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.

# 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 /l/vanvlm1/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  WordFrequency  ...  BigramFrequency  ConsonantVowelProportion  VisualComplexity
748      ruin      3.600000       2.004321  ...       344.750000                  0.500000         52.606471
629   special      2.550000       3.526598  ...       371.714286                  0.571429         64.664890
506      bore      3.550000       2.068186  ...       786.250000                  0.500000         68.910505
459  prospect      2.800000       2.732394  ...       554.000000                  0.750000         67.446434
804     craze      3.200000       1.662758  ...       474.400000                  0.600000         64.583106
183   sarcasm      3.150000       1.724276  ...       548.428571                  0.714286         69.948661
417   transit      4.315789       2.152288  ...       721.571429                  0.714286         55.318946
536     moral      2.350000       3.014521  ...       727.800000                  0.600000         64.202329
892   worship      2.900000       2.336460  ...       418.714286                  0.714286         68.605603
65      smoke      5.550000       2.933993  ...       208.400000                  0.600000         76.196174

[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