Generate DSMs in a searchlight pattern on epochs.
The brain activity during the epochs. The event codes are used to distinguish between items.
When specified, the data will by normalized using the noise covariance. This is recommended in all cases, but a hard requirement when the data contains sensors of different types. Defaults to None.
The spatial radius of the searchlight patch in meters. All sensors within this radius will belong to the searchlight patch. Set to None to only perform the searchlight over time, flattening across sensors. Defaults to 0.04.
The temporal radius of the searchlight patch in seconds. Set to None to only perform the searchlight over sensors, flattening across time. Defaults to 0.1.
The metric to use to compute the DSM for the epochs. This can be any
metric supported by the scipy.distance.pdist function. See also the
epochs_dsm_params parameter to specify and additional parameter for
the distance function. Defaults to ‘correlation’.
Extra arguments for the distance metric used to compute the DSMs.
scipy.spatial.distance for a list of all other metrics
and their arguments. Defaults to an empty dictionary.
For each Epoch, a number indicating the item to which it belongs.
None, the event codes are used to differentiate between items.
Number of cross-validation folds to use when computing the distance
metric. Folds are created based on the
y parameter. Specify
None to use the maximum number of folds possible, given the data.
Alternatively, you can pass a Scikit-Learn cross validator object (e.g.
sklearn.model_selection.KFold) to assert fine-grained control over
how folds are created.
Defaults to 1 (no cross-validation).
Channels to include. Slices and lists of integers will be interpreted
as channel indices. In lists, channel type strings (e.g.,
'eeg']) will pick channels of those types, channel name strings
['MEG0111', 'MEG2623'] will pick the given channels. Can
also be the string values “all” to pick all channels, or “data” to pick
None (default) will pick all MEG and EEG channels,
excluding those maked as “bad”.
When set, searchlight patches will only be generated from subsequent
time points starting from this time point. This value is given in
seconds. Defaults to
None, in which case patches are generated
starting from the first time point.
When set, searchlight patches will only be generated up to and
including this time point. This value is given in seconds. Defaults to
None, in which case patches are generated up to and including the
last time point.
When this is set to
True, the drop log will be used to inject NaN
values in the DSMs at the locations where a bad epoch was dropped. This
is useful to ensure the dimensions of the DSM are the same,
irregardless of any bad epochs that were dropped. Make sure to use
ignore_nan=True when using DSMs with NaNs in them during subsequent
RSA computations. Defaults to
New in version 0.8.
A DSM for each searchlight patch.