole fits.
DipoleFixed(info, data, times, nave, aspect_kind)	Dipole class for fixed-position dipole fits.
fit_dipole(evoked, cov, bem[, trans, …])	Fit a dipole.

mne.dipole:

Single-dipole functions and classes.

get_phantom_dipoles([kind])

Get standard phantom dipole locations and orientations.

Source Space Data

BiHemiLabel(lh, rh[, name, color])	A freesurfer/MNE label with vertices in both hemispheres.
Label([vertices, pos, values, hemi, …])	A FreeSurfer/MNE label with vertices restricted to one hemisphere.
MixedSourceEstimate(data[, vertices, tmin, …])	Container for mixed surface and volume source estimates.
MixedVectorSourceEstimate(data[, vertices, …])	Container for volume source estimates.
SourceEstimate(data, vertices, tmin, tstep)	Container for surface source estimates.
VectorSourceEstimate(data[, vertices, tmin, …])	Container for vector surface source estimates.
VolSourceEstimate(data, vertices, tmin, tstep)	Container for volume source estimates.
VolVectorSourceEstimate(data[, vertices, …])	Container for volume source estimates.
SourceMorph(subject_from, subject_to, kind, …)	Morph source space data from one subject to another.
compute_source_morph(src[, subject_from, …])	Create a SourceMorph from one subject to another.
head_to_mni(pos, subject, mri_head_t[, …])	Convert pos from head coordinate system to MNI ones.
head_to_mri(pos, subject, mri_head_t[, …])	Convert pos from head coordinate system to MRI ones.
extract_label_time_course(stcs, labels, src)	Extract label time course for lists of labels and source estimates.
grade_to_tris(grade[, verbose])	Get tris defined for a certain grade.
grade_to_vertices(subject, grade[, …])	Convert a grade to source space vertices for a given subject.
label.select_sources(subject, label[, …])	Select sources from a label.
grow_labels(subject, seeds, extents, hemis)	Generate circular labels in source space with region growing.
label_sign_flip(label, src)	Compute sign for label averaging.
labels_to_stc(labels, values[, tmin, tstep, …])	Convert a set of labels and values to a STC.
morph_labels(labels, subject_to[, …])	Morph a set of labels.
random_parcellation(subject, n_parcel, hemi)	Generate random cortex parcellation by growing labels.
read_labels_from_annot(subject[, parc, …])	Read labels from a FreeSurfer annotation file.
read_dipole(fname[, verbose])	Read .dip file from Neuromag/xfit or MNE.
read_label(filename[, subject, color])	Read FreeSurfer Label file.
read_source_estimate(fname[, subject])	Read a source estimate object.
read_source_morph(fname)	Load the morph for source estimates from a file.
split_label(label[, parts, subject, …])	Split a Label into two or more parts.
stc_to_label(stc[, src, smooth, connected, …])	Compute a label from the non-zero sources in an stc object.
transform_surface_to(surf, dest, trans[, copy])	Transform surface to the desired coordinate system.
vertex_to_mni(vertices, hemis, subject[, …])	Convert the array of vertices for a hemisphere to MNI coordinates.
write_labels_to_annot(labels[, subject, …])	Create a FreeSurfer annotation from a list of labels.
write_label(filename, label[, verbose])	Write a FreeSurfer label.

Time-Frequency

mne.time_frequency:

Time frequency analysis tools.

AverageTFR(info, data, times, freqs, nave[, …])	Container for Time-Frequency data.
EpochsTFR(info, data, times, freqs[, …])	Container for Time-Frequency data on epochs.
CrossSpectralDensity(data, ch_names, …[, …])	Cross-spectral density.

Functions that operate on mne-python objects:

csd_fourier(epochs[, fmin, fmax, tmin, …])	Estimate cross-spectral density from an array using short-time fourier.
csd_multitaper(epochs[, fmin, fmax, tmin, …])	Estimate cross-spectral density from epochs using a multitaper method.
csd_morlet(epochs, frequencies[, tmin, …])	Estimate cross-spectral density from epochs using Morlet wavelets.
pick_channels_csd(csd[, include, exclude, …])	Pick channels from cross-spectral density matrix.
read_csd(fname)	Read a CrossSpectralDensity object from an HDF5 file.
fit_iir_model_raw(raw[, order, picks, tmin, …])	Fit an AR model to raw data and creates the corresponding IIR filter.
psd_welch(inst[, fmin, fmax, tmin, tmax, …])	Compute the power spectral density (PSD) using Welch’s method.
psd_multitaper(inst[, fmin, fmax, tmin, …])	Compute the power spectral density (PSD) using multitapers.
tfr_morlet(inst, freqs, n_cycles[, use_fft, …])	Compute Time-Frequency Representation (TFR) using Morlet wavelets.
tfr_multitaper(inst, freqs, n_cycles[, …])	Compute Time-Frequency Representation (TFR) using DPSS tapers.
tfr_stockwell(inst[, fmin, fmax, n_fft, …])	Compute Time-Frequency Representation (TFR) using Stockwell Transform.
read_tfrs(fname[, condition])	Read TFR datasets from hdf5 file.
write_tfrs(fname, tfr[, overwrite])	Write a TFR dataset to hdf5.

