Preprocessing#

Projections:

`Projection`(*, data[, desc, kind, active, ...])	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`(kind[, path, scale])	Read layout from a file.
`find_layout`(info[, ch_type, exclude])	Choose a layout based on the channels in the info 'chs' field.
`make_eeg_layout`(info[, radius, width, ...])	Create .lout file from EEG electrode digitization.
`make_grid_layout`(info[, picks, n_col])	Generate .lout file for custom data, i.e., ICA sources.
`find_ch_adjacency`(info, ch_type)	Find the adjacency matrix for the given channels.
`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.
`rename_channels`(info, mapping[, ...])	Rename channels.
`generate_2d_layout`(xy[, w, h, pad, ...])	Generate a custom 2D layout from xy points.
`make_1020_channel_selections`(info[, midline])	Return dict mapping from ROI names to lists of picks for 10/20 setups.
`combine_channels`(inst, groups[, method, ...])	Combine channels based on specified channel grouping.

mne.preprocessing:

Preprocessing with artifact detection, SSP, and ICA.

`ICA`([n_components, noise_cov, random_state, ...])	Data decomposition using Independent Component Analysis (ICA).
`Xdawn`([n_components, signal_cov, ...])	Implementation of the Xdawn Algorithm.
`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.
`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_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.
`realign_raw`(raw, other, t_raw, t_other[, ...])	Realign two simultaneous recordings.
`regress_artifact`(inst[, picks, ...])	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, *[, verbose])	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.

EEG referencing:

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

mne.filter:

IIR and FIR filtering and resampling functions.

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

mne.chpi

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

`compute_chpi_amplitudes`(raw[, t_step_min, ...])	Compute time-varying cHPI amplitudes.
`compute_chpi_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_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.