plot_evoked

plotly.mne_plotting.plot_evoked(
    epo: mne.BaseEpochs,
    dp: Optional[pl.DataFrame] = None,
    time_topo: Optional[list[float]] = None,
    cmap: Optional[dict[str, str]] = None,
    mean_ci: bool = True,
)

Create interactive evoked response plot with optional topoplots.

Visualizes event-related potentials (ERPs) with mean and confidence intervals for all channels. Optionally adds topographic maps at specific time points to show spatial distribution of activity.

Parameters

Name	Type	Description	Default
epo	mne.BaseEpochs	MNE Epochs object containing the data to plot. Each channel will be displayed as a separate line with mean ± CI across epochs.	required
dp	pl.DataFrame	Pre-computed Polars DataFrame from mne_epochs_to_polars(). If None, will be computed automatically. Must contain ‘sample_idx’, ‘epoch_nr’, ‘time’ columns plus all channel columns. Default is None.	None
time_topo	list of float	Time points (in seconds) at which to display topographic maps. If provided, creates a subplot layout with topoplots in the top row and the time series in the bottom row. If None, only the time series is displayed. Default is None.	None
cmap	dict of str to str	Custom color map for channels. Keys are channel names, values are hex colors. If None, uses Viridis colorscale sampled across all channels. Default is None.	None
mean_ci	bool	If True, show confidence intervals (SEM) around the mean trace for each channel. If False, only the mean trace is shown. Default is True.	True

Returns

Name	Type	Description
	go.Figure	Plotly Figure with: - If time_topo is None: Single plot showing all channel traces - If time_topo is provided: Subplot layout with topoplots (top) and time series (bottom) with connecting lines to mark time points

Raises

Name	Type	Description
	ValueError	If dp is provided but doesn’t contain required columns (‘sample_idx’, ‘epoch_nr’, ‘time’, and all channel names from epo).

Examples

>>> import mne
>>> from mdu.plotly.mne_plotting import plot_evoked
>>> # Load sample data
>>> sample_data = mne.datasets.sample.data_path()
>>> raw_fname = sample_data / 'MEG' / 'sample' / 'sample_audvis_raw.fif'
>>> raw = mne.io.read_raw_fif(raw_fname, preload=True)
>>> raw.pick_types(meg=False, eeg=True)
>>>
>>> # Create epochs
>>> events_fname = sample_data / 'MEG' / 'sample' / 'sample_audvis_raw-eve.fif'
>>> events = mne.read_events(events_fname)
>>> epochs = mne.Epochs(raw, events, tmin=-0.2, tmax=0.5)
>>>
>>> # Simple evoked plot
>>> fig = plot_evoked(epochs)
>>> fig.show()
>>>
>>> # With topoplots at specific times
>>> fig = plot_evoked(epochs, time_topo=[0.1, 0.2, 0.3])
>>> fig.show()
>>>
>>> # Custom color scheme
>>> custom_colors = {ch: '#1f77b4' for ch in epochs.ch_names[:10]}
>>> fig = plot_evoked(epochs, cmap=custom_colors)
>>> fig.show()

See Also

mdu.plotly.multiline.multiline_plot : Underlying plotting function for time series mdu.plotly.mne_plotting_utils.topoplot.create_plotly_topoplot : Topoplot creation mdu.mne.mne2dataframe.mne_epochs_to_polars : Convert epochs to DataFrame

Notes

• Data is automatically scaled to microvolts (µV)
• Confidence intervals are computed using bootstrapping across epochs
• Topoplots use Clough-Tocher interpolation for smooth spatial distribution
• When time_topo is used, connecting lines link time points to their topoplots