Currently, MNE-Python provides a set of functions
allowing to compute spectral analyses in the source space.
Many these functions return mne.SourceEstimate() objects or collections thereof.
Note
The mne.SourceEstimate() object was initially designed for classical time-domain analyses.
In this context, the time axis can actually refer to frequencies. This might be improved
in the future.
The following functions are based on minimum norm estimates (MNE).
mne.minimum_norm.compute_source_psd_epochs() returns single-trial power spectral density (PSD) esitmates using multi-tapers.
Here, the time axis actually refers to frequencies, even if labeled as time.mne.minimum_norm.compute_source_psd() returns power spectral density (PSD) esitmates from continuous data usign FFT.
Here, the time axis actually refers to frequencies, even if labeled as time.mne.minimum_norm.source_band_induced_power() returns a collection of time-domain mne.SourceEstimate() for each
frequency band, based on Morlet-Wavelets.mne.minimum_norm.source_induced_power() returns power and inter-trial-coherence (ITC) as raw numpy arrays, based on Morlet-Wavelets.Alternatively, the source power spectral density can also be estimated using the DICS beamformer,
see mne.beamformer.dics_source_power().