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.
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 |
Measurement information. |
IO module for reading raw data.
anonymize_info(info) |
Anonymize measurement information in place. |
find_edf_events(raw) |
Get original EDF events as read from the header. |
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, 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, GDF 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. |
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. |
Base class:
BaseRaw(info[, preload, first_samps, …]) |
Base class for Raw data. |
KIT module for reading raw data.
read_mrk(fname) |
Marker Point Extraction in MEG space directly from sqd. |
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[, …]) |
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 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_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) |
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]) |
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) |
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. |
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. |
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, verbose]) |
Raw object from numpy array. |
create_info(ch_names, sfreq[, ch_types, …]) |
Create a basic Info instance suitable for use with create_raw. |
Functions for fetching remote datasets.
See datasets 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. |
fetch_hcp_mmp_parcellation([subjects_dir, …]) |
Fetch the HCP-MMP parcellation. |
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. |
megsim.data_path(url[, path, force_update, …]) |
Get path to local copy of MEGSIM dataset URL. |
megsim.load_data([condition, data_format, …]) |
Get path to local copy of MEGSIM dataset type. |
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. |
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. |
Visualization routines.
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. |
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. |
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_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, 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 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, gfp, …]) |
Plot evoked time courses for one or more conditions and/or channels. |
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 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[, 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, verbose]) |
Plot a data SNR estimate. |
plot_source_estimates(stc[, subject, …]) |
Plot SourceEstimates with PySurfer. |
plot_volume_source_estimates(stc, src[, …]) |
Plot Nutmeg style volumetric source estimates using nilearn. |
plot_vector_source_estimates(stc[, subject, …]) |
Plot VectorSourceEstimates 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. |
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) |
Read projections from a FIF file. |
write_proj(fname, projs) |
Write projections to a FIF file. |
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. |
Montage(pos, ch_names, kind, selection[, …]) |
Montage for standard EEG electrode locations. |
DigMontage([hsp, hpi, elp, point_names, …]) |
Montage for digitized electrode and headshape position data. |
fix_mag_coil_types(info) |
Fix magnetometer coil types. |
read_montage(kind[, ch_names, path, unit, …]) |
Read a generic (built-in) montage. |
get_builtin_montages() |
Get a list of all builtin montages. |
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. |
find_ch_connectivity(info, ch_type) |
Find the connectivity matrix for the given channels. |
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. |
make_1020_channel_selections(info[, midline]) |
Return dict mapping from ROI names to lists of picks for 10/20 setups. |
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. |
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. |
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, …]) |
Apply Maxwell filter to data using multipole moments. |
oversampled_temporal_projection(raw[, …]) |
Denoise MEG channels using leave-one-out temporal projection. |
read_ica(fname[, verbose]) |
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) |
Re-reference selected channels using a bipolar referencing scheme. |
set_eeg_reference(inst[, ref_channels, …]) |
Specify which reference to use for EEG data. |
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. |
Functions for fitting head positions with (c)HPI coils.
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. |
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. |
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. |
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. |
IO with fif files containing events.
define_target_events(events, reference_id, …) |
Define new events by co-occurrence of existing events. |
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. |
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(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, 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. |
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.regularize(cov, info[, mag, grad, eeg, …]) |
Regularize noise covariance matrix. |
cov.compute_whitener(noise_cov, info[, …]) |
Compute whitening matrix. |
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. |
Step by step instructions for using gui.coregistration():
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 |
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. |
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]) |
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. |
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. |
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, verbose]) |
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. |
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_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. |
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. |
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. |
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. |
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. |
make_dics(info, forward, csd[, reg, label, …]) |
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, …) |
5D time-frequency beamforming based on LCMV. |
Dipole(times, pos, amplitude, ori, gof[, …]) |
Dipole class for sequential dipole fits. |
DipoleFixed(info, data, times, nave, …[, …]) |
Dipole class for fixed-position dipole fits. |
fit_dipole(evoked, cov, bem[, trans, …]) |
Fit a dipole. |
Single-dipole functions and classes.
get_phantom_dipoles([kind]) |
Get standard phantom dipole locations and orientations. |
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. |
SourceEstimate(data[, vertices, tmin, …]) |
Container for surface source estimates. |
VectorSourceEstimate(data[, vertices, tmin, …]) |
Container for vector surface source estimates. |
VolSourceEstimate(data[, vertices, tmin, …]) |
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. |
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. |
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 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 Morlet wavelets. |
csd_morlet(epochs, frequencies[, tmin, …]) |
Estimate cross-spectral density from epochs using Morlet wavelets. |
pick_channels_csd(csd[, include, exclude]) |
Pick channels from covariance 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, …]) |
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 Morlet wavelets. |
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]) |
Frequencies of stft transformation. |
psd_array_multitaper(x, sfreq[, fmin, fmax, …]) |
Compute power spectrum 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 transform using Morlet wavelets. |
tfr_array_multitaper(epoch_data, sfreq, freqs) |
Compute time-frequency transforms using wavelets and multitaper windows. |
tfr_array_stockwell(data, sfreq[, fmin, …]) |
Compute power and intertrial coherence using Stockwell (S) transform. |
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. |
Spectral and effective connectivity measures.
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. |
phase_slope_index(data[, indices, sfreq, …]) |
Compute the Phase Slope Index (PSI) connectivity measure. |
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. |
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:
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. |
Compute connectivity matrices for cluster-level statistics:
channels.find_ch_connectivity(info, ch_type) |
Find the connectivity matrix for the given channels. |
channels.read_ch_connectivity(fname[, picks]) |
Parse FieldTrip neighbors .mat file. |
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. |
Data simulation code.
simulate_evoked(fwd, stc, info, cov[, nave, …]) |
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 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 spectrum 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 MEG data processing with servers and clients.
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([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. |
get_config_path([home_dir]) |
Get path to standard mne-python config file. |
get_config([key, default, raise_error, home_dir]) |
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. |
sys_info([fid, show_paths]) |
Print the system information for debugging. |
verbose(function, *args, **kwargs) |
Verbose decorator to allow functions to override log-level. |
get_cuda_memory([kind]) |
Get the amount of free memory for CUDA operations. |
init_cuda([ignore_config, verbose]) |
Initialize CUDA functionality. |