activity
20172025
most citedFiltering Eye-Tracking Data From an EyeLink 1000: Comparing Heuristic, Savitzky-Golay, IIR and FIR Digital Filters

16 citations · 65 across the 20 of their papers we have counts for

collaborators

20 papers

cs.HC2025

Gaze Prediction as Time-Series Forecasting for Virtual Reality Applications: Quantifying Performance Variability and Extreme-Case Errors

Kateryna Melnyk, Lee Friedman, Oleg Komogortsev

Gaze prediction is essential for addressing motion-to-photon latency and ensuring seamless foveated rendering in Virtual Reality. The reliability of gaze forecasting is highly sens…

cs.HC2025

Gaze Prediction as a Function of Eye Movement Type and Individual Differences

Kateryna Melnyk, Lee Friedman, Dmytro Katrychuk +1

Eye movement prediction is a promising area of research with the potential to improve performance and the user experience of systems based on eye-tracking technology. In this study…

q-bio.NC2024★ 1 cited

Evidence for five types of fixation during a random saccade eye tracking task: Implications for the study of oculomotor fatigue

Lee Friedman, Oleg V. Komogortsev

Our interest was to evaluate changes in fixation duration as a function of time-on-task (TOT) during a random saccade task. We employed a large, publicly available dataset. The fre…

cs.HC2024

Evaluation of Eye Tracking Signal Quality for Virtual Reality Applications: A Case Study in the Meta Quest Pro

Samantha Aziz, Dillon J Lohr, Lee Friedman +1

We present an extensive, in-depth analysis of the eye tracking capabilities of the Meta Quest Pro virtual reality headset using a dataset of eye movement recordings collected from…

cs.CV2024★ 1 cited

Temporal Persistence and Intercorrelation of Embeddings Learned by an End-to-End Deep Learning Eye Movement-driven Biometrics Pipeline

Mehedi Hasan Raju, Lee Friedman, Dillon J Lohr +1

What qualities make a feature useful for biometric performance? In prior research, pre-dating the advent of deep learning (DL) approaches to biometric analysis, a strong relationsh…

cs.HC2023★ 12 cited

Signal vs Noise in Eye-tracking Data: Biometric Implications and Identity Information Across Frequencies

Mehedi H. Raju, Lee Friedman, Dillon Lohr +1

Prior research states that frequencies below 75 Hz in eye-tracking data represent the primary eye movement termed ``signal'' while those above 75 Hz are deemed ``noise''. This stud…