Python API Reference

This is the classes and functions reference of MNE-Python. Functions are 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.

Classes

io.Raw(fname[, allow_maxshield, preload, ...]) Raw data in FIF format
io.RawFIF alias of Raw
io.RawArray(data, info[, first_samp, verbose]) Raw object from numpy array
Annotations(onset, duration, description[, ...]) Annotation object for annotating segments of raw data.
AcqParserFIF(info) Parser for Elekta data acquisition settings.
Epochs(raw, events[, event_id, tmin, tmax, ...]) Epochs extracted from a Raw instance
Evoked(fname[, condition, baseline, proj, ...]) Evoked data
SourceSpaces(source_spaces[, info]) Represent a list of source space
Forward Forward class to represent info from forward solution
SourceEstimate(data[, vertices, tmin, ...]) Container for surface source estimates
VolSourceEstimate(data[, vertices, tmin, ...]) Container for volume source estimates
MixedSourceEstimate(data[, vertices, tmin, ...]) Container for mixed surface and volume source estimates
Covariance(data, names, bads, projs, nfree) Noise covariance matrix.
Dipole(times, pos, amplitude, ori, gof[, name]) Dipole class for sequential dipole fits
DipoleFixed(info, data, times, nave, ...[, ...]) Dipole class for fixed-position dipole fits
Label(vertices[, pos, values, hemi, ...]) A FreeSurfer/MNE label with vertices restricted to one hemisphere
BiHemiLabel(lh, rh[, name, color]) A freesurfer/MNE label with vertices in both hemispheres
Transform(fro, to, trans) A transform
Report([info_fname, subjects_dir, subject, ...]) Object for rendering HTML
Info Information about the recording.
Projection Projection vector
preprocessing.ICA([n_components, ...]) M/EEG signal decomposition using Independent Component Analysis (ICA)
preprocessing.Xdawn([n_components, ...]) Implementation of the Xdawn Algorithm.
decoding.CSP([n_components, reg, log, ...]) M/EEG signal decomposition using the Common Spatial Patterns (CSP).
decoding.EpochsVectorizer(\*args, \*\*kwargs) EpochsVectorizer transforms epoch data to fit into a scikit-learn pipeline.
decoding.FilterEstimator(info, l_freq, h_freq) Estimator to filter RtEpochs
decoding.GeneralizationAcrossTime([picks, ...]) Generalize across time and conditions
decoding.PSDEstimator([sfreq, fmin, fmax, ...]) Compute power spectrum density (PSD) using a multi-taper method
decoding.Scaler(info[, with_mean, with_std]) Standardizes data across channels
decoding.TimeDecoding([picks, cv, clf, ...]) Train and test a series of classifiers at each time point to obtain a score across time.
realtime.RtEpochs(client, event_id, tmin, tmax) Realtime Epochs
realtime.RtClient(host[, cmd_port, ...]) Realtime Client
realtime.MockRtClient(raw[, verbose]) Mock Realtime Client
realtime.FieldTripClient([info, host, port, ...]) Realtime FieldTrip client
realtime.StimServer([port, n_clients]) Stimulation Server
realtime.StimClient(host[, port, timeout, ...]) Stimulation Client

Logging and Configuration

get_config_path([home_dir]) Get path to standard mne-python config file
get_config([key, default, raise_error, home_dir]) Read mne(-python) preference from env, then mne-python config
set_log_level([verbose, return_old_level]) Convenience function for setting the logging level
set_log_file([fname, output_format, overwrite]) Convenience function for setting the log to print to a file
set_config(key, value[, home_dir, set_env]) Set mne-python preference in config
sys_info([fid, show_paths]) Print the system information for debugging

mne.cuda:

init_cuda([ignore_config]) Initialize CUDA functionality

Reading raw data

mne.io:

Classes:

