A Fusion Algorithm Based on a Constant Velocity Model for Improving the Measurement of Saccade Parameters with...

Area of Science:

• Biomedical Engineering
• Neuroscience
• Signal Processing

Background:

• Electrooculography (EOG) is crucial for tracking saccadic eye movements in medical diagnostics and human-computer interaction.
• EOG signals are susceptible to noise from various sources, including electrical interference and muscle activity, complicating analysis.
• Existing denoising methods often alter essential signal characteristics or are too complex for real-time application.

Purpose of the Study:

• To develop a real-time denoising method for electrooculography (EOG) signals.
• To preserve the integrity of EOG saccade signal parameters during noise reduction.
• To offer an efficient and accurate alternative to traditional EOG signal processing techniques.

Main Methods:

• A physics-based, model-oriented approach utilizing a constant velocity model for EOG signal denoising.
• The method assumes a consistent rate of change in cornea-retinal potential during saccadic movements.
• Real-time processing capabilities were prioritized in the method's design.

Main Results:

• The proposed constant velocity model method demonstrated superior preservation of EOG saccade signals.
• Signal preservation was enhanced by up to 29% compared to alternative denoising techniques.
• The method effectively mitigates noise interference while maintaining signal fidelity.

Conclusions:

• The novel real-time denoising method offers a significant advancement in EOG signal processing.
• This approach effectively preserves key saccadic eye movement signal characteristics.
• The technique provides a more robust and accurate solution for EOG-based applications.