Note

Click here to download the full example code

The Info data structure

The Info data object is typically created when data is imported into MNE-Python and contains details such as:

  • date, subject information, and other recording details
  • the sampling rate
  • information about the data channels (name, type, position, etc.)
  • digitized points
  • sensor–head coordinate transformation matrices

and so forth. See the the API reference for a complete list of all data fields. Once created, this object is passed around throughout the data analysis pipeline.

import mne
import os.path as op

mne.Info behaves as a nested Python dictionary:

# Read the info object from an example recording
info = mne.io.read_info(
    op.join(mne.datasets.sample.data_path(), 'MEG', 'sample',
            'sample_audvis_raw.fif'), verbose=False)

List all the fields in the info object

print('Keys in info dictionary:\n', info.keys())

Out:

Keys in info dictionary:
 dict_keys(['file_id', 'events', 'hpi_results', 'hpi_meas', 'subject_info', 'hpi_subsystem', 'proc_history', 'meas_id', 'experimenter', 'description', 'proj_id', 'proj_name', 'meas_date', 'sfreq', 'highpass', 'lowpass', 'line_freq', 'gantry_angle', 'chs', 'dev_head_t', 'ctf_head_t', 'dev_ctf_t', 'dig', 'bads', 'ch_names', 'nchan', 'projs', 'comps', 'acq_pars', 'acq_stim', 'custom_ref_applied', 'xplotter_layout', 'kit_system_id'])

Obtain the sampling rate of the data

print(info['sfreq'], 'Hz')

Out:

600.614990234375 Hz

List all information about the first data channel

print(info['chs'][0])

Out:

{'scanno': 1, 'logno': 113, 'kind': 1, 'range': 0.00030517578125, 'cal': 3.1600000394149674e-09, 'coil_type': 3012, 'loc': array([-0.1066    ,  0.0464    , -0.0604    , -0.0127    ,  0.0057    ,
       -0.99990302, -0.186801  , -0.98240298, -0.0033    , -0.98232698,
        0.18674099,  0.013541  ]), 'unit': 201, 'unit_mul': 0, 'ch_name': 'MEG 0113', 'coord_frame': 1}

Obtaining subsets of channels

There are a number of convenience functions to obtain channel indices, given an mne.Info object.

Get channel indices by name

channel_indices = mne.pick_channels(info['ch_names'], ['MEG 0312', 'EEG 005'])

Get channel indices by regular expression

channel_indices = mne.pick_channels_regexp(info['ch_names'], 'MEG *')

Channel types

MNE supports different channel types:

  • eeg : For EEG channels with data stored in Volts (V)
  • meg (mag) : For MEG magnetometers channels stored in Tesla (T)
  • meg (grad) : For MEG gradiometers channels stored in Tesla/Meter (T/m)
  • ecg : For ECG channels stored in Volts (V)
  • seeg : For Stereotactic EEG channels in Volts (V).
  • ecog : For Electrocorticography (ECoG) channels in Volts (V).
  • fnirs (HBO) : Functional near-infrared spectroscopy oxyhemoglobin data.
  • fnirs (HBR) : Functional near-infrared spectroscopy deoxyhemoglobin data.
  • emg : For EMG channels stored in Volts (V)
  • bio : For biological channels (AU).
  • stim : For the stimulus (a.k.a. trigger) channels (AU)
  • resp : For the response-trigger channel (AU)
  • chpi : For HPI coil channels (T).
  • exci : Flux excitation channel used to be a stimulus channel.
  • ias : For Internal Active Shielding data (maybe on Triux only).
  • syst : System status channel information (on Triux systems only).

Get channel indices by type

channel_indices = mne.pick_types(info, meg=True)  # MEG only
channel_indices = mne.pick_types(info, eeg=True)  # EEG only

MEG gradiometers and EEG channels

channel_indices = mne.pick_types(info, meg='grad', eeg=True)

Get a dictionary of channel indices, grouped by channel type

channel_indices_by_type = mne.io.pick.channel_indices_by_type(info)
print('The first three magnetometers:', channel_indices_by_type['mag'][:3])

Out:

The first three magnetometers: [2, 5, 8]

Obtaining information about channels

# Channel type of a specific channel
channel_type = mne.io.pick.channel_type(info, 75)
print('Channel #75 is of type:', channel_type)

Out:

Channel #75 is of type: grad

Channel types of a collection of channels

meg_channels = mne.pick_types(info, meg=True)[:10]
channel_types = [mne.io.pick.channel_type(info, ch) for ch in meg_channels]
print('First 10 MEG channels are of type:\n', channel_types)

Out:

First 10 MEG channels are of type:
 ['grad', 'grad', 'mag', 'grad', 'grad', 'mag', 'grad', 'grad', 'mag', 'grad']

Dropping channels from an info structure

It is possible to limit the info structure to only include a subset of channels with the mne.pick_info() function:

# Only keep EEG channels
eeg_indices = mne.pick_types(info, meg=False, eeg=True)
reduced_info = mne.pick_info(info, eeg_indices)

print(reduced_info)

Out:

<Info | 24 non-empty fields
    acq_pars : str | 13886 items
    bads : list | 0 items
    ch_names : list | EEG 001, EEG 002, EEG 003, EEG 004, EEG 005, EEG 006, ...
    chs : list | 59 items (EEG: 59)
    comps : list | 0 items
    custom_ref_applied : bool | False
    description : str | 49 items
    dev_head_t : Transform | 3 items
    dig : list | 146 items
    events : list | 1 items
    experimenter : str | 3 items
    file_id : dict | 4 items
    highpass : float | 0.10000000149011612 Hz
    hpi_meas : list | 1 items
    hpi_results : list | 1 items
    lowpass : float | 172.17630004882812 Hz
    meas_date : ndarray | 2002-12-03 19:01:10 GMT
    meas_id : dict | 4 items
    nchan : int | 59
    proc_history : list | 0 items
    proj_id : ndarray | 1 items
    proj_name : str | 4 items
    projs : list | PCA-v1: off, PCA-v2: off, PCA-v3: off
    sfreq : float | 600.614990234375 Hz
    acq_stim : NoneType
    ctf_head_t : NoneType
    dev_ctf_t : NoneType
    gantry_angle : NoneType
    hpi_subsystem : NoneType
    kit_system_id : NoneType
    line_freq : NoneType
    subject_info : NoneType
    xplotter_layout : NoneType
>

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