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This example demonstrates how to perform cross-validation when computing dissimilarity matrices (DSMs). When the data has repeated measurements of the same stimulus type, cross-validation can be used to provide much more robust distance estimates between stimulus types. Repeated measurements can for example be actual repetitions of the same stimulus within the same recording, or recordings on multiple volunteers with the same stimuli.
The dataset will be the kiloword dataset : approximately 1,000 words were presented to 75 participants in a go/no-go lexical decision task while event-related potentials (ERPs) were recorded.
This dataset as provided does not have repeated measurements of the same stimuli. To illustrate cross-validation, we will treat words with the same number of letters as being repeated measurements of the same stimulus type.
# Import required packages import mne import mne_rsa
MNE-Python contains a build-in data loader for the kiloword dataset. We use it here to read it as 960 epochs. Each epoch represents the brain response to a single word, averaged across all the participants. For this example, we speed up the computation, at a cost of temporal precision, by downsampling the data from the original 250 Hz. to 100 Hz.
data_path = mne.datasets.kiloword.data_path(verbose=True) epochs = mne.read_epochs(data_path / 'kword_metadata-epo.fif') epochs = epochs.resample(100)
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
epochs object contains a
.metadata field that contains
information about the 960 words that were used in the experiment. Let’s have
a look at the metadata for the 10 random words:
The kiloword dataset as provided does not have repeated measurements of the same stimuli. To illustrate cross-validation, we will treat words with the same number of letters as being repeated measurements of the same stimulus type.
To denote which epochs are repetitions of the same stimulus, we create a list
y that contains integer labels for each epoch. Repetitions of the same
stimulus have the same label in the
y list. This scheme is lifted from
the machine learning literature (and the Scikit-Learn API). In this example,
we use the number of letters in the stimulus words as the labels for the
Many high-level functions in the MNE-RSA module can take the
y list as a
parameter to enable cross-validation. Notably the functions for performing
RSA and computing DSMs. In this example, we will restrict the analysis to
computing DSMs using a spatio-temporal searchlight on the sensor-level data.
dsms = mne_rsa.dsm_epochs( epochs, # The EEG data y=y, # Set labels to enable cross validation n_folds=5, # Number of folds to use during cross validation dist_metric='sqeuclidean', # Distance metric to compute the DSMs spatial_radius=0.45, # Spatial radius of the searchlight patch in meters. temporal_radius=0.05, # Temporal radius of the searchlight path in seconds. tmin=0.15, tmax=0.25) # To save time, only analyze this time interval
Plotting the cross-validated DSMs
Creating spatio-temporal searchlight patches <Figure size 640x480 with 29 Axes>
For performance reasons, the low-level functions of MNE-RSA do not take a
y list for cross-validation. Instead, they require the data to be already
split into folds. The
create_folds() function can create these
X = epochs.get_data() y = epochs.metadata.NumberOfLetters.astype(int) folds = mne_rsa.create_folds(X, y, n_folds=5) dsm = mne_rsa.compute_dsm_cv(folds, metric='euclidean') mne_rsa.plot_dsms(dsm)
<Figure size 200x200 with 2 Axes>
Total running time of the script: ( 0 minutes 3.220 seconds)