- mne.time_frequency.psd_array_multitaper(x, sfreq, fmin=0.0, fmax=inf, bandwidth=None, adaptive=False, low_bias=True, normalization='length', output='power', n_jobs=None, *, max_iter=150, verbose=None)#
Compute power spectral density (PSD) using a multi-taper method.
The power spectral density is computed with DPSS tapers.
array, shape=(…, n_times)
The data to compute PSD from.
The sampling frequency.
- fmin, fmax
The lower- and upper-bound on frequencies of interest. Default is
fmin=0, fmax=np.inf(spans all frequencies present in the data).
Half-bandwidth of the multi-taper window function in Hz. For a given frequency, frequencies at ± half-bandwidth are smoothed together. The default value is a half-bandwidth of 4.
Use adaptive weights to combine the tapered spectra into PSD (slow, use n_jobs >> 1 to speed up computation).
Only use tapers with more than 90% spectral concentration within bandwidth.
- normalization‘full’ | ‘length’
Normalization strategy. If “full”, the PSD will be normalized by the sampling rate as well as the length of the signal (as in Nitime). Default is
The format of the returned
'power': the power spectral density is returned.
'complex': the complex fourier coefficients are returned per taper.
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
ndarray, shape (…, n_freqs) or (…, n_tapers, n_freqs)
The power spectral densities. All dimensions up to the last (or the last two if
output='complex') will be the same as input.
The frequency points in Hz of the PSD.
The weights used for averaging across tapers. Only returned if
New in version 0.14.0.