mne.pick_types

mne.pick_types(info, meg=None, eeg=False, stim=False, eog=False, ecg=False, emg=False, ref_meg='auto', misc=False, resp=False, chpi=False, exci=False, ias=False, syst=False, seeg=False, dipole=False, gof=False, bio=False, ecog=False, fnirs=False, csd=False, include=(), exclude='bads', selection=None)

Pick channels by type and names.

Parameters

infodict

The measurement info.

megbool | str

If True include MEG channels. If string it can be ‘mag’, ‘grad’, ‘planar1’ or ‘planar2’ to select only magnetometers, all gradiometers, or a specific type of gradiometer.

eegbool

If True include EEG channels.

stimbool

If True include stimulus channels.

eogbool

If True include EOG channels.

ecgbool

If True include ECG channels.

emgbool

If True include EMG channels.

ref_megbool | str

If True include CTF / 4D reference channels. If ‘auto’, reference channels are included if compensations are present and meg is not False. Can also be the string options for the meg parameter.

miscbool

If True include miscellaneous analog channels.

respbool

If True include response-trigger channel. For some MEG systems this is separate from the stim channel.

chpibool

If True include continuous HPI coil channels.

excibool

Flux excitation channel used to be a stimulus channel.

iasbool

Internal Active Shielding data (maybe on Triux only).

systbool

System status channel information (on Triux systems only).

seegbool

Stereotactic EEG channels.

dipolebool

Dipole time course channels.

gofbool

Dipole goodness of fit channels.

biobool

Bio channels.

ecogbool

Electrocorticography channels.

fnirsbool | str

Functional near-infrared spectroscopy channels. If True include all fNIRS channels. If False (default) include none. If string it can be ‘hbo’ (to include channels measuring oxyhemoglobin) or ‘hbr’ (to include channels measuring deoxyhemoglobin).

csdbool

Current source density channels.

includelist of str

List of additional channels to include. If empty do not include any.

excludelist of str | str

List of channels to exclude. If ‘bads’ (default), exclude channels in info['bads'].

selectionlist of str

Restrict sensor channels (MEG, EEG) to this list of channel names.

Returns

selarray of int

Indices of good channels.

Examples using mne.pick_types

Creating MNE objects from data arrays

Reading and writing raw files

Reading epochs from a raw FIF file

Generate simulated evoked data

Define target events based on time lag, plot evoked response

Show EOG artifact timing

Find ECG artifacts

Find EOG artifacts

Find MEG reference channel artifacts

Compare the different ICA algorithms in MNE

Re-referencing the EEG signal

XDAWN Denoising

Plotting topographic arrowmaps of evoked data

Plot custom topographies for MEG sensors

Compute a cross-spectral density (CSD) matrix

Compute the power spectral density of raw data

Compute Power Spectral Density of inverse solution from single epochs

Compute power and phase lock in label of the source space

Compute source power spectral density (PSD) in a label

Compute induced power in the source space with dSPM

Temporal whitening with AR model

Permutation F-test on sensor data with 1D cluster level

FDR correction on T-test on sensor data

Regression on continuous data (rER[P/F])

Permutation T-test on sensor data

Representational Similarity Analysis

Motor imagery decoding from EEG data using the Common Spatial Pattern (CSP)

Decoding source space data

Decoding sensor space data with generalization across time and conditions

Analysis of evoked response using ICA and PCA reduction techniques

XDAWN Decoding From EEG data

Compute effect-matched-spatial filtering (EMS)

Compute seed-based time-frequency connectivity in sensor space

Compute coherence in source space using a MNE inverse solution

Compute full spectrum source space connectivity between labels

Compute source space connectivity and visualize it using a circular graph

Compute Phase Slope Index (PSI) in source space for a visual stimulus

Compute all-to-all connectivity in sensor space

Display sensitivity maps for EEG and MEG sensors

Use source space morphing

Compute MNE-dSPM inverse solution on single epochs

Compute evoked ERS source power using DICS, LCMV beamformer, and dSPM

Compute iterative reweighted TF-MxNE with multiscale time-frequency dictionary

Compute cross-talk functions for LCMV beamformers

Time-frequency beamforming using DICS

Brainstorm raw (median nerve) dataset

Optically pumped magnetometer (OPM) data

From raw data to dSPM on SPM Faces dataset

The Info data structure

Working with sensor locations

The Raw data structure: continuous data

Overview of artifact detection

Interpolating bad channels

Rejecting bad data spans

Filtering and resampling data

Repairing artifacts with regression

Repairing artifacts with SSP

Preprocessing functional near-infrared spectroscopy (fNIRS) data

Visualizing Evoked data

Frequency and time-frequency sensors analysis

Non-parametric 1 sample cluster statistic on single trial power

Non-parametric between conditions cluster statistic on single trial power

Spatiotemporal permutation F-test on full sensor data

Permutation t-test on source data with spatio-temporal clustering

Repeated measures ANOVA on source data with spatio-temporal clustering

Mass-univariate twoway repeated measures ANOVA on single trial power

DICS for power mapping

Modifying data in-place