Preprocessing

Projections:

Projection(*, data[, desc, kind, active, ...])

Dictionary-like object holding a projection vector.

compute_proj_epochs(epochs[, n_grad, n_mag, ...])	Compute SSP (signal-space projection) vectors on epoched data.
compute_proj_evoked(evoked[, n_grad, n_mag, ...])	Compute SSP (signal-space projection) vectors on evoked data.
compute_proj_raw(raw[, start, stop, ...])	Compute SSP (signal-space projection) vectors on continuous data.
read_proj(fname[, verbose])	Read projections from a FIF file.
write_proj(fname, projs, *[, overwrite, verbose])	Write projections to a FIF file.

mne.channels:

Module dedicated to manipulation of channels.

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

Layout(box, pos, names, ids, kind)	Sensor layouts.
DigMontage(*[, dig, ch_names])	Montage for digitized electrode and headshape position data.
compute_native_head_t(montage, *[, ...])	Compute the native-to-head transformation for a montage.
fix_mag_coil_types(info[, use_cal])	Fix magnetometer coil types.
read_polhemus_fastscan(fname[, unit, ...])	Read Polhemus FastSCAN digitizer data from a .txt file.
get_builtin_montages(*[, descriptions])	Get a list of all standard montages shipping with MNE-Python.
make_dig_montage([ch_pos, nasion, lpa, rpa, ...])	Make montage from arrays.
read_dig_polhemus_isotrak(fname[, ch_names, ...])	Read Polhemus digitizer data from a file.
read_dig_captrak(fname)	Read electrode locations from CapTrak Brain Products system.
read_dig_dat(fname)	Read electrode positions from a *.dat file.
read_dig_egi(fname)	Read electrode locations from EGI system.
read_dig_fif(fname)	Read digitized points from a .fif file.
read_dig_hpts(fname[, unit])	Read historical .hpts MNE-C files.
read_dig_localite(fname[, nasion, lpa, rpa])	Read Localite .csv file.
make_standard_montage(kind[, head_size])	Read a generic (built-in) standard montage that ships with MNE-Python.
read_custom_montage(fname[, head_size, ...])	Read a montage from a file.
compute_dev_head_t(montage)	Compute device to head transform from a DigMontage.
read_layout([fname, scale])	Read layout from a file.
find_layout(info[, ch_type, exclude])	Choose a layout based on the channels in the info 'chs' field.
make_eeg_layout(info[, radius, width, ...])	Create .lout file from EEG electrode digitization.
make_grid_layout(info[, picks, n_col])	Generate .lout file for custom data, i.e., ICA sources.
find_ch_adjacency(info, ch_type)	Find the adjacency matrix for the given channels.
get_builtin_ch_adjacencies(*[, descriptions])	Get a list of all FieldTrip neighbor definitions shipping with MNE.
read_ch_adjacency(fname[, picks])	Read a channel adjacency ("neighbors") file that ships with MNE.
equalize_channels(instances[, copy, verbose])	Equalize channel picks and ordering across multiple MNE-Python objects.
unify_bad_channels(insts)	Unify bad channels across a list of instances.
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[, ...])	Map hemisphere names to corresponding EEG channel names or indices.
combine_channels(inst, groups[, method, ...])	Combine channels based on specified channel grouping.

mne.preprocessing:

Preprocessing with artifact detection, SSP, and ICA.

