mne_rsa.dsm_epochs¶

mne_rsa.dsm_epochs(epochs, noise_cov=None, spatial_radius=0.04, temporal_radius=0.1, dist_metric='correlation', dist_params={}, y=None, n_folds=1, picks=None, tmin=None, tmax=None, dropped_as_nan=False)[source]¶

Generate DSMs in a searchlight pattern on epochs.

Parameters:

epochsinstance of mne.Epochs

The brain activity during the epochs. The event codes are used to distinguish between items.

noise_covmne.Covariance | None

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.

spatial_radiusfloats | 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.

temporal_radiusfloat | None

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.

dist_metricstr

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’.

dist_paramsdict

Extra arguments for the distance metric used to compute the DSMs. Refer to scipy.spatial.distance for a list of all other metrics and their arguments. Defaults to an empty dictionary.

yndarray of int, shape (n_items,) | None

For each Epoch, a number indicating the item to which it belongs. When None, the event codes are used to differentiate between items. Defaults to None.

n_foldsint | sklearn.model_selection.BaseCrollValidator | None

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).

picksstr | list | slice | None

Channels to include. Slices and lists of integers will be interpreted as channel indices. In lists, channel type strings (e.g., ['meg', 'eeg']) will pick channels of those types, channel name strings (e.g., ['MEG0111', 'MEG2623'] will pick the given channels. Can also be the string values “all” to pick all channels, or “data” to pick data channels. None (default) will pick all MEG and EEG channels, excluding those maked as “bad”.

tminfloat | None

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.

tmaxfloat | None

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.

dropped_as_nanbool

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 False.

New in version 0.8.

Yields:

dsmndarray, shape (n_items, n_items)

A DSM for each searchlight patch.