mne.preprocessing.cortical_signal_suppression¶
- mne.preprocessing.cortical_signal_suppression(evoked, picks=None, mag_picks=None, grad_picks=None, n_proj=6, *, verbose=None)[source]¶
Apply cortical signal suppression (CSS) to evoked data.
- Parameters
- evokedinstance of
Evoked The evoked object to use for CSS. Must contain magnetometer, gradiometer, and EEG channels.
- picks
str|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 ininfo['bads']will be included if their names or indices are explicitly provided.- mag_picksarray_like of
int Array of the magnetometer channel indices that will be used to find the reference data. If None (default), all magnetometers will be used.
- grad_picksarray_like of
int Array of the gradiometer channel indices that will be used to find the reference data. If None (default), all gradiometers will be used.
- n_proj
int The number of projection vectors.
- verbosebool |
str|int|None Control verbosity of the logging output. If
None, use the default verbosity level. See the logging documentation andmne.verbose()for details. Should only be passed as a keyword argument.
- evokedinstance of
- Returns
- evoked_subcorticalinstance of
Evoked The evoked object with cortical contributions to the EEG data suppressed.
- evoked_subcorticalinstance of
Notes
This method removes the common signal subspace between the magnetometer data and the gradiometer data from the EEG data. This is done by a temporal projection using
n_projnumber of projection vectors. For reference, see 1.References
- 1
John Samuelsson, Sheraz Khan, Padma Sundaram, Noam Peled, and Matti Hämäläinen. Cortical signal suppression (css) for detection of subcortical activity using meg and eeg. Brain Topography, 32:215–228, 2019. doi:10.1007/s10548-018-00694-5.
