Describe the new feature or enhancement

hi all,

for the 2022 sprint I suggested to add basic support for eye tracking data to MNE.
these data could be useful for several purposes, from improving data cleaning/IC labelling to all types of new analyses s.a. saccade related potentials.

@drammock suggested to post this issue ahead of time to discuss the topic.
I'd be happy to hear your feedback!

Describe your proposed implementation

there are several steps that I think would be necessary for a minimal implementation.

• deciding how to store these data it in the MNE ecosystem
  e.g. simply as a misc channel type along the other sensor data?
• handling of different sampling frequencies of the devices and missing data
  (as recording periods might only imperfectly overlap)
• basic I/O tools
  loading raw eye track data and/or annotiations (blinks, saccades) from different file formats (at least from text/csv files)
• alignment methods
  e.g. based on common annotations/trigger

a fuller (but still basic) version could also do (parts of) the following:

• allow to identify events from the raw eye track
  most importantly blinks, and saccades (e.g. via the Engbert&Mergenthaler algorithm)
• basic visualization tools

..and then of course there would be a host of specific tools the gaze information might be used for.
this might be beyond the scope of this issue, though.

Describe possible alternatives

..not even opening this box ;)

Additional comments

• a comment related to my other suggestion DOC: new tutorial for using automated preprocessing pipelines / switching back and forth between EEGLAB and MNE-Python #10750 : there is e.g. Olaf Dimigen's EEGLAB toolbox EYE-EEG that can do most of the above described. so if MNE could store eye-track data in a consistent way, it might also make sense to update the import functions
• I know there are several python toolboxes that act as parsers for different eye tracker data formats. it might make sense to check these out