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 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  WordFrequency  OrthographicDistance  NumberOfLetters  BigramFrequency  ConsonantVowelProportion  VisualComplexity
91      filth         4.050       2.004321                  1.85              5.0       433.400000                  0.800000         49.631810
425   embargo         4.950       1.568202                  3.05              7.0       203.000000                  0.571429         77.366752
110    crisis         3.650       3.022428                  2.00              6.0       575.666667                  0.666667         54.164593
90      felon         4.650       1.079181                  1.85              5.0       436.600000                  0.600000         60.050717
238  warranty         4.750       0.845098                  2.80              8.0       686.375000                  0.625000         64.544162
588    bottom         4.000       3.166726                  2.35              6.0       430.500000                  0.666667         68.942478
548     stone         6.350       2.868056                  1.45              5.0       539.800000                  0.600000         65.933265
168   courage         1.900       2.773786                  2.30              7.0       841.142857                  0.428571         69.928589
909   blunder         4.300       1.491362                  1.80              7.0       683.571429                  0.714286         65.055058
237  modality         3.125       0.301030                  2.60              8.0       463.000000                  0.500000         65.056680

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