Conveniently generate epochs around EOG artifact events.
This function will:
Filter the EOG data channel.
Find the peaks of eyeblinks in the EOG data using
Epochs around the eyeblinks.
The raw data.
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
Multiple channel names can be passed as a list of strings.
None (default), use the channel(s) in
raw with type
The index to assign to found events.
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 all channels. Note that channels in
info['bads'] will be included if their names or indices are
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.
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) )
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.
listof length 2, or
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
False, no rejection based on annotations is performed.
New in version 0.14.0.
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
New in version 0.21.0.
Data epoched around EOG events.
Filtering is only applied to the EOG channel while finding events.
eog_epochs will have no filtering applied (i.e., have
the same filter properties as the input