ICA([n_components, noise_cov, random_state, ...])	Data decomposition using Independent Component Analysis (ICA).
Xdawn([n_components, signal_cov, ...])	Implementation of the Xdawn Algorithm.
EOGRegression([picks, exclude, ...])	Remove EOG artifact signals from other channels by regression.
annotate_amplitude(raw[, peak, flat, ...])	Annotate raw data based on peak-to-peak amplitude.
annotate_break(raw[, events, ...])	Create Annotations for breaks in an ongoing recording.
annotate_movement(raw, pos[, ...])	Detect segments with movement.
annotate_muscle_zscore(raw[, threshold, ...])	Create annotations for segments that likely contain muscle artifacts.
annotate_nan(raw, *[, verbose])	Detect segments with NaN and return a new Annotations instance.
compute_average_dev_head_t(raw, pos, *[, ...])	Get new device to head transform based on good segments.
compute_current_source_density(inst[, ...])	Get the current source density (CSD) transformation.
compute_bridged_electrodes(inst[, ...])	Compute bridged EEG electrodes using the intrinsic Hjorth algorithm.
compute_fine_calibration(raw[, n_imbalance, ...])	Compute fine calibration from empty-room data.
compute_maxwell_basis(info[, origin, ...])	Compute the SSS basis for a given measurement info structure.
compute_proj_ecg(raw[, raw_event, tmin, ...])	Compute SSP (signal-space projection) vectors for ECG artifacts.
compute_proj_eog(raw[, raw_event, tmin, ...])	Compute SSP (signal-space projection) vectors for EOG artifacts.
compute_proj_hfc(info[, order, picks, ...])	Generate projectors to perform homogeneous/harmonic correction to data.
cortical_signal_suppression(evoked[, picks, ...])	Apply cortical signal suppression (CSS) to evoked data.
create_ecg_epochs(raw[, ch_name, event_id, ...])	Conveniently generate epochs around ECG artifact events.
create_eog_epochs(raw[, ch_name, event_id, ...])	Conveniently generate epochs around EOG artifact events.
find_bad_channels_lof(raw[, n_neighbors, ...])	Find bad channels using Local Outlier Factor (LOF) algorithm.
find_bad_channels_maxwell(raw[, limit, ...])	Find bad channels using Maxwell filtering.
find_ecg_events(raw[, event_id, ch_name, ...])	Find ECG events by localizing the R wave 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.
interpolate_bridged_electrodes(inst, bridged_idx)	Interpolate bridged electrode pairs.
equalize_bads(insts[, interp_thresh, copy])	Interpolate or mark bads consistently for a list of instances.
maxwell_filter(raw[, origin, int_order, ...])	Maxwell filter data using multipole moments.
maxwell_filter_prepare_emptyroom(raw_er, *, raw)	Prepare an empty-room recording for Maxwell filtering.
oversampled_temporal_projection(raw[, ...])	Denoise MEG channels using leave-one-out temporal projection.
peak_finder(x0[, thresh, extrema, verbose])	Noise-tolerant fast peak-finding algorithm.
read_ica(fname[, verbose])	Restore ICA solution from fif file.
read_eog_regression(fname)	Read an EOG regression model from an HDF5 file.
realign_raw(raw, other, t_raw, t_other[, ...])	Realign two simultaneous recordings.
regress_artifact(inst[, picks, exclude, ...])	Remove artifacts using regression based on reference channels.
corrmap(icas, template[, threshold, label, ...])	Find similar Independent Components across subjects by map similarity.
read_ica_eeglab(fname, *[, montage_units, ...])	Load ICA information saved in an EEGLAB .set file.
read_fine_calibration(fname)	Read fine calibration information from a .dat file.
write_fine_calibration(fname, calibration)	Write fine calibration information to a .dat file.

mne.preprocessing.nirs:

NIRS specific preprocessing functions.

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

mne.preprocessing.ieeg:

Intracranial EEG specific preprocessing functions.

project_sensors_onto_brain(info, trans, subject)	Project sensors onto the brain surface.
make_montage_volume(montage, base_image[, ...])	Make a volume from intracranial electrode contact locations.
warp_montage(montage, moving, static, ...[, ...])	Warp a montage to a template with image volumes using SDR.

mne.preprocessing.eyetracking:

Eye tracking specific preprocessing functions.

Calibration(*, onset, model, eye, avg_error, ...)	Eye-tracking calibration info.
read_eyelink_calibration(fname[, ...])	Return info on calibrations collected in an eyelink file.
set_channel_types_eyetrack(inst, mapping)	Define sensor type for eyetrack channels.
convert_units(inst, calibration[, to])	Convert Eyegaze data from pixels to radians of visual angle or vice versa.
get_screen_visual_angle(calibration)	Calculate the radians of visual angle that the participant screen subtends.
interpolate_blinks(raw[, buffer, match, ...])	Interpolate eyetracking signals during blinks.

EEG referencing:

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

mne.filter:

IIR and FIR filtering and resampling functions.

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

mne.chpi

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

compute_head_pos can be used to:

1. Drop coils whose GOF are below gof_limit. If fewer than 3 coils remain, abandon fitting for the chunk.

2. Fit dev_head_t quaternion (using _fit_chpi_quat_subset), iteratively dropping coils (as long as 3 remain) to find the best GOF (using _fit_chpi_quat).

3. If fewer than 3 coils meet the dist_limit criteria following projection of the fitted device coil locations into the head frame, abandon fitting for the chunk.

The function filter_chpi uses the same linear model to filter cHPI and (optionally) line frequencies from the data.

compute_chpi_amplitudes(raw[, t_step_min, ...])	Compute time-varying cHPI amplitudes.
compute_chpi_snr(raw[, t_step_min, ...])	Compute time-varying estimates of cHPI SNR.
compute_chpi_locs(info, chpi_amplitudes[, ...])	Compute locations of each cHPI coils over time.
compute_head_pos(info, chpi_locs[, ...])	Compute time-varying head positions.
extract_chpi_locs_ctf(raw[, verbose])	Extract cHPI locations from CTF data.
extract_chpi_locs_kit(raw[, stim_channel, ...])	Extract cHPI locations from KIT data.
filter_chpi(raw[, include_line, t_step, ...])	Remove cHPI and line noise from data.
get_active_chpi(raw, *[, on_missing, verbose])	Determine how many HPI coils were active for a time point.
get_chpi_info(info[, on_missing, verbose])	Retrieve cHPI information from the data.
head_pos_to_trans_rot_t(quats)	Convert Maxfilter-formatted head position quaternions.
read_head_pos(fname)	Read MaxFilter-formatted head position parameters.
write_head_pos(fname, pos)	Write MaxFilter-formatted head position parameters.

mne.transforms

Helpers for various transformations.

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