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====================================
Spectral and Time-frequency Analysis
====================================
Source Space
^^^^^^^^^^^^
Currently, MNE-Python provides a set of functions
allowing to compute spectral analyses in the source space.
Many these functions return :func:`mne.SourceEstimate` objects or collections thereof.
.. note::
The :func:`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).
- :func:`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.
- :func:`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.
- :func:`mne.minimum_norm.source_band_induced_power` returns a collection of time-domain :func:`mne.SourceEstimate` for each
frequency band, based on Morlet-Wavelets.
- :func:`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 :func:`mne.beamformer.dics_source_power`.
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