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_names
list
ofstr
|int
Channel names. If an int, a list of channel names will be created from
range(ch_names)
.- sfreq
float
Sample rate of the data.
- ch_types
list
ofstr
|str
Channel types, default is
'misc'
which is a non-data channel. Currently supported fields are ‘bio’, ‘chpi’, ‘csd’, ‘dbs’, ‘dipole’, ‘ecg’, ‘ecog’, ‘eeg’, ‘emg’, ‘eog’, ‘exci’, ‘eyegaze’, ‘fnirs_cw_amplitude’, ‘fnirs_fd_ac_amplitude’, ‘fnirs_fd_phase’, ‘fnirs_od’, ‘gof’, ‘gsr’, ‘hbo’, ‘hbr’, ‘ias’, ‘misc’, ‘pupil’, ‘ref_meg’, ‘resp’, ‘seeg’, ‘stim’, ‘syst’, ‘temperature’ (see also sensor types). 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 andmne.verbose()
for details. Should only be passed as a keyword argument.
- ch_names
- Returns:
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, resp, fnirs_fd_ac_amplitude, fnirs_cw_amplitude, fnirs_od
T: mag, chpi, ref_meg
T/m: grad
M: hbo, hbr
rad: fnirs_fd_phase
Am: dipole
S: gsr
C: temperature
V/m²: csd
GOF: gof
AU: misc, stim, eyegaze, pupil
Examples using mne.create_info
#
Importing data from fNIRS devices
EEG forward operator with a template MRI
Creating MNE-Python data structures from scratch
How to use data in neural ensemble (NEO) format
Identify EEG Electrodes Bridged by too much Gel
Plotting sensor layouts of EEG systems
Time-frequency on simulated data (Multitaper vs. Morlet vs. Stockwell vs. Hilbert)
Decoding in time-frequency space using Common Spatial Patterns (CSP)
Analysis of evoked response using ICA and PCA reduction techniques
Receptive Field Estimation and Prediction