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.
io.Raw |
alias of RawFIF |
io.RawFIF (fnames[, allow_maxshield, ...]) |
Raw data |
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 |
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 |
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 |
io.Info |
Information about the recording. |
io.Projection |
Projection vector |
preprocessing.ICA ([n_components, ...]) |
M/EEG signal decomposition using Independent Component Analysis (ICA) |
decoding.CSP ([n_components, reg, log, cov_est]) |
M/EEG signal decomposition using the Common Spatial Patterns (CSP). |
decoding.Scaler (info[, with_mean, with_std]) |
Standardizes data across channels |
decoding.ConcatenateChannels |
|
decoding.FilterEstimator (info, l_freq, h_freq) |
Estimator to filter RtEpochs |
decoding.PSDEstimator ([sfreq, fmin, fmax, ...]) |
Compute power spectrum density (PSD) using a multi-taper method |
decoding.GeneralizationAcrossTime ([picks, ...]) |
Generalize across time and conditions |
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.StimServer ([ip, port, n_clients]) |
Stimulation Server |
realtime.StimClient (host[, port, timeout, ...]) |
Stimulation Client |
report.Report ([info_fname, subjects_dir, ...]) |
Object for rendering HTML |
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 mne-python preference in config |
init_cuda ([ignore_config]) |
Initialize CUDA functionality |
mne.io
:
Classes:
Raw |
alias of RawFIF |
Functions:
read_raw_bti (pdf_fname[, config_fname, ...]) |
Raw object from 4D Neuroimaging MagnesWH3600 data |
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, ...]) |
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 (fnames[, allow_maxshield, ...]) |
Reader function for Raw FIF data |
mne.io.kit
:
read_mrk (fname) |
Marker Point Extraction in MEG space directly from sqd |
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, mask]) |
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, 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[, verbose]) |
Load a Freesurfer surface mesh in triangular format |
read_trans (fname) |
Read a -trans.fif 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 |
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[, 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 |
MNE sample dataset
data_path ([path, force_update, update_path, ...]) |
Get path to local copy of sample dataset |
SPM face dataset
data_path ([path, force_update, update_path, ...]) |
Get path to local copy of spm dataset |
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 |
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 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_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 |
plot_evoked_image (evoked[, picks, exclude, ...]) |
Plot evoked data as images |
plot_evoked_topomap (evoked[, times, ...]) |
Plot topographic maps of specific time points of evoked data |
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_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_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_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_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 |
|
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 |
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 |
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 montage from a file |
read_dig_montage ([hsp, hpi, elp, ...]) |
Read digitization data from a file and generate 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 |
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. |
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 |
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). |
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 |
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. |
concatenate_events (events, first_samps, ...) |
Concatenate event lists in a manner compatible with |
find_events (raw[, stim_channel, verbose, ...]) |
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, mask]) |
Reads events from fif or text file |
write_events (filename, event_list) |
Write events to file |
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, pos[, orig_sfreq, ...]) |
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 |
concatenate_epochs (epochs_list) |
Concatenate a list of epochs into one epochs object |
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 |
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 |
get_chpi_positions (raw[, t_step, ...]) |
Extract head positions |
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 |
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, verbose]) |
Read channel selection from file |
rename_channels (info, mapping) |
Rename channels. |
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. |
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() |
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 |
do_forward_solution (subject, meas[, fname, ...]) |
Calculate a forward solution for a subject using MNE-C routines |
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_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[, 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 source space with subsampling |
setup_volume_source_space (subject[, fname, ...]) |
Setup a volume source space with grid spacing or discrete source space |
write_bem_surface |
|
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 |
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 |
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 |
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 |
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 analysis tools
Classes:
AverageTFR (info, data, times, freqs, nave[, ...]) |
Container for Time-Frequency data |
Functions that operate on mne-python objects:
compute_epochs_csd (epochs[, mode, fmin, ...]) |
Estimate cross-spectral density from epochs |
compute_epochs_psd (epochs[, picks, fmin, ...]) |
Compute power spectral density with average periodograms. |
compute_raw_psd (raw[, tmin, tmax, picks, ...]) |
Compute power spectral density with average periodograms. |
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 wavelets |
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:
cwt_morlet (X, sfreq, freqs[, use_fft, ...]) |
Compute time freq decomposition with Morlet wavelets |
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 Wavelets for the given frequency range |
multitaper_psd (x[, sfreq, fmin, fmax, ...]) |
Compute power spectrum density (PSD) using a multi-taper method |
single_trial_power (data, sfreq, frequencies) |
Compute time-frequency power on single epochs |
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 |
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 Wavelets for the given frequency range |
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 |
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 |
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 |
Data simulation code
simulate_evoked (fwd, stc, info, cov[, snr, ...]) |
Generate noisy evoked data |
simulate_raw (raw, stc, trans, src, bem[, ...]) |
Simulate raw data with head movements |
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 |
Classes:
Scaler (info[, with_mean, with_std]) |
Standardizes data across channels |
ConcatenateChannels |
|
PSDEstimator ([sfreq, fmin, fmax, bandwidth, ...]) |
Compute power spectrum density (PSD) using a multi-taper method |
FilterEstimator (info, l_freq, h_freq[, ...]) |
Estimator to filter RtEpochs |
CSP ([n_components, reg, log, cov_est]) |
M/EEG signal decomposition using the Common Spatial Patterns (CSP). |
GeneralizationAcrossTime ([picks, cv, clf, ...]) |
Generalize across time and conditions |
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 ([ip, port, n_clients]) |
Stimulation Server |
StimClient (host[, port, timeout, verbose]) |
Stimulation Client |
Generate html report from MNE database
Classes:
Report ([info_fname, subjects_dir, subject, ...]) |
Object for rendering HTML |