Automatic eye fixations identification based on analysis of variance and covariance

Veneri, Giacomo; Piu, Pietro; Rosini, Francesca; Federighi, Pamela; Federico, Antonio; Rufa, Alessandra

doi:10.1016/j.patrec.2011.06.012

Cited by 24 publications

(11 citation statements)

References 19 publications

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“…Veneri et al ( 2011 ) designed a fixation-labeling algorithm (hereafter, C-DT ) with a search rule based on the covariance of the horizontal and vertical eye position signals. The algorithm labels gaze samples as belonging to a fixation when an F test indicates that the variances of the horizontal and vertical eye positions are equal.…”

Section: Methodsmentioning

confidence: 99%

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

et al. 2016

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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).

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Section: Methodsmentioning

confidence: 99%

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

et al. 2016

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“…The Fixation Dispersion Algorithm based on Covariance (CDT) by Veneri et al (2011) is an improvement of the fixation dispersion algorithm based on F-tests (FDT) previously developed by the same authors (Veneri et al, 2010). The improvement consists in complementing their previous, F-testbased, algorithm with co-variance calculations on the x-and ycoordinates of the gaze.…”

Section: Current Algorithms For Eye-movement Classificationmentioning

confidence: 99%

One algorithm to rule them all? An evaluation and discussion of ten eye movement event-detection algorithms

et al. 2016

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Almost all eye-movement researchers use algorithms to parse raw data and detect distinct types of eye movement events, such as fixations, saccades, and pursuit, and then base their results on these. Surprisingly, these algorithms are rarely evaluated. We evaluated the classifications of ten eyemovement event detection algorithms, on data from an SMI HiSpeed 1250 system, and compared them to manual ratings of two human experts. The evaluation focused on fixations, saccades, and post-saccadic oscillations. The evaluation used both event duration parameters, and sample-by-sample comparisons to rank the algorithms. The resulting event durations varied substantially as a function of what algorithm was used. This evaluation differed from previous evaluations by considering a relatively large set of algorithms, multiple events, and data from both static and dynamic stimuli. The main conclusion is that current detectors of only fixations and saccades work reasonably well for static stimuli, but barely better than chance for dynamic stimuli. Differing results across evaluation methods make it difficult to select one winner for fixation detection. For saccade detection, however, the algorithm by Larsson, Nyström and Stridh (IEEE Transaction on Biomedical Engineering, 60(9): [2484][2485][2486][2487][2488][2489][2490][2491][2492][2493]2013) outperforms all algorithms in data from both static and dynamic stimuli. The data also show how improperly selected algorithms applied to dynamic data misestimate fixation and saccade properties.

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“…dt 2 acceleration threshold I-AT → fix (Behrens & Weiss, 1992;Behrens, MacKeben, & Schröder-Preikschat, 2010) (Wyatt, 1998), (Matsuoka & Harato, 1983, in Japanese) fixation pickers dispersion threshold I-DT → fix (Mason, 1976;Kliegl & Olson, 1981) dispersion and duration thresholds I-DDT → fix (Widdel, 1984;Nodine, Kundel, Toto, & Krupinski, 1992;Manor & Gordon, 2003;Krassanakis, Filippakopoulou, & Nakos, 2014) These are taken from other disciplines like • Signal processing -Finite impulse response filter (Tole & Young, 1981) -Cumulative sum (CUSUM) (Olsson, 2007;Tobii, 2014;Gustafsson, 2000) • Statistics -F-test and correlation (Veneri et al, 2010(Veneri et al, , 2011Veneri, 2013) -Gap-statistics (Mould et al, 2012) • Stochastic processes and time series analysis -Auto-regressive processes and wavelet analysis (Duchowski, 1998 (Shelhamer, 1998;Shelhamer & Zalewski, 2001) As of now threshold based methods are common standard. Probabilistic methods are promising candidates inasmuch as they offer the possibility to implement an online learning algorithm to adjust to changing viewing behavior.…”

Section: Range Of Advanced Methodsmentioning

confidence: 99%

Topology for gaze analyses - Raw data segmentation

Hein

Zangemeister²

2017

JEMR

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Recent years have witnessed a remarkable growth in the way mathematics, informatics, and computer science can process data. In disciplines such as machine learning, pattern recognition, computer vision, computational neurology, molecular biology, information retrieval, etc., many new methods have been developed to cope with the ever increasing amount and complexity of the data. These new methods offer interesting possibilities for processing, classifying and interpreting eye-tracking data. The present paper exemplifies the application of topological arguments to improve the evaluation of eye-tracking data. The task of classifying raw eye-tracking data into saccades and fixations, with a single, simple as well as intuitive argument, described as coherence of spacetime, is discussed, and the hierarchical ordering of the fixations into dwells is shown. The method, namely identification by topological characteristics (ITop), is parameter-free and needs no pre-processing and post-processing of the raw data. The general and robust topological argument is easy to expand into complexsettings of higher visual tasks, making it possible to identify visual strategies. As supplementary file an interactive demonstration of the method can be downloaded,

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Automatic eye fixations identification based on analysis of variance and covariance

Cited by 24 publications

References 19 publications

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

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

One algorithm to rule them all? An evaluation and discussion of ten eye movement event-detection algorithms

Topology for gaze analyses - Raw data segmentation

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