bemobil_mne.viz.plot_TFR

bemobil_mne.viz.plot_TFR(inst_dict, *, freqs=array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40]), tmin=-0.5, tmax=1, baseline=None, picks=None, combine=None, is_sources=False, tfr_kwargs=None, plot_kwargs=None)

Plot Event-Related Spectral Perturbations (ERSP) for given events.

Parameters:

inst_dictdict

Dictionary of epochs or evoked, grouped by condition such as: {"condition": mne.Epochs} or {"condition": mne.Evoked}. If epochs are passed, the PSD is computed per epoch and then averaged over epochs. It’s best to provide epochs, since they contain more information about the time-frequency dynamics of the data.

freqslist of float

The frequencies to resolve over.

tminfloat | None

Start time before event, by default -0.5.

tmaxfloat | None

End time after event, by default 1.

baselinetuple | None

Time interval for baseline correction, by default (None, 0).

pickslist of str or list of int | None

Channel names or, in the case of ICA, channel indices, e.g., [3]. Defaults to None, which means picking all channels or sources.

combinestr | None

If str, may be one of {‘mean’, ‘rms’}, which will then use the specified metric to combine all channels (or sources) specified via picks. If None, each channel (or source) will be plotted separately.

is_sourcesbool | None

If True, indicates that the input data are ICA sources. Defaults to False.

tfr_kwargsdict | None

Additional keyword arguments for the TFR computation. If None, defaults are used.

plot_kwargsdict | None

Additional keyword arguments for the TFR plotting function. If None, defaults are used.

Returns:

figsdictionary of figures

A dictionary mapping event names to matplotlib figures.