A Fusion Algorithm Based on a Constant Velocity Model for Improving the Measurement of Saccade Parameters with Electrooculography
This repository contains the implementation of the algorithm proposed in the paper:
@article{gunawardane2024fusion, title={A fusion algorithm based on a constant velocity model for improving the measurement of saccade parameters with electrooculography}, author={Gunawardane, Palpolage Don Shehan Hiroshan and MacNeil, Raymond Robert and Zhao, Leo and Enns, James Theodore and de Silva, Clarence Wilfred and Chiao, Mu}, journal={Sensors}, volume={24}, number={2}, year={2024}, publisher={MDPI} }
This project provides a robust algorithm to improve the accuracy of saccade parameter measurement using electrooculography (EOG) signals. The method fuses two estimation approaches — a regression-based technique and a velocity-threshold-based method — using a constant velocity motion model, yielding improved estimates of key saccade parameters such as amplitude, velocity, and duration.
- Implements a fusion algorithm based on a constant velocity motion model
- Enhances the accuracy and reliability of saccade detection using EOG
- Provides tools for:
- Preprocessing raw EOG signals
- Detecting saccades
- Estimating saccade parameters
- Evaluating performance against ground truth