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
mne.preprocessing.find_eog_events()
.
Create Epochs
around the eyeblinks.
Raw
The raw data.
str
| 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
.
int
The index to assign to found events.
str
| list
| 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.
float
Start time before event.
float
End time after event.
float
Low pass frequency to apply to the EOG channel while finding events.
float
High pass frequency to apply to the EOG channel while finding events.
dict
| 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)
)
dict
| 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.
tuple
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 version 0.14.0.
float
Threshold to trigger EOG event.
int
Factor by which to subsample the data.
Warning
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.
New in version 0.21.0.
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.
Epochs
Data epoched around EOG events.
See also
Notes
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).
mne.preprocessing.create_eog_epochs
#Getting started with mne.Report
Overview of artifact detection
Rejecting bad data spans and breaks
Repairing artifacts with regression
From raw data to dSPM on SPM Faces dataset