import os.path as op
import mne
data_path = op.join(mne.datasets.sample.data_path(), 'MEG', 'sample')
raw = mne.io.read_raw_fif(op.join(data_path, 'sample_audvis_raw.fif'),
add_eeg_ref=False)
raw.set_eeg_reference() # set EEG average reference
events = mne.read_events(op.join(data_path, 'sample_audvis_raw-eve.fif'))
picks = mne.pick_types(raw.info, meg='grad')
epochs = mne.Epochs(raw, events, [1, 2], picks=picks, add_eeg_ref=False)
Out:
Opening raw data file /home/ubuntu/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
Adding average EEG reference projection.
1 projection items deactivated
145 matching events found
Applying baseline correction (mode: mean)
4 projection items activated
This tutorial focuses on visualization of epoched data. All of the functions introduced here are basically high level matplotlib functions with built in intelligence to work with epoched data. All the methods return a handle to matplotlib figure instance.
All plotting functions start with plot. Let’s start with the most
obvious. mne.Epochs.plot() offers an interactive browser that allows
rejection by hand when called in combination with a keyword block=True.
This blocks the execution of the script until the browser window is closed.
epochs.plot(block=True)
Out:
Loading data for 145 events and 421 original time points ...
0 bad epochs dropped
Loading data for 20 events and 421 original time points ...
The numbers at the top refer to the event id of the epoch. We only have events with id numbers of 1 and 2 since we included only those when constructing the epochs.
Since we did no artifact correction or rejection, there are epochs contaminated with blinks and saccades. For instance, epoch number 9 (see numbering at the bottom) seems to be contaminated by a blink (scroll to the bottom to view the EOG channel). This epoch can be marked for rejection by clicking on top of the browser window. The epoch should turn red when you click it. This means that it will be dropped as the browser window is closed. You should check out help at the lower left corner of the window for more information about the interactive features.
To plot individual channels as an image, where you see all the epochs at one
glance, you can use function mne.Epochs.plot_image(). It shows the
amplitude of the signal over all the epochs plus an average of the
activation. We explicitly set interactive colorbar on (it is also on by
default for plotting functions with a colorbar except the topo plots). In
interactive mode you can scale and change the colormap with mouse scroll and
up/down arrow keys. You can also drag the colorbar with left/right mouse
button. Hitting space bar resets the scale.
epochs.plot_image(97, cmap='interactive')
# You also have functions for plotting channelwise information arranged into a
# shape of the channel array. The image plotting uses automatic scaling by
# default, but noisy channels and different channel types can cause the scaling
# to be a bit off. Here we define the limits by hand.
epochs.plot_topo_image(vmin=-200, vmax=200, title='ERF images')
Out:
Loading data for 145 events and 421 original time points ...
Loading data for 145 events and 421 original time points ...
Total running time of the script: ( 0 minutes 4.923 seconds)