mne.decoding.compute_ems¶

mne.decoding.compute_ems(epochs, conditions=None, picks=None, n_jobs=1, verbose=None, cv=None)[source]¶

Compute event-matched spatial filter on epochs.

This version of EMS [R39] operates on the entire time course. No time window needs to be specified. The result is a spatial filter at each time point and a corresponding time course. Intuitively, the result gives the similarity between the filter at each time point and the data vector (sensors) at that time point.

Parameters:

epochs : instance of mne.Epochs The epochs. conditions : list of str | None, defaults to None If a list of strings, strings must match the epochs.event_id’s key as well as the number of conditions supported by the objective_function. If None keys in epochs.event_id are used. picks : array-like of int | None, defaults to None Channels to be included. If None only good data channels are used. n_jobs : int, defaults to 1 Number of jobs to run in parallel. verbose : bool, str, int, or None, defaults to self.verbose If not None, override default verbose level (see mne.verbose() and Logging documentation for more). cv : cross-validation object | str | None, defaults to LeaveOneOut The cross-validation scheme.

Returns:

surrogate_trials : ndarray, shape (n_trials // 2, n_times) The trial surrogates. mean_spatial_filter : ndarray, shape (n_channels, n_times) The set of spatial filters. conditions : ndarray, shape (n_classes,) The conditions used. Values correspond to original event ids.

References

[R39]

(1, 2) Aaron Schurger, Sebastien Marti, and Stanislas Dehaene, “Reducing multi-sensor data to a single time course that reveals experimental effects”, BMC Neuroscience 2013, 14:122

Examples using mne.decoding.compute_ems¶

Compute effect-matched-spatial filtering (EMS)