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Saccade derivative filters and their clinical implications

J D Enderle¹, M B Hallowell

¹Electrical & Systems Engineering Department, University of Connecticut, Storrs 06269-2157, USA.

Biomedical Sciences Instrumentation

|January 1, 1997

Summary

The median derivative filter offers superior velocity estimation from eye position signals compared to linear filters. This nonlinear digital filter accurately captures saccade dynamics, avoiding issues like Gibb's phenomena.

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Area of Science:

Ophthalmology
Biomedical Engineering
Signal Processing

Background:

Accurate estimation of eye movement velocity and acceleration is crucial for understanding visual perception and neurological disorders.
Traditional derivative algorithms can be susceptible to noise and introduce artifacts, impacting signal fidelity.

Purpose of the Study:

To evaluate and compare the accuracy of three derivative algorithms for estimating saccadic eye movement velocity and acceleration.
To identify the most effective algorithm for processing noisy eye position signals.

Main Methods:

Utilized a saccade model with simulated, noise-contaminated eye position data.
Compared estimated velocity and acceleration waveforms against known ground truth for three algorithms: two-point central difference, band-limited, and median derivative filters.

Main Results:

The median derivative filter demonstrated superior performance in estimating velocity compared to the other two algorithms.
This nonlinear filter effectively mitigated noise and avoided Gibb's phenomena, an artifact common in linear filters.

Conclusions:

The median derivative filter is a robust and accurate method for calculating velocity from eye position signals.
Its ease of implementation and superior performance make it a valuable tool in eye-tracking research and clinical applications.

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