Functions that operate on np.ndarray objects:

csd_array_fourier(X, sfreq[, t0, fmin, …])	Estimate cross-spectral density from an array using short-time fourier.
csd_array_multitaper(X, sfreq[, t0, fmin, …])	Estimate cross-spectral density from an array using a multitaper method.
csd_array_morlet(X, sfreq, frequencies[, …])	Estimate cross-spectral density from an array using Morlet wavelets.
dpss_windows(N, half_nbw, Kmax[, low_bias, …])	Compute Discrete Prolate Spheroidal Sequences.
morlet(sfreq, freqs[, n_cycles, sigma, …])	Compute Morlet wavelets for the given frequency range.
stft(x, wsize[, tstep, verbose])	STFT Short-Term Fourier Transform using a sine window.
istft(X[, tstep, Tx])	ISTFT Inverse Short-Term Fourier Transform using a sine window.
stftfreq(wsize[, sfreq])	Compute frequencies of stft transformation.
psd_array_multitaper(x, sfreq[, fmin, fmax, …])	Compute power spectral density (PSD) using a multi-taper method.
psd_array_welch(x, sfreq[, fmin, fmax, …])	Compute power spectral density (PSD) using Welch’s method.
tfr_array_morlet(epoch_data, sfreq, freqs[, …])	Compute Time-Frequency Representation (TFR) using Morlet wavelets.
tfr_array_multitaper(epoch_data, sfreq, freqs)	Compute Time-Frequency Representation (TFR) using DPSS tapers.
tfr_array_stockwell(data, sfreq[, fmin, …])	Compute power and intertrial coherence using Stockwell (S) transform.

mne.time_frequency.tfr:

A module which implements the time-frequency estimation.

Morlet code inspired by Matlab code from Sheraz Khan & Brainstorm & SPM

cwt(X, Ws[, use_fft, mode, decim])	Compute time freq decomposition with continuous wavelet transform.
morlet(sfreq, freqs[, n_cycles, sigma, …])	Compute Morlet wavelets for the given frequency range.

Connectivity Estimation

mne.connectivity:

Spectral and effective connectivity measures.

degree(connectivity[, threshold_prop])	Compute the undirected degree of a connectivity matrix.
envelope_correlation(data[, combine, …])	Compute the envelope correlation.
phase_slope_index(data[, indices, sfreq, …])	Compute the Phase Slope Index (PSI) connectivity measure.
seed_target_indices(seeds, targets)	Generate indices parameter for seed based connectivity analysis.
spectral_connectivity(data[, method, …])	Compute frequency- and time-frequency-domain connectivity measures.

Statistics

mne.stats:

Functions for statistical analysis.

Parametric statistics (see scipy.stats and statsmodels for more options):

ttest_1samp_no_p(X[, sigma, method])	Perform one-sample t-test.
ttest_ind_no_p(a, b[, equal_var, sigma])	Independent samples t-test without p calculation.
f_oneway(*args)	Perform a 1-way ANOVA.
f_mway_rm(data, factor_levels[, effects, …])	Compute M-way repeated measures ANOVA for fully balanced designs.
f_threshold_mway_rm(n_subjects, factor_levels)	Compute F-value thresholds for a two-way ANOVA.
linear_regression(inst, design_matrix[, names])	Fit Ordinary Least Squares regression (OLS).
linear_regression_raw(raw, events[, …])	Estimate regression-based evoked potentials/fields by linear modeling.

Mass-univariate multiple comparison correction:

bonferroni_correction(pval[, alpha])	P-value correction with Bonferroni method.
fdr_correction(pvals[, alpha, method])	P-value correction with False Discovery Rate (FDR).

Non-parametric (clustering) resampling methods:

combine_adjacency(*structure)	Create a sparse binary adjacency/neighbors matrix.
permutation_cluster_test(X[, threshold, …])	Cluster-level statistical permutation test.
permutation_cluster_1samp_test(X[, …])	Non-parametric cluster-level paired t-test.
permutation_t_test(X[, n_permutations, …])	One sample/paired sample permutation test based on a t-statistic.
spatio_temporal_cluster_test(X[, threshold, …])	Non-parametric cluster-level test for spatio-temporal data.
spatio_temporal_cluster_1samp_test(X[, …])	Non-parametric cluster-level paired t-test for spatio-temporal data.
summarize_clusters_stc(clu[, p_thresh, …])	Assemble summary SourceEstimate from spatiotemporal cluster results.
bootstrap_confidence_interval(arr[, ci, …])	Get confidence intervals from non-parametric bootstrap.

