mne.create_info#

mne.create_info(ch_names, sfreq, ch_types='misc', verbose=None)[source]#

Create a basic Info instance suitable for use with create_raw.

Parameters
ch_nameslist of str | int

Channel names. If an int, a list of channel names will be created from range(ch_names).

sfreqfloat

Sample rate of the data.

ch_typeslist of str | str

Channel types, default is 'misc' which is not a data channel. Currently supported fields are ‘ecg’, ‘bio’, ‘stim’, ‘eog’, ‘misc’, ‘seeg’, ‘dbs’, ‘ecog’, ‘mag’, ‘eeg’, ‘ref_meg’, ‘grad’, ‘emg’, ‘hbr’ or ‘hbo’. If str, then all channels are assumed to be of the same type.

verbosebool | str | int | None

Control verbosity of the logging output. If None, use the default verbosity level. See the logging documentation and mne.verbose() for details. Should only be passed as a keyword argument.

Returns
infomne.Info

The mne.Info object with information about the sensors and methods of measurement.

Notes

The info dictionary will be sparsely populated to enable functionality within the rest of the package. Advanced functionality such as source localization can only be obtained through substantial, proper modifications of the info structure (not recommended).

Note that the MEG device-to-head transform info['dev_head_t'] will be initialized to the identity transform.

Proper units of measure: * V: eeg, eog, seeg, dbs, emg, ecg, bio, ecog * T: mag * T/m: grad * M: hbo, hbr * Am: dipole * AU: misc

Examples using mne.create_info#

Importing data from MEG devices

Importing data from MEG devices

Importing data from MEG devices
Importing data from fNIRS devices

Importing data from fNIRS devices

Importing data from fNIRS devices
EEG forward operator with a template MRI

EEG forward operator with a template MRI

EEG forward operator with a template MRI
Creating MNE-Python data structures from scratch

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How to use data in neural ensemble (NEO) format

How to use data in neural ensemble (NEO) format

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Reading XDF EEG data

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Reading XDF EEG data
Identify EEG Electrodes Bridged by too much Gel

Identify EEG Electrodes Bridged by too much Gel

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How to plot topomaps the way EEGLAB does

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How to plot topomaps the way EEGLAB does
Plotting sensor layouts of EEG systems

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Time-frequency on simulated data (Multitaper vs. Morlet vs. Stockwell)

Time-frequency on simulated data (Multitaper vs. Morlet vs. Stockwell)

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Decoding in time-frequency space using Common Spatial Patterns (CSP)

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Analysis of evoked response using ICA and PCA reduction techniques

Analysis of evoked response using ICA and PCA reduction techniques

Analysis of evoked response using ICA and PCA reduction techniques
XDAWN Decoding From EEG data

XDAWN Decoding From EEG data

XDAWN Decoding From EEG data
Receptive Field Estimation and Prediction

Receptive Field Estimation and Prediction

Receptive Field Estimation and Prediction