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%matplotlib inline
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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(n_channels, 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. The correct units for the data are:
The :class:mne.io.RawArray 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)
Epochs <mne.Epochs> objectsTo create an :class:mne.Epochs object from scratch, you can use the
:class:mne.EpochsArray class, which uses a numpy array directly without
wrapping a raw object. The array must be of shape(n_epochs, n_chans,
n_times). The proper units of measure are listed above.
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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 sample
number (time) of the event, the second column indicates the value from which
the transition is made from (only used when the new value is bigger than the
old one), and the third column is the new event value.
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# Create an event matrix: 10 events with alternating event codes
events = np.array([
[0, 0, 1],
[1, 0, 2],
[2, 0, 1],
[3, 0, 2],
[4, 0, 1],
[5, 0, 2],
[6, 0, 1],
[7, 0, 2],
[8, 0, 1],
[9, 0, 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()
Evoked <mne.Evoked> ObjectsIf you already have data that is collapsed across trials, you may also
directly create an evoked array. Its constructor accepts an array of
shape(n_chans, n_times) in addition to some bookkeeping parameters.
The proper units of measure for the data are listed above.
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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()