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%matplotlib inline

Compute the power spectral density of epochs

This script shows how to compute the power spectral density (PSD) of measurements on epochs. It also shows how to plot its spatial distribution.



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# Authors: Denis Engemann <denis.engemann@gmail.com>
#
# License: BSD (3-clause)

import mne
from mne import io
from mne.datasets import sample

print(__doc__)

Set parameters



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data_path = sample.data_path()
raw_fname = data_path + '/MEG/sample/sample_audvis_raw.fif'
event_fname = data_path + '/MEG/sample/sample_audvis_raw-eve.fif'

# Setup for reading the raw data
raw = io.Raw(raw_fname)
events = mne.read_events(event_fname)

tmin, tmax, event_id = -1., 1., 1
raw.info['bads'] += ['MEG 2443']  # bads

epochs = mne.Epochs(raw, events, event_id, tmin, tmax,
                    proj=True, baseline=(None, 0), preload=True,
                    reject=dict(grad=4000e-13, eog=150e-6))

# Let's first check out all channel types by averaging across epochs.
epochs.plot_psd(fmin=2, fmax=200)

# picks MEG gradiometers
picks = mne.pick_types(raw.info, meg='grad', eeg=False, eog=False,
                       stim=False, exclude='bads')

# Now let's take a look at the spatial distributions of the psd.
epochs.plot_psd_topomap(ch_type='grad', normalize=True)