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 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  ...  BigramFrequency  ConsonantVowelProportion  VisualComplexity
458  monument          5.60       2.278754  ...       611.000000                  0.625000         72.044648
897   mistake          3.40       2.928396  ...       674.000000                  0.571429         68.146539
674  research          4.05       3.334856  ...       636.375000                  0.625000         65.317730
592    notion          2.30       2.815578  ...       459.166667                  0.500000         59.229152
123    camera          6.20       2.635484  ...       941.166667                  0.500000         71.909491
807     plaza          5.70       1.792392  ...       326.600000                  0.600000         67.873974
241      hope          1.75       3.009451  ...       419.000000                  0.500000         76.603193
216  appetite          3.85       2.536558  ...       548.500000                  0.500000         67.873615
912  function          3.30       2.998695  ...       491.875000                  0.625000         57.562123
638   contact          4.00       3.077368  ...      1042.428571                  0.714286         61.307924

[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