Improving the adaptive event detection algorithm of Nyström and Holmquist for noisy data

Detecting eye tracking events such as fixations and saccades is one of the first important steps in eye tracking research. The adaptive algorithm by Nyström and Holmqvist [2010] estimates thresholds by computing a "peak velocity detection threshold" (PT) that depends on the data's noise level. However, too high thresholds might result in only few detected saccades. The present study investigated a solution with an upper bound for PT. Fixations and saccades were computed for N = 68 participants who performed a fixation task and a visual detection test. The original version of the algorithm was compared with five versions utilizing upper bounds for PT (ranging from 100deg/sec to 300deg/sec) according to three predefined criteria. These criteria suggest an optimal upper bound at 200deg/sec for the utilized static and simple structured testing materials.