Raw(fname[, allow_maxshield, preload, proj, ...]) Raw data in FIF format

Functions:

read_raw_bti(pdf_fname[, config_fname, ...]) Raw object from 4D Neuroimaging MagnesWH3600 data
read_raw_cnt(input_fname, montage[, eog, ...]) Read CNT data as raw object.
read_raw_ctf(directory[, system_clock, ...]) Raw object from CTF directory
read_raw_edf(input_fname[, montage, eog, ...]) Reader function for EDF+, BDF conversion to FIF
read_raw_kit(input_fname[, mrk, elp, hsp, ...]) Reader function for KIT conversion to FIF
read_raw_nicolet(input_fname, ch_type[, ...]) Read Nicolet data as raw object
read_raw_eeglab(input_fname[, montage, eog, ...]) Read an EEGLAB .set file
read_raw_brainvision(vhdr_fname[, montage, ...]) Reader for Brain Vision EEG file
read_raw_egi(input_fname[, montage, eog, ...]) Read EGI simple binary as raw object
read_raw_fif(fname[, allow_maxshield, ...]) Reader function for Raw FIF data

mne.io.kit:

read_mrk(fname) Marker Point Extraction in MEG space directly from sqd

File I/O

Functions:

decimate_surface(points, triangles, n_triangles) Decimate surface data
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) Returns a list of names of segmented volumes.
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, add_eeg_ref, ...]) Read epochs from a fif file
read_epochs_kit(input_fname, events[, ...]) Reader function for KIT epochs files
read_epochs_eeglab(input_fname[, events, ...]) Reader function for EEGLAB epochs files
read_events(filename[, include, exclude, ...]) Reads events from fif or text file
read_evokeds(fname[, condition, baseline, ...]) Read evoked dataset(s)
read_forward_solution(fname[, force_fixed, ...]) 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) 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 soure estimate object
read_source_spaces(fname[, patch_stats, verbose]) Read the source spaces from a FIF file
read_surface(fname[, read_metadata, verbose]) Load a Freesurfer surface mesh in triangular format
read_trans(fname) Read a -trans.fif file
read_tri(fname_in[, swap, verbose]) Function for reading triangle definitions from an ascii file.
save_stc_as_volume(fname, stc, src[, dest, ...]) Save a volume source estimate in a nifti file
write_labels_to_annot(labels[, subject, ...]) Create a FreeSurfer annotation from a list of labels
write_bem_solution(fname, bem) 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[, verbose]) 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
io.read_info(fname[, verbose]) Read measurement info from a file

Creating data objects from arrays

Classes:

mne:

EvokedArray(data, info, tmin[, comment, ...]) Evoked object from numpy array
EpochsArray(data, info[, events, tmin, ...]) Epochs object from numpy array

mne.io:

RawArray(data, info[, first_samp, verbose]) Raw object from numpy array

Functions:

mne:

create_info(ch_names, sfreq[, ch_types, montage]) Create a basic Info instance suitable for use with create_raw

Sample datasets

mne.datasets.sample:

MNE sample dataset

data_path([path, force_update, update_path, ...]) Get path to local copy of sample dataset

mne.datasets.spm_face:

SPM face dataset

data_path([path, force_update, update_path, ...]) Get path to local copy of spm dataset

mne.datasets.brainstorm:

Brainstorm Dataset

bst_auditory.data_path([path, force_update, ...]) Get path to local copy of brainstorm (bst_auditory) dataset
bst_resting.data_path([path, force_update, ...]) Get path to local copy of brainstorm (bst_resting) dataset
bst_raw.data_path([path, force_update, ...]) Get path to local copy of brainstorm (bst_raw) dataset

mne.datasets.megsim:

MEGSIM dataset

data_path(url[, path, force_update, ...]) Get path to local copy of MEGSIM dataset URL
load_data([condition, data_format, ...]) Get path to local copy of MEGSIM dataset type

Visualization

mne.viz:

Visualization routines

Classes:

ClickableImage(imdata, \*\*kwargs) Display an image so you can click on it and store x/y positions.

Functions:

circular_layout(node_names, node_order[, ...]) Create layout arranging nodes on a circle.
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_connectivity_circle(con, node_names[, ...]) Visualize connectivity as a circular graph.
plot_cov(cov, info[, exclude, colorbar, ...]) Plot Covariance data
plot_dipole_amplitudes(dipoles[, colors, show]) Plot the amplitude traces of a set of dipoles
plot_dipole_locations(dipoles, trans, subject) 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_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 butteryfly 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 selected 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_compare_evokeds(evokeds[, picks, gfp, ...]) Plot evoked time courses for one or multiple channels and conditions
plot_ica_sources(ica, inst[, picks, ...]) Plot estimated latent sources given the unmixing matrix.
plot_ica_components(ica[, picks, ch_type, ...]) Project unmixing matrix on interpolated sensor topogrpahy.
plot_ica_properties(ica, inst[, picks, ...]) Display component properties: topography, epochs image, ERP/ERF, power spectrum and epoch variance.
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[, show]) Plot the sensor positions.
plot_montage(montage[, scale_factor, ...]) Plot a montage
plot_projs_topomap(projs[, layout, cmap, ...]) Plot topographic maps of SSP projections
plot_raw(raw[, events, duration, start, ...]) Plot raw data
plot_raw_psd(raw[, tmin, tmax, fmin, fmax, ...]) Plot the power spectral density across channels
plot_sensors(info[, kind, ch_type, title, ...]) Plot sensors positions.
plot_snr_estimate(evoked, inv[, show]) Plot a data SNR estimate
plot_source_estimates(stc[, subject, ...]) Plot SourceEstimates 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_trans(info[, trans, subject, ...]) Plot MEG/EEG head surface and helmet in 3D.
compare_fiff(fname_1, fname_2[, fname_out, ...]) Compare the contents of two fiff files using diff and show_fiff
add_background_image(fig, im[, set_ratios]) Add a background image to a plot.
show_fiff(fname[, indent, read_limit, ...]) Show FIFF information

