mne_rsa.rsa_evokeds#
- mne_rsa.rsa_evokeds(evokeds, rdm_model, noise_cov=None, spatial_radius=None, temporal_radius=None, evoked_rdm_metric='correlation', evoked_rdm_params={}, rsa_metric='spearman', ignore_nan=False, y=None, n_folds=1, picks=None, tmin=None, tmax=None, n_jobs=1, verbose=False)[source]#
Perform RSA in a searchlight pattern on evokeds.
The output is an Evoked object where the “signal” at each sensor is the RSA, computed using all surrounding sensors.
- Parameters:
- evokedslist of
mne.Evoked
The evoked brain activity for each item. If you have more than one Evoked object per item (i.e. repetitions), you can use the
y
parameter to assign evokeds to items.- rdm_modelndarray, shape (n, n) | (n * (n - 1) // 2,) | list of ndarray
The model RDM, see
compute_rdm()
. For efficiency, you can give it in condensed form, meaning only the upper triangle of the matrix as a vector. Seescipy.spatial.distance.squareform()
. To perform RSA against multiple models at the same time, supply a list of model RDMs.Use
compute_rdm()
to compute RDMs.- noise_cov
mne.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_radiusfloat | 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 None.
- 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 None.
- evoked_rdm_metricstr
The metric to use to compute the RDM for the evokeds. This can be any metric supported by the scipy.distance.pdist function. See also the
evoked_rdm_params
parameter to specify and additional parameter for the distance function. Defaults to ‘correlation’.- evoked_rdm_paramsdict
Extra arguments for the distance metric used to compute the RDMs. Refer to
scipy.spatial.distance
for a list of all other metrics and their arguments. Defaults to an empty dictionary.- rsa_metricstr
The RSA metric to use to compare the RDMs. Valid options are:
‘spearman’ for Spearman’s correlation (the default)
‘pearson’ for Pearson’s correlation
‘kendall-tau-a’ for Kendall’s Tau (alpha variant)
‘partial’ for partial Pearson correlations
‘partial-spearman’ for partial Spearman correlations
‘regression’ for linear regression weights
Defaults to ‘spearman’.
- ignore_nanbool
Whether to treat NaN’s as missing values and ignore them when computing the distance metric. Defaults to
False
.New in version 0.8.
- yndarray of int, shape (n_items,) | None
For each Evoked, a number indicating the item to which it belongs. When
None
, each Evoked is assumed to belong to a different item. Defaults toNone
.- 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. SpecifyNone
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.- n_jobsint
The number of processes (=number of CPU cores) to use. Specify -1 to use all available cores. Defaults to 1.
- verbosebool
Whether to display a progress bar. In order for this to work, you need the tqdm python module installed. Defaults to False.
- evokedslist of
- Returns:
- rsaEvoked | list of Evoked
The correlation values for each searchlight patch. When spatial_radius is set to None, there will only be one virtual sensor. When temporal_radius is set to None, there will only be one time point. When multiple models have been supplied, a list will be returned containing the RSA results for each model.
See also