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%matplotlib inline
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from __future__ import print_function
import mne
import numpy as np
Info <mne.Info> objectsfor full documentation on the `Info` object, see `tut_info_objects`. See also `sphx_glr_auto_examples_io_plot_objects_from_arrays.py`.
Normally, :class:mne.Info objects are created by the various
data import functions <ch_convert>.
However, if you wish to create one from scratch, you can use the
:func:mne.create_info function to initialize the minimally required
fields. Further fields can be assigned later as one would with a regular
dictionary.
The following creates the absolute minimum info structure:
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# Create some dummy metadata
n_channels = 32
sampling_rate = 200
info = mne.create_info(32, sampling_rate)
print(info)
You can also supply more extensive metadata:
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# Names for each channel
channel_names = ['MEG1', 'MEG2', 'Cz', 'Pz', 'EOG']
# The type (mag, grad, eeg, eog, misc, ...) of each channel
channel_types = ['grad', 'grad', 'eeg', 'eeg', 'eog']
# The sampling rate of the recording
sfreq = 1000 # in Hertz
# The EEG channels use the standard naming strategy.
# By supplying the 'montage' parameter, approximate locations
# will be added for them
montage = 'standard_1005'
# Initialize required fields
info = mne.create_info(channel_names, sfreq, channel_types, montage)
# Add some more information
info['description'] = 'My custom dataset'
info['bads'] = ['Pz'] # Names of bad channels
print(info)
When assigning new values to the fields of an :class:`mne.Info` object, it is important that the fields are consistent: - The length of the channel information field `chs` must be `nchan`. - The length of the `ch_names` field must be `nchan`. - The `ch_names` field should be consistent with the `name` field of the channel information contained in `chs`.
Raw <mne.io.Raw> objectsTo create a :class:mne.io.Raw object from scratch, you can use the
:class:mne.io.RawArray class, which implements raw data that is backed by a
numpy array. Its constructor simply takes the data matrix and
:class:mne.Info object:
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# Generate some random data
data = np.random.randn(5, 1000)
# Initialize an info structure
info = mne.create_info(
ch_names=['MEG1', 'MEG2', 'EEG1', 'EEG2', 'EOG'],
ch_types=['grad', 'grad', 'eeg', 'eeg', 'eog'],
sfreq=100
)
custom_raw = mne.io.RawArray(data, info)
print(custom_raw)
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# Generate some random data: 10 epochs, 5 channels, 2 seconds per epoch
sfreq = 100
data = np.random.randn(10, 5, sfreq * 2)
# Initialize an info structure
info = mne.create_info(
ch_names=['MEG1', 'MEG2', 'EEG1', 'EEG2', 'EOG'],
ch_types=['grad', 'grad', 'eeg', 'eeg', 'eog'],
sfreq=sfreq
)
It is necessary to supply an "events" array in order to create an Epochs
object. This is of shape(n_events, 3) where the first column is the index
of the event, the second column is the length of the event, and the third
column is the event type.
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# Create an event matrix: 10 events with a duration of 1 sample, alternating
# event codes
events = np.array([
[0, 1, 1],
[1, 1, 2],
[2, 1, 1],
[3, 1, 2],
[4, 1, 1],
[5, 1, 2],
[6, 1, 1],
[7, 1, 2],
[8, 1, 1],
[9, 1, 2],
])
More information about the event codes: subject was either smiling or frowning
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event_id = dict(smiling=1, frowning=2)
Finally, we must specify the beginning of an epoch (the end will be inferred from the sampling frequency and n_samples)
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# Trials were cut from -0.1 to 1.0 seconds
tmin = -0.1
Now we can create the :class:mne.EpochsArray object
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custom_epochs = mne.EpochsArray(data, info, events, tmin, event_id)
print(custom_epochs)
# We can treat the epochs object as we would any other
_ = custom_epochs['smiling'].average().plot()
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# The averaged data
data_evoked = data.mean(0)
# The number of epochs that were averaged
nave = data.shape[0]
# A comment to describe to evoked (usually the condition name)
comment = "Smiley faces"
# Create the Evoked object
evoked_array = mne.EvokedArray(data_evoked, info, tmin,
comment=comment, nave=nave)
print(evoked_array)
_ = evoked_array.plot()