Preprocessing

Projections:

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) Read projections from a FIF file.
write_proj(fname, projs) Write projections to a FIF file.
make_eeg_average_ref_proj(info[, activate, ...]) Create an EEG average reference SSP projection vector

Manipulate channels and set sensors locations for processing and plotting:

Classes:

Layout(box, pos, names, ids, kind) Sensor layouts
Montage(pos, ch_names, kind, selection) Montage for EEG cap
DigMontage(hsp, hpi, elp, point_names[, ...]) Montage for Digitized data

Functions:

fix_mag_coil_types(info) Fix magnetometer coil types
read_montage(kind[, ch_names, path, unit, ...]) Read a generic (built-in) montage from a file
read_dig_montage([hsp, hpi, elp, ...]) Read subject-specific digitization montage from a file
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
read_ch_connectivity(fname[, picks]) Parse FieldTrip neighbors .mat file
equalize_channels(candidates[, verbose]) Equalize channel picks for a collection of MNE-Python objects
rename_channels(info, mapping) Rename channels.
generate_2d_layout(xy[, w, h, pad, ...]) Generate a custom 2D layout from xy points.

mne.preprocessing:

Preprocessing with artifact detection, SSP, and ICA

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_ecg_events(raw[, event_id, ch_name, ...]) Find ECG peaks
find_eog_events(raw[, event_id, l_freq, ...]) Locate EOG artifacts
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
maxwell_filter(raw[, origin, int_order, ...]) Apply Maxwell filter to data using multipole moments
read_ica(fname) Restore ICA solution from fif file.
run_ica(raw, n_components[, ...]) Run ICA decomposition on raw data and identify artifact sources
corrmap(icas, template[, threshold, label, ...]) Find similar Independent Components across subjects by map similarity.

EEG referencing:

add_reference_channels(inst, ref_channels[, ...]) Add reference channels to data that consists of all zeros.
set_bipolar_reference(inst, anode, cathode) Rereference selected channels using a bipolar referencing scheme.
set_eeg_reference(inst[, ref_channels, copy]) Rereference EEG channels to new reference channel(s).

mne.filter:

IIR and FIR filtering functions

band_pass_filter(x, Fs, Fp1, Fp2[, ...]) Bandpass filter for the signal x.
construct_iir_filter(iir_params[, f_pass, ...]) Use IIR parameters to get filtering coefficients
estimate_ringing_samples(system[, max_try]) Estimate filter ringing
filter_data(data, sfreq, l_freq, h_freq[, ...]) Filter a subset of channels.
high_pass_filter(x, Fs, Fp[, filter_length, ...]) Highpass filter for the signal x.
low_pass_filter(x, Fs, Fp[, filter_length, ...]) Lowpass filter for the signal x.
notch_filter(x, Fs, freqs[, filter_length, ...]) Notch filter for the signal x.

Head position estimation:

filter_chpi(raw[, include_line, verbose]) 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
quat_to_rot(quat) Convert a set of quaternions to rotations
rot_to_quat(rot) Convert a set of rotations to quaternions

Events

concatenate_events(events, first_samps, ...) Concatenate event lists in a manner compatible with
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
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_events(filename[, include, exclude, ...]) Reads events from fif or text file
write_events(filename, event_list) Write events to file
concatenate_epochs(epochs_list) Concatenate a list of epochs into one epochs object
define_target_events(events, reference_id, ...) Define new events by co-occurrence of existing events
add_channels_epochs(epochs_list[, name, ...]) 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, events_list]) Concatenate raw instances as if they were continuous.
equalize_channels(candidates[, verbose]) Equalize channel picks for a collection of MNE-Python objects
grand_average(all_inst[, interpolate_bads, ...]) Make grand average of a list 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]) Restrict an info structure to a selection of channels
read_epochs(fname[, proj, add_eeg_ref, ...]) 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.

