%matplotlib inline

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.

mne.Info behaves as a nested Python dictionary:

import mne
import os.path as op

# 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())

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'])

List all information about the first data channel

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

{'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}

Obtain the sampling rate of the data

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

600.614990234375 Hz

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, meg=False, 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])

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)

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)

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)

<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 : tuple | 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
>

Exercises

1) Can you plot the EEG sensors in the info?

import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D

fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')

# your code here to compute x, y, z

# ax.scatter(x, y, z)

png

In fact, you can use a convenient method raw.plot_sensors to just do this.

# % matplotlib notebook
data_path = op.join(mne.datasets.sample.data_path(), 'MEG',
                    'sample', 'sample_audvis_raw.fif')
raw = mne.io.read_raw_fif(data_path, preload=True).pick_types(eeg=True, meg=False)
raw.plot_sensors(kind='3d');

Opening raw data file /local_mount/space/meghnn/1/users/mjas/mne_data/MNE-sample-data/MEG/sample/sample_audvis_raw.fif...
    Read a total of 3 projection items:
        PCA-v1 (1 x 102)  idle
        PCA-v2 (1 x 102)  idle
        PCA-v3 (1 x 102)  idle
    Range : 25800 ... 192599 =     42.956 ...   320.670 secs
Ready.
Current compensation grade : 0
Reading 0 ... 166799  =      0.000 ...   277.714 secs...

png

The Info data structure