Microsaccades Tracking by Secondary Speckle Pattern Analysis.

Area of Science:

• Ophthalmology and Vision Science
• Biomedical Engineering
• Optics and Photonics

Background:

• Microsaccades are small, rapid eye movements crucial for visual processing.
• Current video-based eye trackers often rely on pupil detection, limiting their application.
• A non-contact, pupil-independent method for microsaccade detection is needed.

Purpose of the Study:

• To propose and validate a novel, pupil-independent microsaccade tracking technique.
• To introduce a non-contact laser-based photonic system for eye movement detection.
• To compare the performance of speckle-based tracking with traditional video-based methods.

Main Methods:

• Developed a speckle-based tracker (SBT) using laser scattering from the eye's sclera.
• Simultaneously recorded eye movement data using SBT and a video-based eye tracker (Eyelink).
• Analyzed data using the Engbert and Kliegl (E&K) method and machine learning detection (MLD).

Main Results:

• The SBT detected 93% of microsaccades identified by the Eyelink with the E&K method.
• Machine learning detection (MLD) achieved a precision of 86% with the SBT data.
• Demonstrated a proof of concept for microsaccade detection using speckle pattern analysis.

Conclusions:

• Speckle-based eye tracking offers a viable, pupil-independent alternative for microsaccade detection.
• This photonic system shows potential for improved measurement of tiny eye movements.
• The findings suggest a significant advancement over conventional video-based eye tracking techniques.