Describe the new feature or enhancement

The original Eyelink blink detection algorithm simply parses blinks when pupil becomes indetectable. However, this approach ignores the temporal dynamics of the opening/closing of eyelids. One existing solution is mne.preprocessing.eyetracking.interpolate_blinks which interpolates data in a fixed-duration buffer zone before and after a blink. The algorithm is effective but not adaptive enough to account for individual and trial-to-trial differences.

Instead, a new method (https://doi.org/10.3758/s13428-017-1008-1) makes use of pupilometry noise and sharp slopes that precede and follow the blinks. The Python code is publically available at https://osf.io/jyz43/ thanks to @titoghose. The OSF repo has no indicated license, but we hope that's OK @titoghose if we adapt the code for MNE.

@AlexLepauvre and I can make a quick adaptation of the original code and make the algorithm available to everyone using MNE to analyze pupilometry data.

Describe your proposed implementation

New function

mne.preprocessing.eyetracking.find_blinks_by_slope(
        raw,
        concat=False,
        concat_gap_interval=100,
        description='BAD_blink'
        )

raw is the Raw object storing eye-tracking data that at least has one channel named 'pupil_*'.
concat controls whether to concat nearby blinks.
'concat_gap_interval' determines the interval between successive missing samples/blinks to concatenate.
description determines the annotation description for the newly detected blinks. If set to 'BAD_blink' will also replace the existing 'BAD_blink' annotations.

The function will try to look for all 'pupil_*' channels and perform the detection. The detected blink events will be saved as annotations with ch_names set to the channel they were detected from.

Describe possible alternatives

mne.preprocessing.eyetracking.interpolate_blinks, but it lacks the flexibility to detect individual blinks.

Additional context

No response