Visualize Raw dataΒΆ

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'))

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

The visualization module (mne.viz) contains all the plotting functions that work in combination with MNE data structures. Usually the easiest way to use them is to call a method of the data container. All of the plotting method names start with plot. If you’re using Ipython console, you can just write raw.plot and ask the interpreter for suggestions with a tab key.

To visually inspect your raw data, you can use the python equivalent of mne_browse_raw.

raw.plot(block=True)
../_images/sphx_glr_plot_visualize_raw_001.png

The channels are color coded by channel type. Generally MEG channels are colored in different shades of blue, whereas EEG channels are black. The scrollbar on right side of the browser window also tells us that two of the channels are marked as bad. Bad channels are color coded gray. By clicking the lines or channel names on the left, you can mark or unmark a bad channel interactively. You can use +/- keys to adjust the scale (also = works for magnifying the data). Note that the initial scaling factors can be set with parameter scalings. If you don’t know the scaling factor for channels, you can automatically set them by passing scalings=’auto’. With pageup/pagedown and home/end keys you can adjust the amount of data viewed at once. To see all the interactive features, hit ? or click help in the lower left corner of the browser window.

The channels are sorted by channel type by default. You can use the order parameter of raw.plot to group the channels in a different way. order='selection' uses the same channel groups as MNE-C’s mne_browse_raw (see Selection). The selections are defined in mne-python/mne/data/mne_analyze.sel and by modifying the channels there, you can define your own selection groups. Notice that this also affects the selections returned by mne.read_selection(). By default the selections only work for Neuromag data, but order='position' tries to mimic this behavior for any data with sensor positions available. The channels are grouped by sensor positions to 8 evenly sized regions. Notice that for this to work effectively, all the data channels in the channel array must be present. The order parameter can also be passed as an array of ints (picks) to plot the channels in the given order.

raw.plot(order='selection')
  • ../_images/sphx_glr_plot_visualize_raw_002.png
  • ../_images/sphx_glr_plot_visualize_raw_003.png

We read the events from a file and passed it as a parameter when calling the method. The events are plotted as vertical lines so you can see how they align with the raw data.

We can check where the channels reside with plot_sensors. Notice that this method (along with many other MNE plotting functions) is callable using any MNE data container where the channel information is available.

raw.plot_sensors(kind='3d', ch_type='mag', ch_groups='position')
../_images/sphx_glr_plot_visualize_raw_004.png

We used ch_groups='position' to color code the different regions. It uses the same algorithm for dividing the regions as order='position' of raw.plot. You can also pass a list of picks to color any channel group with different colors.

Now let’s add some ssp projectors to the raw data. Here we read them from a file and plot them.

projs = mne.read_proj(op.join(data_path, 'sample_audvis_eog-proj.fif'))
raw.add_proj(projs)
raw.plot_projs_topomap()
../_images/sphx_glr_plot_visualize_raw_005.png

Out:

Read a total of 6 projection items:
        EOG-planar-998--0.200-0.200-PCA-01 (1 x 203)  idle
        EOG-planar-998--0.200-0.200-PCA-02 (1 x 203)  idle
        EOG-axial-998--0.200-0.200-PCA-01 (1 x 102)  idle
        EOG-axial-998--0.200-0.200-PCA-02 (1 x 102)  idle
        EOG-eeg-998--0.200-0.200-PCA-01 (1 x 59)  idle
        EOG-eeg-998--0.200-0.200-PCA-02 (1 x 59)  idle
6 projection items deactivated

The first three projectors that we see are the SSP vectors from empty room measurements to compensate for the noise. The fourth one is the average EEG reference. These are already applied to the data and can no longer be removed. The next six are the EOG projections that we added. Every data channel type has two projection vectors each. Let’s try the raw browser again.

raw.plot()
../_images/sphx_glr_plot_visualize_raw_006.png

Now click the proj button at the lower right corner of the browser window. A selection dialog should appear, where you can toggle the projectors on and off. Notice that the first four are already applied to the data and toggling them does not change the data. However the newly added projectors modify the data to get rid of the EOG artifacts. Note that toggling the projectors here doesn’t actually modify the data. This is purely for visually inspecting the effect. See mne.io.Raw.del_proj() to actually remove the projectors.

Raw container also lets us easily plot the power spectra over the raw data. See the API documentation for more info.

raw.plot_psd()
../_images/sphx_glr_plot_visualize_raw_007.png

Out:

Effective window size : 3.410 (s)
Effective window size : 3.410 (s)
Effective window size : 3.410 (s)

Plotting channel-wise power spectra is just as easy. The layout is inferred from the data by default when plotting topo plots. This works for most data, but it is also possible to define the layouts by hand. Here we select a layout with only magnetometer channels and plot it. Then we plot the channel wise spectra of first 30 seconds of the data.

layout = mne.channels.read_layout('Vectorview-mag')
layout.plot()
raw.plot_psd_topo(tmax=30., fmin=5., fmax=60., n_fft=1024, layout=layout)
  • ../_images/sphx_glr_plot_visualize_raw_008.png
  • ../_images/sphx_glr_plot_visualize_raw_009.png

Out:

Effective window size : 1.705 (s)

Total running time of the script: ( 0 minutes 17.105 seconds)

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