Construct a model DSM¶

This example shows how to create DSMs from arbitrary data. A common use case for this is to construct a “model” DSM to RSA against the brain data. In this example, we will create a DSM based on the length of the words shown during an EEG experiment.

# Import required packages
import mne
import mne_rsa

MNE-Python contains a build-in data loader for the kiloword dataset, which is used here as an example dataset. Since we only need the words shown during the experiment, which are in the metadata, we can pass preload=False to prevent MNE-Python from loading the EEG data, which is a nice speed gain.

data_path = mne.datasets.kiloword.data_path(verbose=True)
epochs = mne.read_epochs(data_path / 'kword_metadata-epo.fif', preload=False)

# Show the metadata of 10 random epochs
print(epochs.metadata.sample(10))

Reading C:\Users\wmvan\mne_data\MNE-kiloword-data\kword_metadata-epo.fif ...
Isotrak not found
    Found the data of interest:
        t =    -100.00 ...     920.00 ms
        0 CTF compensation matrices available
Adding metadata with 8 columns
960 matching events found
No baseline correction applied
0 projection items activated
         WORD  Concreteness  ...  ConsonantVowelProportion  VisualComplexity
362    hazard          3.45  ...                  0.666667         68.943826
690  interior          4.20  ...                  0.500000         53.360692
332     poesy          5.00  ...                  0.400000         74.259702
552     stern          3.65  ...                  0.800000         60.215695
692  doctrine          2.95  ...                  0.625000         61.243776
67      crowd          5.55  ...                  0.800000         71.864295
671   fluency          2.85  ...                  0.571429         60.572977
375    mentor          4.45  ...                  0.666667         66.052349
25       golf          5.70  ...                  0.750000         63.026557
617    oracle          3.60  ...                  0.500000         61.460712

[10 rows x 8 columns]

Now we are ready to create the “model” DSM, which will encode the difference in length between the words shown during the experiment.

dsm = mne_rsa.compute_dsm(epochs.metadata.NumberOfLetters, metric='euclidean')

# Plot the DSM
fig = mne_rsa.plot_dsms(dsm, title='Word length DSM')
fig.set_size_inches(3, 3)  # Make figure a little bigger to show axis properly

Total running time of the script: ( 0 minutes 0.515 seconds)

Gallery generated by Sphinx-Gallery