mne.minimum_norm.compute_source_psd_epochs

mne.minimum_norm.compute_source_psd_epochs(epochs, inverse_operator, lambda2=0.1111111111111111, method='dSPM', fmin=0.0, fmax=200.0, pick_ori=None, label=None, nave=1, pca=True, inv_split=None, bandwidth=4.0, adaptive=False, low_bias=True, return_generator=False, n_jobs=1, prepared=False, method_params=None, return_sensor=False, verbose=None)[source]

Compute source power spectrum density (PSD) from Epochs.

This uses the multi-taper method to compute the PSD for each epoch.

Parameters:
epochs : instance of Epochs

The raw data.

inverse_operator : instance of InverseOperator

The inverse operator.

lambda2 : float

The regularization parameter.

method : “MNE” | “dSPM” | “sLORETA” | “eLORETA”

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

fmin : float

The lower frequency of interest.

fmax : float

The upper frequency of interest.

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).

inv_split : int or None

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

bandwidth : float | str

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

adaptive : bool

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

low_bias : bool

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

return_generator : bool

Return a generator object instead of a list. This allows iterating over the stcs without having to keep them all in memory.

n_jobs : int

Number of parallel jobs to use (only used if adaptive=True).

prepared : bool

If True, do not call prepare_inverse_operator().

method_params : dict | None

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

New in version 0.16.

return_sensor : bool

If True, also return the sensor PSD for each epoch as an EvokedArray.

New in version 0.17.

verbose : bool, str, int, or None

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

Returns:
out : list (or generator object)

A list (or generator) for the source space PSD (and optionally the sensor PSD) for each epoch.

Examples using mne.minimum_norm.compute_source_psd_epochs