- mne.time_frequency.psd_welch(inst, fmin=0, fmax=inf, tmin=None, tmax=None, n_fft=256, n_overlap=0, n_per_seg=None, picks=None, proj=False, n_jobs=None, reject_by_annotation=True, average='mean', window='hamming', *, verbose=None)#
psd_welch()is deprecated; for Raw/Epochs/Evoked instances use
spectrum = instance.compute_psd(method="welch")instead, followed by
Compute the power spectral density (PSD) using Welch’s method.
Calculates periodograms for a sliding window over the time dimension, then optionally averages them together for each channel/epoch.
- instinstance of
The data for PSD calculation.
- 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).
- tmin, tmax
First and last times to include, in seconds.
Noneuses the first or last time present in the data. Default is
tmin=None, tmax=None(all times).
The length of FFT used, must be
>= n_per_seg(default: 256). The segments will be zero-padded if
n_fft > n_per_seg. If n_per_seg is None, n_fft must be <= number of time points in the data.
The number of points of overlap between segments. Will be adjusted to be <= n_per_seg. The default value is 0.
Length of each Welch segment (windowed with a Hamming window). Defaults to None, which sets n_per_seg equal to n_fft.
str| array_like |
Channels to include. Slices and lists of integers will be interpreted as channel indices. In lists, channel type strings (e.g.,
['meg', 'eeg']) will pick channels of those types, channel name strings (e.g.,
['MEG0111', 'MEG2623']will pick the given channels. Can also be the string values “all” to pick all channels, or “data” to pick data channels. None (default) will pick good data channels (excluding reference MEG channels). Note that channels in
info['bads']will be included if their names or indices are explicitly provided.
Whether to apply SSP projection vectors before spectral estimation. Default is
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
Whether to omit bad segments from the data before fitting. If
True(default), annotated segments whose description begins with
'bad'are omitted. If
False, no rejection based on annotations is performed.
Has no effect if
instis not a
New in version 0.15.0.
How to average the segments. If
mean(default), calculate the arithmetic mean. If
median, calculate the median, corrected for its bias relative to the mean. If
None, returns the unaggregated segments.
New in version 0.19.0.
Windowing function to use. See
New in version 0.22.0.
- instinstance of
ndarray, shape (…, n_freqs) or (…, n_freqs, n_segments)
The power spectral densities. If
average='median'and input is of type Raw or Evoked, then psds will be of shape (n_channels, n_freqs); if input is of type Epochs, then psds will be of shape (n_epochs, n_channels, n_freqs). If
average=None, the returned array will have an additional dimension corresponding to the unaggregated segments.
ndarray, shape (n_freqs,)
New in version 0.12.0.