mne.decoding.compute_ems

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

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. Note that channels in info['bads'] will be included if their names or indices are explicitly provided.

n_jobsint

The number of jobs to run in parallel (default 1). If -1, it is set to the number of CPU cores. Requires the joblib package.

cvcross-validation object | str | None, default LeaveOneOut

The cross-validation scheme.

verbosebool | str | int | None

Control verbosity of the logging output. If None, use the default verbosity level. See the logging documentation and mne.verbose() for details. Should only be passed as a keyword argument.

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. doi:10.1186/1471-2202-14-122.

Examples using mne.decoding.compute_ems

Compute effect-matched-spatial filtering (EMS)

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