mne.decoding.compute_ems¶

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

Compute event-matched spatial filter on epochs.

This version of EMS [1] 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

epochsinstance of mne.Epochs: The epochs.
conditionslist of str | None, default 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.
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 good data channels.
n_jobsint: The number of jobs to run in parallel (default 1). Requires the joblib package.
cvcross-validation object | str | None, default LeaveOneOut: The cross-validation scheme.
verbosebool, str, int, or None: If not None, override default verbose level (see mne.verbose() and Logging documentation for more). If used, it should be passed as a keyword-argument only.

Returns

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

References

1: 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