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', window='hamming', 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 zero-padded 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.
- window
str
|float
|tuple
Windowing function to use. See
scipy.signal.get_window()
.New in version 0.22.0.
- verbosebool,
str
,int
, orNone
If not None, override default verbose level (see
mne.verbose()
and Logging documentation for more). If used, it should be passed as a keyword-argument only.
- 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.