Construct a model RDM

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

Authors:

Marijn van Vliet <marijn.vanvliet@aalto.fi>

Stefan Appelhoff <stefan.appelhoff@mailbox.org>

# Import required packages
import mne
import mne_rsa

MNE-Python contains a built-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 /home/runner/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
943  disposal          4.45  ...                  0.625000         66.746396
168   courage          1.90  ...                  0.428571         69.928589
144   purpose          2.40  ...                  0.571429         70.606155
64      uncle          5.60  ...                  0.600000         61.525668
779     voice          3.90  ...                  0.400000         62.184962
733      term          3.95  ...                  0.750000         65.811350
448  conflict          3.75  ...                  0.750000         54.200787
98     common          2.80  ...                  0.666667         75.608379
545     truck          6.40  ...                  0.800000         56.602296
809     broil          4.70  ...                  0.600000         55.141335

[10 rows x 8 columns]

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

rdm = mne_rsa.compute_rdm(epochs.metadata.NumberOfLetters, metric="euclidean")

# Plot the RDM
fig = mne_rsa.plot_rdms(rdm, title="Word length RDM")
fig.set_size_inches(3, 3)  # Make figure a little bigger to show axis properly