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%matplotlib inline

Plotting whitened data

This tutorial demonstrates how to plot whitened evoked data.

Data are whitened for many processes, including dipole fitting, source localization and some decoding algorithms. Viewing whitened data thus gives a different perspective on the data that these algorithms operate on.

Let's start by loading some data and computing a signal (spatial) covariance that we'll consider to be noise.



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import mne
from mne.datasets import sample

Raw data with whitening

Note

In the :meth:`mne.io.Raw.plot` with ``noise_cov`` supplied, you can press they "w" key to turn whitening on and off.



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data_path = sample.data_path()
raw_fname = data_path + '/MEG/sample/sample_audvis_filt-0-40_raw.fif'
raw = mne.io.read_raw_fif(raw_fname, preload=True)

events = mne.find_events(raw, stim_channel='STI 014')
event_id = {'auditory/left': 1, 'auditory/right': 2, 'visual/left': 3,
            'visual/right': 4, 'smiley': 5, 'button': 32}
reject = dict(grad=4000e-13, mag=4e-12, eog=150e-6)
epochs = mne.Epochs(raw, events, event_id=event_id, reject=reject)

# baseline noise cov, not a lot of samples
noise_cov = mne.compute_covariance(epochs, tmax=0., method='shrunk', rank=None,
                                   verbose='error')

# butterfly mode shows the differences most clearly
raw.plot(events=events, butterfly=True)
raw.plot(noise_cov=noise_cov, events=events, butterfly=True)

Epochs with whitening



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epochs.plot()
epochs.plot(noise_cov=noise_cov)

Evoked data with whitening



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evoked = epochs.average()
evoked.plot(time_unit='s')
evoked.plot(noise_cov=noise_cov, time_unit='s')

Evoked data with scaled whitening

The :meth:mne.Evoked.plot_white function takes an additional step of scaling the whitened plots to show how well the assumption of Gaussian noise is satisfied by the data:



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evoked.plot_white(noise_cov=noise_cov, time_unit='s')

Topographic plot with whitening



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evoked.comment = 'All trials'
evoked.plot_topo(title='Evoked data')
evoked.plot_topo(noise_cov=noise_cov, title='Whitened evoked data')