mne.preprocessing.create_eog_epochs(raw, ch_name=None, event_id=998, picks=None, tmin=-0.5, tmax=0.5, l_freq=1, h_freq=10, reject=None, flat=None, baseline=None, preload=True, reject_by_annotation=True, thresh=None, decim=1, verbose=None)[source]#

Conveniently generate epochs around EOG artifact events.

This function will:

  1. Filter the EOG data channel.

  2. Find the peaks of eyeblinks in the EOG data using mne.preprocessing.find_eog_events().

  3. Create Epochs around the eyeblinks.

rawinstance of Raw

The raw data.

ch_namestr | list of str | None

The name of the channel(s) to use for EOG peak detection. If a string, can be an arbitrary channel. This doesn’t have to be a channel of eog type; it could, for example, also be an ordinary EEG channel that was placed close to the eyes, like Fp1 or Fp2.

Multiple channel names can be passed as a list of strings.

If None (default), use the channel(s) in raw with type eog.


The index to assign to found events.

picksstr | array_like | slice | None

Channels to include. Slices and lists of integers will be interpreted as channel indices. In lists, channel type strings (e.g., ['meg', '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 all channels. Note that channels in info['bads'] will be included if their names or indices are explicitly provided.


Start time before event.


End time after event.


Low pass frequency to apply to the EOG channel while finding events.


High pass frequency to apply to the EOG channel while finding events.

rejectdict | None

Rejection parameters based on peak-to-peak amplitude. Valid keys are ‘grad’ | ‘mag’ | ‘eeg’ | ‘eog’ | ‘ecg’. If reject is None then no rejection is done. Example:

reject = dict(grad=4000e-13, # T / m (gradiometers)
              mag=4e-12, # T (magnetometers)
              eeg=40e-6, # V (EEG channels)
              eog=250e-6 # V (EOG channels)
flatdict | None

Rejection parameters based on flatness of signal. Valid keys are ‘grad’ | ‘mag’ | ‘eeg’ | ‘eog’ | ‘ecg’, and values are floats that set the minimum acceptable peak-to-peak amplitude. If flat is None then no rejection is done.

baselinetuple or list of length 2, or None

The time interval to apply rescaling / baseline correction. If None do not apply it. If baseline is (a, b) the interval is between “a (s)” and “b (s)”. If a is None the beginning of the data is used and if b is None then b is set to the end of the interval. If baseline is equal to (None, None) all the time interval is used. If None, no correction is applied.


Preload epochs or not.


Whether to reject based on annotations. If True (default), epochs overlapping with segments whose description begins with 'bad' are rejected. If False, no rejection based on annotations is performed.

New in v0.14.0.


Threshold to trigger EOG event.


Factor by which to subsample the data.


Low-pass filtering is not performed, this simply selects every Nth sample (where N is the value passed to decim), i.e., it compresses the signal (see Notes). If the data are not properly filtered, aliasing artifacts may occur. See Resampling and decimating data for more information.

New in v0.21.0.

verbosebool | str | int | None

Control verbosity of the logging output. If None, use the default verbosity level. See the logging documentation and mne.verbose() for details. Should only be passed as a keyword argument.

eog_epochsinstance of Epochs

Data epoched around EOG events.


Filtering is only applied to the EOG channel while finding events. The resulting eog_epochs will have no filtering applied (i.e., have the same filter properties as the input raw instance).

Examples using mne.preprocessing.create_eog_epochs#

Getting started with mne.Report

Getting started with mne.Report

Overview of artifact detection

Overview of artifact detection

Repairing artifacts with regression

Repairing artifacts with regression

Repairing artifacts with ICA

Repairing artifacts with ICA

Repairing artifacts with SSP

Repairing artifacts with SSP

From raw data to dSPM on SPM Faces dataset

From raw data to dSPM on SPM Faces dataset