mne.channels.make_standard_montage

mne.channels.make_standard_montage(kind, head_size='auto')

Read a generic (built-in) standard montage that ships with MNE-Python.

Parameters:

kindstr

The name of the montage to use.

Note

You can retrieve the names of all built-in montages via mne.channels.get_builtin_montages().

head_sizefloat | None | str

The head size (radius, in meters) to use for spherical montages. Can be None to not scale the read sizes. 'auto' (default) will use 95mm for all montages except the 'standard*', 'mgh*', and 'artinis*', which are already in fsaverage’s MRI coordinates (same as MNI).

Returns:

montageinstance of DigMontage

The montage.

See also

get_builtin_montages

make_dig_montage

read_custom_montage

Notes

Individualized (digitized) electrode positions should be read in using read_dig_captrak(), read_dig_dat(), read_dig_egi(), read_dig_fif(), read_dig_polhemus_isotrak(), read_dig_hpts(), or manually made with make_dig_montage().

New in v0.19.0.

Examples using mne.channels.make_standard_montage

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

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

Receptive Field Estimation and Prediction

Receptive Field Estimation and Prediction

Identify EEG Electrodes Bridged by too much Gel

Identify EEG Electrodes Bridged by too much Gel

Removing muscle ICA components

Removing muscle ICA components

Plotting sensor layouts of EEG systems

Plotting sensor layouts of EEG systems

EEG forward operator with a template MRI

EEG forward operator with a template MRI

Working with sensor locations

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Importing data from fNIRS devices

Importing data from fNIRS devices

Frequency-tagging: Basic analysis of an SSVEP/vSSR dataset

Frequency-tagging: Basic analysis of an SSVEP/vSSR dataset