Covariance

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.
make_ad_hoc_cov(info[, 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.
regularize(cov, info[, mag, grad, eeg, ...]) Regularize noise covariance matrix.

MRI Processing

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

gui.coregistration([tabbed, split, ...]) 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([mne_root, fs_home, ...]) 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

mne:

Functions:

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
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[, force_fixed, ...]) Read a forward solution a.k.a.
read_trans(fname) 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, verbose]) Load a Freesurfer surface mesh in triangular format
sensitivity_map(fwd[, projs, ch_type, mode, ...]) Compute sensitivity map
setup_source_space(subject[, fname, ...]) Setup a bilater hemisphere surface-based source space with subsampling
setup_volume_source_space(subject[, fname, ...]) Setup a volume source space with grid spacing or discrete source space
write_bem_surfaces(fname, surfs) Write BEM surfaces to a fiff file
write_trans(fname, trans) Write a -trans.fif file
make_watershed_bem(subject[, subjects_dir, ...]) Create BEM surfaces using the watershed algorithm included with FreeSurfer
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
restrict_forward_to_label(fwd, labels) Restricts forward operator to labels
restrict_forward_to_stc(fwd, stc) Restricts forward operator to active sources in a source estimate

Inverse Solutions

mne.minimum_norm:

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

Classes:

InverseOperator InverseOperator class to represent info from inverse operator

Functions:

apply_inverse(evoked, inverse_operator[, ...]) Apply inverse operator to evoked 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 spectrum density (PSD)
compute_source_psd_epochs(epochs, ...[, ...]) Compute source power spectrum density (PSD) from Epochs using
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
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
point_spread_function(inverse_operator, ...) Compute point-spread functions (PSFs) for linear estimators
cross_talk_function(inverse_operator, ...[, ...]) Compute cross-talk functions (CTFs) for linear estimators

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

mne.beamformer:

Beamformers for source localization

lcmv(evoked, forward, noise_cov, data_cov[, ...]) Linearly Constrained Minimum Variance (LCMV) beamformer.
lcmv_epochs(epochs, forward, noise_cov, data_cov) Linearly Constrained Minimum Variance (LCMV) beamformer.
lcmv_raw(raw, forward, noise_cov, data_cov) Linearly Constrained Minimum Variance (LCMV) beamformer.
dics(evoked, forward, noise_csd, data_csd[, ...]) Dynamic Imaging of Coherent Sources (DICS).
dics_epochs(epochs, forward, noise_csd, data_csd) Dynamic Imaging of Coherent Sources (DICS).
dics_source_power(info, forward, noise_csds, ...) Dynamic Imaging of Coherent Sources (DICS).
rap_music(evoked, forward, noise_cov[, ...]) RAP-MUSIC source localization method.

mne:

Functions:

fit_dipole(evoked, cov, bem[, trans, ...]) Fit a dipole

mne.dipole:

Functions:

get_phantom_dipoles([kind]) Get standard phantom dipole locations and orientations

Source Space Data

compute_morph_matrix(subject_from, ...[, ...]) Get a matrix that morphs data from one subject to another
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
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
morph_data(subject_from, subject_to, stc_from) Morph a source estimate from one subject to another
morph_data_precomputed(subject_from, ...) Morph source estimate between subjects using a precomputed matrix
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 soure estimate object
save_stc_as_volume(fname, stc, src[, dest, ...]) Save a volume source estimate in a nifti 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) 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

Classes:

AverageTFR(info, data, times, freqs, nave[, ...]) Container for Time-Frequency data
EpochsTFR(info, data, times, freqs[, ...]) Container for Time-Frequency data on epochs

Functions that operate on mne-python objects:

csd_epochs(epochs[, mode, fmin, fmax, fsum, ...]) Estimate cross-spectral density from epochs
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.
fit_iir_model_raw(raw[, order, picks, tmin, ...]) Fits an AR model to raw data and creates the corresponding IIR filter
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, ...]) 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(X, sfreq[, mode, fmin, fmax, ...]) Estimate cross-spectral density from an array.
cwt_morlet(\*args, \*\*kwargs)

Warning

DEPRECATED: This function will be removed in mne 0.14; use mne.time_frequency.tfr_morlet() with average=False instead.

dpss_windows(N, half_nbw, Kmax[, low_bias, ...]) Returns the Discrete Prolate Spheroidal Sequences of orders [0,Kmax-1] for a given frequency-spacing multiple NW and sequence length N.
morlet(sfreq, freqs[, n_cycles, sigma, ...]) Compute Morlet wavelets for the given frequency range.
single_trial_power(\*args, \*\*kwargs)

Warning

DEPRECATED: This function will be removed in mne 0.14; use mne.time_frequency.tfr_morlet() with average=False instead.

