mne.io.read_raw_eyelink#
- mne.io.read_raw_eyelink(fname, *, create_annotations=True, apply_offsets=False, find_overlaps=False, overlap_threshold=0.05, verbose=None) RawEyelink [source]#
Reader for an Eyelink
.asc
file.- Parameters:
- fnamepath-like
Path to the eyelink file (
.asc
).- create_annotationsbool |
list
(defaultTrue
) Whether to create
Annotations
from occular events (blinks, fixations, saccades) and experiment messages. If a list, must contain one or more of['fixations', 'saccades',' blinks', messages']
. If True, createsAnnotations
for both occular events and experiment messages.- apply_offsetsbool (default
False
) Adjusts the onset time of the
Annotations
created from Eyelink experiment messages, if offset values exist in the ASCII file. If False, any offset-like values will be prepended to the annotation description.- find_overlapsbool (default
False
) Combine left and right eye
mne.Annotations
(blinks, fixations, saccades) if their start times and their stop times are both not separated by more than overlap_threshold.- overlap_threshold
float
(default 0.05) Time in seconds. Threshold of allowable time-gap between both the start and stop times of the left and right eyes. If the gap is larger than the threshold, the
mne.Annotations
will be kept separate (i.e."blink_L"
,"blink_R"
). If the gap is smaller than the threshold, themne.Annotations
will be merged and labeled as"blink_both"
. Defaults to0.05
seconds (50 ms), meaning that if the blink start times of the left and right eyes are separated by less than 50 ms, and the blink stop times of the left and right eyes are separated by less than 50 ms, then the blink will be merged into a singlemne.Annotations
.- verbosebool |
str
|int
|None
Control verbosity of the logging output. If
None
, use the default verbosity level. See the logging documentation andmne.verbose()
for details. Should only be passed as a keyword argument.
- Returns:
- rawinstance of
RawEyelink
A Raw object containing eyetracker data.
- rawinstance of
See also
mne.io.Raw
Documentation of attribute and methods.
Notes
It is common for SR Research Eyelink eye trackers to only record data during trials. To avoid frequent data discontinuities and to ensure that the data is continuous so that it can be aligned with EEG and MEG data (if applicable), this reader will preserve the times between recording trials and annotate them with
'BAD_ACQ_SKIP'
.
Examples using mne.io.read_raw_eyelink
#
Importing Data from Eyetracking devices
Working with eye tracker data in MNE-Python
Plotting eye-tracking heatmaps in MNE-Python