mne.pick_types#
- 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, include=(), exclude='bads', selection=None)[source]#
Pick channels by type and names.
- Parameters
- info
mne.Info
The
mne.Info
object with information about the sensors and methods of measurement.- 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 themeg
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.
- dbsbool
Deep brain stimulation channels.
- include
list
ofstr
List of additional channels to include. If empty do not include any.
- exclude
list
ofstr
|str
List of channels to exclude. If ‘bads’ (default), exclude channels in
info['bads']
.- selection
list
ofstr
Restrict sensor channels (MEG, EEG) to this list of channel names.
- info
- Returns
Examples using mne.pick_types
#
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
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
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
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