mne.time_frequency.psd_array_multitaper#

mne.time_frequency.psd_array_multitaper(x, sfreq, fmin=0, fmax=inf, bandwidth=None, adaptive=False, low_bias=True, normalization='length', output='power', n_jobs=1, verbose=None)[source]#

Compute power spectral density (PSD) using a multi-taper method.

Parameters

xarray, shape=(…, n_times): The data to compute PSD from.
sfreqfloat: The sampling frequency.
fminfloat: The lower frequency of interest.
fmaxfloat: The upper frequency of interest.
bandwidthfloat: The bandwidth of the multi taper windowing function in Hz.
adaptivebool: Use adaptive weights to combine the tapered spectra into PSD (slow, use n_jobs >> 1 to speed up computation).
low_biasbool: 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 'length'.
outputstr: The format of the returned psds array. Can be either 'complex' or 'power'. If 'power', the power spectral density is returned. If output='complex', the complex fourier coefficients are returned per taper.
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.
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

psdsndarray, 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.
freqsarray: The frequency points in Hz of the PSD.
weightsndarray: The weights used for averaging across tapers. Only returned if output='complex'.