This documentation is for development version 0.18.dev0.

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{‘verbose’: ‘n verbose : bool, str, int, or Nonen If not None, override default verbose level (see mne.verbose()n and Logging documentation for more).’, ‘verbose_meth’: ‘n verbose : bool, str, int, or Nonen If not None, override default verbose level (see mne.verbose()n and Logging documentation for more). Defaults to self.verbose.’, ‘picks_header’: ‘picks : str | list | slice | None’, ‘picks_base’: ‘picks : str | list | slice | Nonen Channels to include. Slices and lists of integers will ben interpreted as channel indices. In lists, channel type stringsn (e.g., [\'meg\', \'eeg\']) will pick channels of thosen types, channel name strings (e.g., [\'MEG0111\', \'MEG2623\']n will pick the given channels. Can also be the string valuesn “all” to pick all channels, or “data” to pick data channels.n None (default) will pick ‘, ‘picks_all’: ‘picks : str | list | slice | Nonen Channels to include. Slices and lists of integers will ben interpreted as channel indices. In lists, channel type stringsn (e.g., [\'meg\', \'eeg\']) will pick channels of thosen types, channel name strings (e.g., [\'MEG0111\', \'MEG2623\']n will pick the given channels. Can also be the string valuesn “all” to pick all channels, or “data” to pick data channels.n None (default) will pick all channels.’, ‘picks_all_data’: ‘picks : str | list | slice | Nonen Channels to include. Slices and lists of integers will ben interpreted as channel indices. In lists, channel type stringsn (e.g., [\'meg\', \'eeg\']) will pick channels of thosen types, channel name strings (e.g., [\'MEG0111\', \'MEG2623\']n will pick the given channels. Can also be the string valuesn “all” to pick all channels, or “data” to pick data channels.n None (default) will pick all data channels.’, ‘picks_all_data_noref’: ‘picks : str | list | slice | Nonen Channels to include. Slices and lists of integers will ben interpreted as channel indices. In lists, channel type stringsn (e.g., [\'meg\', \'eeg\']) will pick channels of thosen types, channel name strings (e.g., [\'MEG0111\', \'MEG2623\']n will pick the given channels. Can also be the string valuesn “all” to pick all channels, or “data” to pick data channels.n None (default) will pick all data channels(excluding reference MEG channels).’, ‘picks_good_data’: ‘picks : str | list | slice | Nonen Channels to include. Slices and lists of integers will ben interpreted as channel indices. In lists, channel type stringsn (e.g., [\'meg\', \'eeg\']) will pick channels of thosen types, channel name strings (e.g., [\'MEG0111\', \'MEG2623\']n will pick the given channels. Can also be the string valuesn “all” to pick all channels, or “data” to pick data channels.n None (default) will pick good data channels.’, ‘picks_good_data_noref’: ‘picks : str | list | slice | Nonen Channels to include. Slices and lists of integers will ben interpreted as channel indices. In lists, channel type stringsn (e.g., [\'meg\', \'eeg\']) will pick channels of thosen types, channel name strings (e.g., [\'MEG0111\', \'MEG2623\']n will pick the given channels. Can also be the string valuesn “all” to pick all channels, or “data” to pick data channels.n None (default) will pick good data channels(excluding reference MEG channels).’, ‘picks_nostr’: ‘n picks : list | slice | Nonen Channels to include. Slices and lists of integers will ben interpreted as channel indices. None (default) will pick all channels.’}pectral 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