mne.minimum_norm.compute_source_psd(raw, inverse_operator, lambda2=0.1111111111111111, method='dSPM', tmin=None, tmax=None, fmin=0.0, fmax=200.0, n_fft=2048, overlap=0.5, pick_ori=None, label=None, nave=1, pca=True, prepared=False, verbose=None)

Compute source power spectrum density (PSD)


raw : instance of Raw

The raw data

inverse_operator : instance of InverseOperator

The inverse operator

lambda2: float

The regularization parameter

method: “MNE” | “dSPM” | “sLORETA”

Use mininum norm, dSPM or sLORETA

tmin : float | None

The beginning of the time interval of interest (in seconds). If None start from the beginning of the file.

tmax : float | None

The end of the time interval of interest (in seconds). If None stop at the end of the file.

fmin : float

The lower frequency of interest

fmax : float

The upper frequency of interest

n_fft: int

Window size for the FFT. Should be a power of 2.

overlap: float

The overlap fraction between windows. Should be between 0 and 1. 0 means no overlap.

pick_ori : None | “normal”

If “normal”, rather than pooling the orientations by taking the norm, only the radial component is kept. This is only implemented when working with loose orientations.

label: Label

Restricts the source estimates to a given label

nave : int

The number of averages used to scale the noise covariance matrix.

pca: bool

If True, the true dimension of data is estimated before running the time-frequency transforms. It reduces the computation times e.g. with a dataset that was maxfiltered (true dim is 64).

prepared : bool

If True, do not call prepare_inverse_operator.

verbose : bool, str, int, or None

If not None, override default verbose level (see mne.verbose).


stc : SourceEstimate | VolSourceEstimate

The PSD (in dB) of each of the sources.

Examples using mne.minimum_norm.compute_source_psd