- mne.time_frequency.csd_array_multitaper(X, sfreq, t0=0, fmin=0, fmax=inf, tmin=None, tmax=None, ch_names=None, n_fft=None, bandwidth=None, adaptive=False, low_bias=True, projs=None, n_jobs=None, max_iter=250, *, verbose=None)#
Estimate cross-spectral density from an array using a multitaper method.
- Xarray_like, shape (n_epochs, n_channels, n_times)
The time series data consisting of n_epochs separate observations of signals with n_channels time-series of length n_times.
Sampling frequency of observations.
Time of the first sample relative to the onset of the epoch, in seconds. Defaults to 0.
Minimum frequency of interest, in Hertz.
Maximum frequency of interest, in Hertz.
Minimum time instant to consider, in seconds. If
Nonestart at first sample.
Maximum time instant to consider, in seconds. If
Noneend at last sample.
A name for each time series. If
None(the default), the series will be named ‘SERIES###’.
Length of the FFT. If
None, the exact number of samples between
tmaxwill be used.
The bandwidth of the multitaper windowing function in Hz.
Use adaptive weights to combine the tapered spectra into PSD.
Only use tapers with more than 90% spectral concentration within bandwidth.
List of projectors to store in the CSD object. Defaults to
None, which means no projectors are stored.
The number of jobs to run in parallel. If
-1, it is set to the number of CPU cores. Requires the
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
Maximum number of iterations to reach convergence when combining the tapered spectra with adaptive weights (see argument
adaptive). This argument has not effect if
adaptiveis set to
- csdinstance of
The computed cross-spectral density.
- csdinstance of