mne.beamformer.rap_music¶

mne.beamformer.rap_music(evoked, forward, noise_cov, n_dipoles=5, return_residual=False, picks=None, verbose=None)[source]¶

RAP-MUSIC source localization method.

Compute Recursively Applied and Projected MUltiple SIgnal Classification (RAP-MUSIC) on evoked data.

Parameters:

evoked : instance of Evoked Evoked data to localize. forward : instance of Forward Forward operator. noise_cov : instance of Covariance The noise covariance. n_dipoles : int The number of dipoles to look for. The default value is 5. return_residual : bool If True, the residual is returned as an Evoked instance. picks : array-like of int | None Indices (in info) of data channels. If None, MEG and EEG data channels (without bad channels) will be used. verbose : bool, str, int, or None If not None, override default verbose level (see mne.verbose() and Logging documentation for more).

Returns:

dipoles : list of instance of Dipole The dipole fits. residual : instance of Evoked The residual a.k.a. data not explained by the dipoles. Only returned if return_residual is True.

See also

mne.fit_dipole

Notes

The references are:

J.C. Mosher and R.M. Leahy. 1999. Source localization using recursively applied and projected (RAP) MUSIC. Signal Processing, IEEE Trans. 47, 2 (February 1999), 332-340. DOI=10.1109/78.740118 https://doi.org/10.1109/78.740118

Mosher, J.C.; Leahy, R.M., EEG and MEG source localization using recursively applied (RAP) MUSIC, Signals, Systems and Computers, 1996. pp.1201,1207 vol.2, 3-6 Nov. 1996 doi: 10.1109/ACSSC.1996.599135

New in version 0.9.0.

Examples using mne.beamformer.rap_music¶

Compute Rap-Music on evoked data