An Effective Algorithm to Analyze the Optokinetic Nystagmus Waveforms from a Low-Cost Eye Tracker

Area of Science:

• Ophthalmology
• Neurology
• Psychology
• Biomedical Engineering

Background:

• Neurological diseases often impact the oculomotor nerve, making eye movement analysis crucial.
• Optokinetic nystagmus (OKN), an involuntary eye movement, is a key area of study for understanding visual-motor responses.
• Current research-grade eye trackers are expensive, limiting accessibility for widespread eye movement research.

Purpose of the Study:

• To develop a cost-effective method for quantitative analysis of OKN eye movements.
• To enable broader application of eye movement research through accessible technology.
• To provide a reliable tool for measuring parameters like mean slow phase velocity (MSPV).

Main Methods:

• Designed an analytical algorithm for quantitative OKN measurement on a low-cost eye tracker.
• Implemented simple conditional filtering for rapid and accurate identification of fast phase (FP) positions.
• Validated the algorithm's effectiveness in analyzing OKN waveforms.

Main Results:

• The algorithm achieves high-precision FP recognition rates.
• Accurate FP identification facilitates subsequent calculation of critical eye movement parameters.
• The method demonstrated faster and superior performance compared to existing approaches.

Conclusions:

• The proposed algorithm offers an effective solution for calculating and analyzing FP positions in OKN.
• This low-cost approach enhances the accessibility of quantitative eye movement analysis.
• The findings support the use of this method as a valuable reference index for various eye conditions.