Estimate cross-spectral density from epochs using Morlet wavelets.
EpochsThe epochs to compute the CSD for.
list of floatThe frequencies of interest, in Hertz.
float | NoneMinimum time instant to consider, in seconds. If None start at
first sample.
float | NoneMaximum time instant to consider, in seconds. If None end at last
sample.
str | list | slice | NoneChannels 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 (excluding reference
MEG channels). Note that channels in info['bads'] will be included if
their names or indices are explicitly provided.
float | list of float | NoneNumber of cycles to use when constructing Morlet wavelets. Fixed number or one per frequency. Defaults to 7.
Whether to use FFT-based convolution to compute the wavelet transform. Defaults to True.
int | sliceTo reduce memory usage, decimation factor during time-frequency decomposition. Defaults to 1 (no decimation).
list of Projection | NoneList of projectors to store in the CSD object. Defaults to None,
which means the projectors defined in the Epochs object will be copied.
int | NoneThe number of jobs to run in parallel. If -1, it is set
to the number of CPU cores. Requires the joblib package.
None (default) is a marker for ‘unset’ that will be interpreted
as n_jobs=1 (sequential execution) unless the call is performed under
a joblib.parallel_backend() context manager that sets another
value for n_jobs.
str | int | NoneControl 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.
CrossSpectralDensityThe computed cross-spectral density.
mne.time_frequency.csd_morlet#Compute evoked ERS source power using DICS, LCMV beamformer, and dSPM