16 citations · 65 across the 20 of their papers we have counts for
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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…
Filtering Eye-Tracking Data From an EyeLink 1000: Comparing Heuristic, Savitzky-Golay, IIR and FIR Digital Filters
Mehedi H. Raju, Lee Friedman, Troy M. Bouman +1
In a previous report (Raju et al.,2023) we concluded that, if the goal was to preserve events such as saccades, microsaccades, and smooth pursuit in eye-tracking recordings, data w…
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
Lee Friedman, Timothy Hanson, Hal S. Stern +1
Spatial precision is often measured using the standard deviation (SD) of the eye position signal or the RMS of the sample-to-sample differences (StoS) signal during fixation. As bo…
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 +1
The Fourier theorem states that any time-series can be decomposed into a set of sinusoidal frequencies, each with its own phase and amplitude. The literature suggests that some fre…