- mne.pick_types(info, meg=False, 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, dbs=False, temperature=False, gsr=False, eyetrack=False, include=(), exclude='bads', selection=None)[source]#
Pick channels by type and names.
mne.Infoobject with information about the sensors and methods of measurement.
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.
If True include EEG channels.
If True include stimulus channels.
If True include EOG channels.
If True include ECG channels.
If True include EMG channels.
If True include CTF / 4D reference channels. If ‘auto’, reference channels are included if compensations are present and
megis not False. Can also be the string options for the
If True include miscellaneous analog channels.
Trueinclude respiratory channels.
If True include continuous HPI coil channels.
Flux excitation channel used to be a stimulus channel.
Internal Active Shielding data (maybe on Triux only).
System status channel information (on Triux systems only).
Stereotactic EEG channels.
Dipole time course channels.
Dipole goodness of fit channels.
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).
Deep brain stimulation channels.
Galvanic skin response channels.
Eyetracking channels. If True include all eyetracking channels. If False (default) include none. If string it can be ‘eyegaze’ (to include eye position channels) or ‘pupil’ (to include pupil-size channels).
List of additional channels to include. If empty do not include any.
List of channels to exclude. If ‘bads’ (default), exclude channels in
Restrict sensor channels (MEG, EEG, etc.) to this list of channel names.
The Raw data structure: continuous data
Overview of artifact detection
Rejecting bad data spans and breaks
Repairing artifacts with regression
Preprocessing functional near-infrared spectroscopy (fNIRS) data
Preprocessing optically pumped magnetometer (OPM) MEG data
Frequency and time-frequency sensor analysis
Frequency-tagging: Basic analysis of an SSVEP/vSSR dataset
Non-parametric 1 sample cluster statistic on single trial power
Non-parametric between conditions cluster statistic on single trial power
Mass-univariate twoway repeated measures ANOVA 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
Generate simulated evoked data
Cortical Signal Suppression (CSS) for removal of cortical signals
Define target events based on time lag, plot evoked response
Find MEG reference channel artifacts
Plotting topographic arrowmaps of evoked data
Plot custom topographies for MEG sensors
Compute a cross-spectral density (CSD) matrix
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
Motor imagery decoding from EEG data using the Common Spatial Pattern (CSP)
Representational Similarity Analysis
Decoding sensor space data with generalization across time and conditions
Analysis of evoked response using ICA and PCA reduction techniques
Compute effect-matched-spatial filtering (EMS)
Display sensitivity maps for EEG and MEG sensors
Compute MNE-dSPM inverse solution on single epochs
Compute evoked ERS source power using DICS, LCMV beamformer, and dSPM
Compute cross-talk functions for LCMV beamformers
Brainstorm raw (median nerve) dataset
Optically pumped magnetometer (OPM) data
From raw data to dSPM on SPM Faces dataset