Automatic drift calibration for EOG-based gaze input interface.

Area of Science:

• Biomedical Engineering
• Neuroscience
• Human-Computer Interaction

Background:

• DC-coupled electrooculogram (EOG) systems are crucial for gaze tracking.
• Signal drift in EOG can degrade the accuracy of long-term gaze input interfaces.
• Existing calibration methods may require explicit user actions, limiting continuous use.

Purpose of the Study:

• To propose a novel drift calibration technique for DC-coupled EOG systems.
• To enable accurate estimation of absolute eye angle using EOG signals.
• To facilitate robust long-term gaze tracking for applications like brain-computer interfaces.

Main Methods:

• A non-linear relationship between EOG signals and eye angle is assumed.
• Absolute eye angle is estimated by analyzing EOG signal differences during saccades.
• Drift is automatically calibrated during each saccade, requiring no explicit user input.

Main Results:

• The proposed technique successfully calibrates drift during natural saccades.
• Accurate estimation of horizontal absolute eye angle was achieved.
• The experimental results demonstrated an average error of approximately 5° for horizontal eye angle estimation.

Conclusions:

• The developed drift calibration method is effective for DC-coupled EOG systems.
• This technique significantly improves the suitability of EOG for long-term gaze input applications.
• It offers a user-independent and continuous calibration solution for enhanced gaze tracking accuracy.