Compute adjacency matrices for cluster-level statistics:

channels.find_ch_adjacency(info, ch_type)	Find the adjacency matrix for the given channels.
channels.read_ch_adjacency(fname[, picks])	Parse FieldTrip neighbors .mat file.
spatial_dist_adjacency(src, dist[, verbose])	Compute adjacency from distances in a source space.
spatial_src_adjacency(src[, dist, verbose])	Compute adjacency for a source space activation.
spatial_tris_adjacency(tris[, …])	Compute adjacency from triangles.
spatial_inter_hemi_adjacency(src, dist[, …])	Get vertices on each hemisphere that are close to the other hemisphere.
spatio_temporal_src_adjacency(src, n_times)	Compute adjacency for a source space activation over time.
spatio_temporal_tris_adjacency(tris, n_times)	Compute adjacency from triangles and time instants.
spatio_temporal_dist_adjacency(src, n_times, …)	Compute adjacency from distances in a source space and time instants.

Simulation

mne.simulation:

Data simulation code.

add_chpi(raw[, head_pos, interp, n_jobs, …])	Add cHPI activations to raw data.
add_ecg(raw[, head_pos, interp, n_jobs, …])	Add ECG noise to raw data.
add_eog(raw[, head_pos, interp, n_jobs, …])	Add blink noise to raw data.
add_noise(inst, cov[, iir_filter, …])	Create noise as a multivariate Gaussian.
simulate_evoked(fwd, stc, info, cov[, nave, …])	Generate noisy evoked data.
simulate_raw(info[, stc, trans, src, bem, …])	Simulate raw data.
simulate_stc(src, labels, stc_data, tmin, tstep)	Simulate sources time courses from waveforms and labels.
simulate_sparse_stc(src, n_dipoles, times[, …])	Generate sparse (n_dipoles) sources time courses from data_fun.
select_source_in_label(src, label[, …])	Select source positions using a label.
SourceSimulator(src[, tstep, duration])	Class to generate simulated Source Estimates.

Decoding

mne.decoding:

Decoding and encoding, including machine learning and receptive fields.

CSP([n_components, reg, log, cov_est, …])	M/EEG signal decomposition using the Common Spatial Patterns (CSP).
EMS()	Transformer to compute event-matched spatial filters.
FilterEstimator(info, l_freq, h_freq[, …])	Estimator to filter RtEpochs.
LinearModel([model])	Compute and store patterns from linear models.
PSDEstimator([sfreq, fmin, fmax, bandwidth, …])	Compute power spectral density (PSD) using a multi-taper method.
Scaler([info, scalings, with_mean, with_std])	Standardize channel data.
TemporalFilter([l_freq, h_freq, sfreq, …])	Estimator to filter data array along the last dimension.
TimeFrequency(freqs[, sfreq, method, …])	Time frequency transformer.
UnsupervisedSpatialFilter(estimator[, average])	Use unsupervised spatial filtering across time and samples.
Vectorizer()	Transform n-dimensional array into 2D array of n_samples by n_features.
ReceptiveField(tmin, tmax, sfreq[, …])	Fit a receptive field model.
TimeDelayingRidge(tmin, tmax, sfreq[, …])	Ridge regression of data with time delays.
SlidingEstimator(base_estimator[, scoring, …])	Search Light.
GeneralizingEstimator(base_estimator[, …])	Generalization Light.
SPoC([n_components, reg, log, …])	Implementation of the SPoC spatial filtering.

Functions that assist with decoding and model fitting:

compute_ems(epochs[, conditions, picks, …])	Compute event-matched spatial filter on epochs.
cross_val_multiscore(estimator, X[, y, …])	Evaluate a score by cross-validation.
get_coef(estimator[, attr, inverse_transform])	Retrieve the coefficients of an estimator ending with a Linear Model.

Realtime

Realtime functionality has moved to the standalone module mne_realtime.

MNE-Report

mne:

Report([info_fname, subjects_dir, subject, …])	Object for rendering HTML.
open_report(fname, **params)	Read a saved report or, if it doesn’t exist yet, create a new one.

Logging and Configuration

get_config_path([home_dir])	Get path to standard mne-python config file.
get_config([key, default, raise_error, …])	Read MNE-Python preferences from environment or config file.
open_docs([kind, version])	Launch a new web browser tab with the MNE documentation.
set_log_level([verbose, return_old_level, …])	Set the logging level.
set_log_file([fname, output_format, overwrite])	Set the log to print to a file.
set_config(key, value[, home_dir, set_env])	Set a MNE-Python preference key in the config file and environment.
set_cache_dir(cache_dir)	Set the directory to be used for temporary file storage.
sys_info([fid, show_paths])	Print the system information for debugging.
verbose(function)	Verbose decorator to allow functions to override log-level.

mne.utils:

deprecated([extra])	Mark a function or class as deprecated (decorator).
warn(message[, category, module])	Emit a warning with trace outside the mne namespace.

mne.cuda:

get_cuda_memory([kind])	Get the amount of free memory for CUDA operations.
init_cuda([ignore_config, verbose])	Initialize CUDA functionality.
set_cuda_device(device_id[, verbose])	Set the CUDA device temporarily for the current session.