A study of artificial eyes for the measurement of precision in eye-trackers

Wang, Dong; Mulvey, Fiona; Pelz, Jeff B.; Holmqvist, Kenneth

doi:10.3758/s13428-016-0755-8

Cited by 55 publications

(55 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…More specifically, for each recording in each data set, we added a white Gaussian noise signal generated using the Box-Muller method (Thomas et al, 2007), separately for the horizontal and vertical gaze components. The choice to use white Gaussian noise to model measurement noise is motivated by recent findings that when recording from an artificial eye (which has no oculomotor noise), eye trackers generally produce white noise (Coey et al, 2012;Wang et al, 2016a). Both studies showed that the power spectrum is in this case constant and independent of frequency.…”

Section: Data Augmentationmentioning

confidence: 99%

Using machine learning to detect events in eye-tracking data

et al. 2017

Self Cite

View full text Add to dashboard Cite

Event detection is a challenging stage in eye movement data analysis. A major drawback of current event detection methods is that parameters have to be adjusted based on eye movement data quality. Here we show that a fully automated classification of raw gaze samples as belonging to fixations, saccades, or other oculomotor events can be achieved using a machine-learning approach. Any already manually or algorithmically detected events can be used to train a classifier to produce similar classification of other data without the need for a user to set parameters. In this study, we explore the application of random forest machine-learning technique for the detection of fixations, saccades, and post-saccadic oscillations (PSOs). In an effort to show practical utility of the proposed method to the applications that employ eye movement classification algorithms, we provide an example where the method is employed in an eye movement-driven biometric application. We conclude that machine-learning techniques lead to superior detection compared to current state-of-the-art event detection algorithms and can reach the performance of manual coding.

show abstract

Section: Data Augmentationmentioning

confidence: 99%

Using machine learning to detect events in eye-tracking data

et al. 2017

Self Cite

View full text Add to dashboard Cite

show abstract

“…In biological systems, the PSD is often inversely proportional to the frequency, and as such characterized by , where f is the frequency and α is a scaling exponent. Such scaling relationships have also been observed in human eye movements, with scaling exponents between –0.6 and –2 (Coey, Wallot, Richardson, & van Orden, 2012 ; Findlay, 1971 ; Wang, Mulvey, Pelz, & Holmqvist, 2016 ). The α value was determined by the slope of a straight line fitted to the PSD in log–log space.…”

Section: Appendix Amentioning

confidence: 62%

Noise-robust fixation detection in eye movement data: Identification by two-means clustering (I2MC)

et al. 2016

View full text Add to dashboard Cite

Eye-tracking research in infants and older children has gained a lot of momentum over the last decades. Although eye-tracking research in these participant groups has become easier with the advance of the remote eye-tracker, this often comes at the cost of poorer data quality than in research with well-trained adults (Hessels, Andersson, Hooge, Nyström, & Kemner Infancy, 20, 601–633, 2015; Wass, Forssman, & Leppänen Infancy, 19, 427–460, 2014). Current fixation detection algorithms are not built for data from infants and young children. As a result, some researchers have even turned to hand correction of fixation detections (Saez de Urabain, Johnson, & Smith Behavior Research Methods, 47, 53–72, 2015). Here we introduce a fixation detection algorithm—identification by two-means clustering (I2MC)—built specifically for data across a wide range of noise levels and when periods of data loss may occur. We evaluated the I2MC algorithm against seven state-of-the-art event detection algorithms, and report that the I2MC algorithm’s output is the most robust to high noise and data loss levels. The algorithm is automatic, works offline, and is suitable for eye-tracking data recorded with remote or tower-mounted eye-trackers using static stimuli. In addition to application of the I2MC algorithm in eye-tracking research with infants, school children, and certain patient groups, the I2MC algorithm also may be useful when the noise and data loss levels are markedly different between trials, participants, or time points (e.g., longitudinal research).

show abstract

“…However, their performance in detecting miniature fixational eye movements has been found to be comparable to that of the invasive search-coil technique, considered the gold standard in this field (McCamy et al, 2015). Compared with other commercial devices, including the dual-Purkinje-image eye tracker, the EyeLink device has been found to be among the highest in tracking precision (Wang, Mulvey, Pelz, & Holmqvist, 2016).…”

Section: Eye Trackingmentioning

confidence: 99%

Transient and sustained effects of stimulus properties on the generation of microsaccades

2019

View full text Add to dashboard Cite

Saccades shift the gaze rapidly every few hundred milliseconds from one fixated location to the next, producing a flow of visual input into the visual system even in the absence of changes in the environment. During fixation, small saccades called microsaccades are produced 1-3 times per second, generating a flow of visual input. The characteristics of this visual flow are determined by the timings of the saccades and by the characteristics of the visual stimuli on which they are performed. Previous models of microsaccade generation have accounted for the effects of external stimulation on the production of microsaccades, but they have not considered the effects of the prolonged background stimulus on which microsaccades are performed. The effects of this stimulus on the process of microsaccade generation could be sustained, following its prolonged presentation, or transient, through the visual transients produced by the microsaccades themselves. In four experiments, we varied the properties of the constant displays and examined the resulting modulation of microsaccade properties: their sizes, their timings, and the correlations between properties of consecutive microsaccades. Findings show that displays of higher spatial frequency and contrast produce smaller microsaccades and longer minimal intervals between consecutive microsaccades; and smaller microsaccades are followed by smaller and delayed microsaccades. We explain these findings in light of previous models and suggest a conceptual model by which both sustained and transient effects of the stimulus have central roles in determining the generation of microsaccades.

show abstract

A study of artificial eyes for the measurement of precision in eye-trackers

Cited by 55 publications

References 15 publications

Using machine learning to detect events in eye-tracking data

Using machine learning to detect events in eye-tracking data

Noise-robust fixation detection in eye movement data: Identification by two-means clustering (I2MC)

Transient and sustained effects of stimulus properties on the generation of microsaccades

Contact Info

Product

Resources

About