mne.minimum_norm.compute_source_psd

mne.minimum_norm.compute_source_psd(raw, inverse_operator, lambda2=0.1111111111111111, method='dSPM', tmin=0.0, 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, method_params=None, inv_split=None, bandwidth='hann', adaptive=False, low_bias=False, n_jobs=1, return_sensor=False, dB=False, verbose=None)[source]

Compute source power spectrum density (PSD).

Parameters
rawinstance of Raw

The raw data

inverse_operatorinstance of InverseOperator

The inverse operator

lambda2: float

The regularization parameter

method: “MNE” | “dSPM” | “sLORETA”

Use minimum norm, dSPM (default), sLORETA, or eLORETA.

tminfloat

The beginning of the time interval of interest (in seconds). Use 0. for the beginning of the file.

tmaxfloat | None

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

fminfloat

The lower frequency of interest

fmaxfloat

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_oriNone | “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

naveint

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).

preparedbool

If True, do not call prepare_inverse_operator().

method_paramsdict | None

Additional options for eLORETA. See Notes of apply_inverse().

New in version 0.16.

inv_splitint or None

Split inverse operator into inv_split parts in order to save memory.

New in version 0.17.

bandwidthfloat | str

The bandwidth of the multi taper windowing function in Hz. Can also be a string (e.g., ‘hann’) to use a single window.

For backward compatibility, the default is ‘hann’.

New in version 0.17.

adaptivebool

Use adaptive weights to combine the tapered spectra into PSD (slow, use n_jobs >> 1 to speed up computation).

New in version 0.17.

low_biasbool

Only use tapers with more than 90% spectral concentration within bandwidth.

New in version 0.17.

n_jobsint

The number of jobs to run in parallel (default 1). Requires the joblib package. It is only used if adaptive=True.

New in version 0.17.

return_sensorbool

If True, return the sensor PSDs as an EvokedArray.

New in version 0.17.

dBbool

If True (default False), return output it decibels.

New in version 0.17.

verbosebool, str, int, or None

If not None, override default verbose level (see mne.verbose() and Logging documentation for more).

Returns
stc_psdinstance of SourceEstimate | VolSourceEstimate

The PSD of each of the sources.

sensor_psdinstance of EvokedArray

The PSD of each sensor. Only returned if return_sensor is True.

Notes

Each window is multiplied by a window before processing, so using a non-zero overlap is recommended.

This function is different from compute_source_psd_epochs() in that:

  1. bandwidth='hann' by default, skipping multitaper estimation

  2. For convenience it wraps mne.make_fixed_length_events() and mne.Epochs.

Otherwise the two should produce identical results.