mne.minimum_norm.compute_source_psd¶

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)[source]¶

Compute source power spectrum density (PSD).

Parameters:

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() and Logging documentation for more).

Returns:

stc : SourceEstimate | VolSourceEstimate The PSD (in dB) of each of the sources.

Examples using mne.minimum_norm.compute_source_psd¶

Compute power spectrum densities of the sources with dSPM