mne.time_frequency.psd_array_welch¶

mne.time_frequency.
psd_array_welch
(x, sfreq, fmin=0, fmax=inf, n_fft=256, n_overlap=0, n_per_seg=None, n_jobs=1, average='mean', verbose=None)[source]¶ Compute power spectral density (PSD) using Welch’s method.
 Parameters
 x
array
, shape=(…, n_times) The data to compute PSD from.
 sfreq
float
The sampling frequency.
 fmin
float
The lower frequency of interest.
 fmax
float
The upper frequency of interest.
 n_fft
int
The length of FFT used, must be
>= n_per_seg
(default: 256). The segments will be zeropadded ifn_fft > n_per_seg
. n_overlap
int
The number of points of overlap between segments. Will be adjusted to be <= n_per_seg. The default value is 0.
 n_per_seg
int
None
Length of each Welch segment (windowed with a Hamming window). Defaults to None, which sets n_per_seg equal to n_fft.
 n_jobs
int
The number of jobs to run in parallel (default 1). Requires the joblib package.
 average
str
None
How to average the segments. If
mean
(default), calculate the arithmetic mean. Ifmedian
, calculate the median, corrected for its bias relative to the mean. IfNone
, returns the unaggregated segments.New in version 0.19.0.
 verbosebool,
str
,int
, orNone
If not None, override default verbose level (see
mne.verbose()
and Logging documentation for more).
 x
 Returns
 psds
ndarray
, shape (…, n_freqs) or (…, n_freqs, n_segments) The power spectral densities. If
average='mean
oraverage='median'
, the returned array will have the same shape as the input data plus an additional frequency dimension. Ifaverage=None
, the returned array will have the same shape as the input data plus two additional dimensions corresponding to frequencies and the unaggregated segments, respectively. freqs
ndarray
, shape (n_freqs,) The frequencies.
 psds
Notes
New in version 0.14.0.