mne.viz.plot_epochs_image

mne.viz.plot_epochs_image(epochs, picks=None, sigma=0.0, vmin=None, vmax=None, colorbar=True, order=None, show=True, units=None, scalings=None, cmap='RdBu_r', fig=None)

Plot Event Related Potential / Fields image

Parameters:

epochs : instance of Epochs

The epochs

picks : int | array-like of int | None

The indices of the channels to consider. If None, all good data channels are plotted.

sigma : float

The standard deviation of the Gaussian smoothing to apply along the epoch axis to apply in the image. If 0., no smoothing is applied.

vmin : float

The min value in the image. The unit is uV for EEG channels, fT for magnetometers and fT/cm for gradiometers

vmax : float

The max value in the image. The unit is uV for EEG channels, fT for magnetometers and fT/cm for gradiometers

colorbar : bool

Display or not a colorbar

order : None | array of int | callable

If not None, order is used to reorder the epochs on the y-axis of the image. If it’s an array of int it should be of length the number of good epochs. If it’s a callable the arguments passed are the times vector and the data as 2d array (data.shape[1] == len(times)

show : bool

Show figure if True.

units : dict | None

The units of the channel types used for axes lables. If None, defaults to units=dict(eeg=’uV’, grad=’fT/cm’, mag=’fT’).

scalings : dict | None

The scalings of the channel types to be applied for plotting. If None, defaults to scalings=dict(eeg=1e6, grad=1e13, mag=1e15, eog=1e6)

cmap : matplotlib colormap

Colormap.

fig : matplotlib figure | None

Figure instance to draw the image to. Figure must contain two axes for drawing the single trials and evoked responses. If None a new figure is created. Defaults to None.

Returns:

figs : the list of matplotlib figures

One figure per channel displayed