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]) Frequencies of stft transformation

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:

Connectivity Analysis Tools

seed_target_indices(seeds, targets) Generate indices parameter for seed based connectivity analysis.
spectral_connectivity(data[, method, ...]) Compute frequency-domain and time-frequency domain connectivity measures
phase_slope_index(data[, indices, sfreq, ...]) Compute the Phase Slope Index (PSI) connectivity measure

Statistics

mne.stats:

Functions for statistical analysis

bonferroni_correction(pval[, alpha]) P-value correction with Bonferroni method
fdr_correction(pvals[, alpha, method]) P-value correction with False Discovery Rate (FDR)
permutation_cluster_test(X[, threshold, ...]) Cluster-level statistical permutation test
permutation_cluster_1samp_test(X[, ...]) Non-parametric cluster-level 1 sample 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 1 sample T-test for spatio-temporal data
ttest_1samp_no_p(X[, sigma, method]) t-test with variance adjustment and no p-value calculation
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 modelling
f_mway_rm(data, factor_levels[, effects, ...]) M-way repeated measures ANOVA for fully balanced designs
f_threshold_mway_rm(n_subjects, factor_levels) Compute f-value thesholds for a two-way ANOVA
summarize_clusters_stc(clu[, p_thresh, ...]) Assemble summary SourceEstimate from spatiotemporal cluster results

Functions to compute connectivity (adjacency) matrices for cluster-level statistics

spatial_dist_connectivity(src, dist[, verbose]) Compute connectivity from distances in a source space
spatial_src_connectivity(src[, dist, verbose]) Compute connectivity for a source space activation
spatial_tris_connectivity(tris[, ...]) Compute connectivity from triangles
spatial_inter_hemi_connectivity(src, dist[, ...]) Get vertices on each hemisphere that are close to the other hemisphere
spatio_temporal_src_connectivity(src, n_times) Compute connectivity for a source space activation over time
spatio_temporal_tris_connectivity(tris, n_times) Compute connectivity from triangles and time instants
spatio_temporal_dist_connectivity(src, ...) Compute connectivity from distances in a source space and time instants

Simulation

mne.simulation:

Data simulation code

simulate_evoked(fwd, stc, info, cov[, snr, ...]) Generate noisy evoked data
simulate_raw(raw, 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

Decoding

mne.decoding:

Classes:

CSP([n_components, reg, log, cov_est, ...]) M/EEG signal decomposition using the Common Spatial Patterns (CSP).
EpochsVectorizer(\*args, \*\*kwargs) EpochsVectorizer transforms epoch data to fit into a scikit-learn pipeline.
FilterEstimator(info, l_freq, h_freq[, ...]) Estimator to filter RtEpochs
GeneralizationAcrossTime([picks, cv, clf, ...]) Generalize across time and conditions
PSDEstimator([sfreq, fmin, fmax, bandwidth, ...]) Compute power spectrum density (PSD) using a multi-taper method
Scaler(info[, with_mean, with_std]) Standardizes data across channels
TimeDecoding([picks, cv, clf, times, ...]) Train and test a series of classifiers at each time point to obtain a score across time.

Realtime

mne.realtime:

Module for realtime MEG data using mne_rt_server

Classes:

RtEpochs(client, event_id, tmin, tmax[, ...]) Realtime Epochs
RtClient(host[, cmd_port, data_port, ...]) Realtime Client
MockRtClient(raw[, verbose]) Mock Realtime Client
FieldTripClient([info, host, port, ...]) Realtime FieldTrip client
StimServer([port, n_clients]) Stimulation Server
StimClient(host[, port, timeout, verbose]) Stimulation Client

MNE-Report

mne.report:

Generate html report from MNE database

Classes:

Report([info_fname, subjects_dir, subject, ...]) Object for rendering HTML