Lee Friedman

Lee Friedman

Texas State University

H-index: 47

North America-United States

About Lee Friedman

Lee Friedman, With an exceptional h-index of 47 and a recent h-index of 19 (since 2020), a distinguished researcher at Texas State University, specializes in the field of neuroscience, eye movements.

His recent articles reflect a diverse array of research interests and contributions to the field:

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

Analysis of Embeddings Learned by End-to-End Machine Learning Eye Movement-driven Biometrics Pipeline

What can entropy metrics tell us about the characteristics of ocular fixation trajectories?

Filtering Eye-Tracking Data From an EyeLink 1000: Comparing Heuristic, Savitzky-Golay, IIR and FIR Digital Filters

Checking the Statistical Assumptions Underlying the Application of the Standard Deviation and RMS Error to Eye-Movement Time Series: A Comparison between Human and Artificial Eyes

Factors affecting inter-rater agreement in human classification of eye movements: a comparison of three datasets

The Importance of the Signal/Noise Distinction for Eye Movement Biometric Performance

GazeBaseVR, a large-scale, longitudinal, binocular eye-tracking dataset collected in virtual reality

Lee Friedman Information

University

Position

Research Scientist Computer Science

Citations(all)

9580

Citations(since 2020)

1523

Cited By

8535

hIndex(all)

47

hIndex(since 2020)

19

i10Index(all)

84

i10Index(since 2020)

35

Email

University Profile Page

Texas State University

Google Scholar

View Google Scholar Profile

Lee Friedman Skills & Research Interests

neuroscience

eye movements

Top articles of Lee Friedman

Title

Journal

Author(s)

Publication Date

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

arXiv preprint arXiv:2403.07210

Samantha Aziz

Dillon J Lohr

Lee Friedman

Oleg Komogortsev

2024/3/11

Analysis of Embeddings Learned by End-to-End Machine Learning Eye Movement-driven Biometrics Pipeline

arXiv preprint arXiv:2402.16399

Mehedi Hasan Raju

Lee Friedman

Dillon J Lohr

Oleg V Komogortsev

2024/2/26

What can entropy metrics tell us about the characteristics of ocular fixation trajectories?

Plos one

Kateryna Melnyk

Lee Friedman

Oleg V Komogortsev

2024/1/2

Filtering Eye-Tracking Data From an EyeLink 1000: Comparing Heuristic, Savitzky-Golay, IIR and FIR Digital Filters

arXiv preprint arXiv:2303.02134

Mehedi H Raju

Lee Friedman

Troy M Bouman

Oleg V Komogortsev

2023/3/3

Checking the Statistical Assumptions Underlying the Application of the Standard Deviation and RMS Error to Eye-Movement Time Series: A Comparison between Human and Artificial Eyes

arXiv preprint arXiv:2303.06004

Lee Friedman

Timothy Hanson

Hal S Stern

Oleg V Komogortsev

2023/2/13

Factors affecting inter-rater agreement in human classification of eye movements: a comparison of three datasets

Behavior Research Methods

Lee Friedman

Vladyslav Prokopenko

Shagen Djanian

Dmytro Katrychuk

Oleg V Komogortsev

2023/1

The Importance of the Signal/Noise Distinction for Eye Movement Biometric Performance

arXiv preprint arXiv:2305.04413

Mehedi H Raju

Lee Friedman

Dillon Lohr

Oleg Komogortsev

2023/5/8

GazeBaseVR, a large-scale, longitudinal, binocular eye-tracking dataset collected in virtual reality

Scientific Data

Dillon Lohr

Samantha Aziz

Lee Friedman

Oleg V Komogortsev

2023/3/30

Determining which sine wave frequencies correspond to signal and which correspond to noise in eye-tracking time-series

Mehedi H Raju

Lee Friedman

Troy M Bouman

Oleg Komogortsev

2022

FKM

Lee Friedman

2022

What Can Entropy Metrics Tell Us About Types of Fixations during Video Viewing?

Kateryna Melnyk

Lee Friedman

Oleg Komogortsev

2022

Biometric performance as a function of gallery size

Applied Sciences

Lee Friedman

Hal Stern

Vladyslav Prokopenko

Shagen Djanian

Henry Griffith

...

2022/11/3

Proposals to Replace the Standard Deviation and the RMS-Sample-to-Sample as Measures of Precision

Lee Friedman

2022

A Method for the Detection of Poorly-Formed or Misclassified Saccades: A case study using the GazeCom Dataset

Lee Friedman

Shagen Djanian

Oleg Komogortsev

2022

Analysis of Heuristic and Digital Filters as Applied to Video-oculography Signals

arXiv preprint arXiv:2209.07657

Mehedi H Raju

Lee Friedman

Troy M Bouman

Oleg V Komogortsev

2022/9/16

Multimodality during fixation–Part II: Evidence for multimodality in spatial precision-related distributions and impact on precision estimates

Journal of Eye Movement Research

Lee Friedman

Timothy Hanson

Oleg V Komogortsev

2021

Angular offset distributions during fixation are, more often than not, multimodal

Journal of eye movement research

Lee Friedman

Dillon Lohr

Timothy Hanson

Oleg V Komogortsev

2021

Rules and Guidelines for Manual Classification of Fixations, Saccades, PSEs and Other Events in High Quality Eye Movement Recordings During Reading

Lee Friedman

Vladyslav Prokopenko

Shagen Djanian

Dmytro Katrychuk

Oleg Komogortsev

2021

Brief Communication: A Re-Examination of the Eye Movement Data used by Hooge et al (2018)[" Is human classification by experienced untrained observers a gold standard in …

Lee Friedman

2020

Why temporal persistence of biometric features, as assessed by the intraclass correlation coefficient, is so valuable for classification performance

Sensors

Lee Friedman

Hal S Stern

Larry R Price

Oleg V Komogortsev

2020/8/14

See List of Professors in Lee Friedman University(Texas State University)