Bad epochs can be marked with a left click on top of the epoch. Bad channels can be selected by clicking the channel name on the left side of the main axes. Calling this function drops all the selected bad epochs as well as bad epochs marked beforehand with rejection parameters.
The epochs object.
Channels to include. Slices and lists of integers will be interpreted as
channel indices. In lists, channel type strings (e.g.,
'eeg']) will pick channels of those types, channel name strings (e.g.,
['MEG0111', 'MEG2623'] will pick the given channels. Can also be the
string values “all” to pick all channels, or “data” to pick data
channels. None (default) will pick good data channels. Note that channels
info['bads'] will be included if their names or indices are
Scaling factors for the traces. If a dictionary where any
'auto', the scaling factor is set to match the 99.5th
percentile of the respective data. If
'auto', all scalings (for all
channel types) are set to
'auto'. If any values are
'auto' and the
data is not preloaded, a subset up to 100 MB will be loaded. If
dict(mag=1e-12, grad=4e-11, eeg=20e-6, eog=150e-6, ecg=5e-4, emg=1e-3, ref_meg=1e-12, misc=1e-3, stim=1, resp=1, chpi=1e-4, whitened=1e2)
A particular scaling value
s corresponds to half of the visualized
signal range around zero (i.e. from
+s or from
-s). For example, the default scaling of
20e-6 (20µV) for EEG
signals means that the visualized range will be 40 µV (20 µV in the
positive direction and 20 µV in the negative direction).
The number of epochs per view. Defaults to 20.
The number of channels per view. Defaults to 20.
The title of the window. If None, epochs name will be displayed. Defaults to None.
array, shape (n_events, 3)
Events to show with vertical bars. You can use
as a legend for the colors. By default, the coloring scheme is the
same. Defaults to
If the epochs have been resampled, the events no longer align with the data.
New in version 0.14.0.
Color(s) to use for events. To show all events in the same
color, pass any matplotlib-compatible color. To color events differently,
dict that maps event names or integer event numbers to colors
(must include entries for all events, or include a “fallback” entry with
None, colors are chosen from the current Matplotlib
Order in which to plot channel types.
New in version 0.18.0.
Show figure if True. Defaults to True.
Whether to halt program execution until the figure is closed. Useful for rejecting bad trials on the fly by clicking on an epoch. Defaults to False.
Amount to decimate the data during display for speed purposes.
You should only decimate if the data are sufficiently low-passed,
otherwise aliasing can occur. The ‘auto’ mode (default) uses
the decimation that results in a sampling rate at least three times
info['lowpass'] (e.g., a 40 Hz lowpass will result in
at least a 120 Hz displayed sample rate).
New in version 0.15.0.
Noise covariance used to whiten the data while plotting.
Whitened data channels are scaled by
and their channel names are shown in italic.
Can be a string to load a covariance from disk.
mne.Evoked.plot_white() for additional inspection
of noise covariance properties when whitening evoked data.
For data processed with SSS, the effective dependence between
magnetometers and gradiometers may introduce differences in scaling,
New in version 0.16.0.
Whether to directly call the butterfly view.
New in version 0.18.0.
Whether to show scrollbars when the plot is initialized. Can be toggled
after initialization by pressing z (“zen mode”) while the plot
window is focused. Default is
New in version 0.19.0.
Whether to show scale bars when the plot is initialized. Can be toggled
after initialization by pressing s while the plot window is focused.
New in version 0.24.0.
list(of n_channels) |
Colors to use for individual epochs. If None, use default colors.
Dictionary of event labels (e.g. ‘aud_l’) as keys and associated event
integers as values. Useful when
events contains event numbers not
epochs.event_id (e.g., because of event subselection).
event_id will take precedence over those in
epochs.event_id when there are overlapping keys.
New in version 0.20.
How to group channels.
'type' groups by channel type,
'original' plots in the order of ch_names,
Elekta’s channel groupings (only works for Neuromag data),
'position' groups the channels by the positions of the sensors.
'position' modes allow custom selections by
using a lasso selector on the topomap. In butterfly mode,
'original' group the channels by type, whereas
'position' use regional grouping.
modes are ignored when
order is not
None. Defaults to
Whether to load all data (not just the visible portion) into RAM and
apply preprocessing (e.g., projectors) to the full data array in a separate
processor thread, instead of window-by-window during scrolling. The default
None uses the
MNE_BROWSER_PRECOMPUTE variable, which defaults to
'auto' compares available RAM space to the expected size of
the precomputed data, and precomputes only if enough RAM is available.
This is only used with the Qt backend.
New in version 0.24.
Changed in version 1.0: Support for the MNE_BROWSER_PRECOMPUTE config variable.
Whether to use OpenGL when rendering the plot (requires
May increase performance, but effect is dependent on system CPU and
graphics hardware. Only works if using the Qt backend. Default is
None, which will use False unless the user configuration variable
MNE_BROWSER_USE_OPENGL is set to
New in version 0.24.
Can be “auto”, “light”, or “dark” or a path-like to a
custom stylesheet. For Dark-Mode and automatic Dark-Mode-Detection,
respectively, are required. If None (default), the config option MNE_BROWSER_THEME will be used,
defaulting to “auto” if it’s not found.
Only supported by the
New in version 1.0.
Can be “channels”, “empty”, or “hidden” to set the overview bar mode
'qt' backend. If None (default), the config option
MNE_BROWSER_OVERVIEW_MODE will be used, defaulting to “channels”
if it’s not found.
New in version 1.1.
The arrow keys (up/down/left/right) can be used to navigate between
channels and epochs and the scaling can be adjusted with - and + (or =)
keys, but this depends on the backend matplotlib is configured to use
TkAgg) should work). Full screen mode can be toggled
with f11 key. The amount of epochs and channels per view can be adjusted
with home/end and page down/page up keys.
h key plots a histogram of
peak-to-peak values along with the used rejection thresholds. Butterfly
plot can be toggled with
b key. Left mouse click adds a vertical line
to the plot. Click ‘help’ button at bottom left corner of the plotter to
view all the options.
New in version 0.10.0.