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Eye Drift Signal Analysis Caused by Goggle Slippage in vHIT Measurements: A Signal Processing Perspective
Ha Ngoc Khoan1,2, Le Ky Bien2, Tran Thi Nhan3
1Cyclotron Centre, 108 Military Central Hospital, Hanoi 100000, Vietnam.
Abstract:
This Technical Report presents a quantitative signal processing approach to analyze and correct eye drift during vestibulo-ocular reflex (VOR) measurements using the video Head Impulse Test (vHIT). The objective is to determine the extent of drift caused by goggle slippage-a technical artifact that can distort the VOR gain index. A total of 57 impulses were categorized into three protocols: Lateral, LARP, and RALP. For each impulse, peak head velocity and eye drift (estimated from the average velocity during the pre- and post-impulse rest periods) were extracted using a custom signal processing pipeline implemented in MATLAB R2020b and Python 3.11 64 bit. Results showed the highest drift in the RALP group (-7.41 deg/s) and the lowest in the LARP group (-3.08 deg/s). The correlation between head velocity and drift was most prominent in the RALP group (r > 0.7), highlighting the impact of stimulation direction on goggle stability. This study proposes a drift detection method to be integrated into VOR correction algorithms, thereby enhancing gain analysis and saccade detection in automated systems.
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