What makes a microsaccade? A review of 70 years research prompts a new detection method

Hauperich, Anna-Katharina; Young, Laura K.; Smithson, Hannah E.

doi:10.16910/jemr.12.6.13

Cited by 13 publications

(24 citation statements)

References 75 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The largest observed difference between two days was 0.7 arcmin, roughly the diameter of two foveal cones. It is a very small difference, considering that a single microsaccade, for example, can move the stimulus across tens of foveal cones ( Hauperich, Young, & Smithson, 2020 ; Nyström, Hansen, Andersson, & Hooge, 2016 ). Indeed, although Bowers, Gautier, Lin, and Roorda (2021) reported differences in fixational eye movements for different tasks, they did not find differences in the PRL for those tasks.…”

Section: Discussionmentioning

confidence: 99%

The retinal and perceived locus of fixation in the human visual system

et al. 2021

View full text Add to dashboard Cite

Due to the dramatic difference in spatial resolution between the central fovea and the surrounding retinal regions, accurate fixation on important objects is critical for humans. It is known that the preferred retinal location (PRL) for fixation of healthy human observers rarely coincides with the retinal location with the highest cone density. It is not currently known, however, whether the PRL is consistent within an observer or is subject to fluctuations and, moreover, whether observers’ subjective fixation location coincides with the PRL. We studied whether the PRL changes between days. We used an adaptive optics scanning laser ophthalmoscope to project a Maltese cross fixation target on an observer's retina and continuously imaged the exact retinal location of the target. We found that observers consistently use the same PRL across days, regardless of how much the PRL is displaced from the cone density peak location. We then showed observers small stimuli near the visual field location on which they fixated, and the observers judged whether or not the stimuli appeared in fixation. Observers’ precision in this task approached that of fixation itself. Observers based their judgment on both the visual scene coordinates and the retinal location of the stimuli. We conclude that the PRL in a normally functioning visual system is fixed, and observers use it as a reference point in judging stimulus locations.

show abstract

Section: Discussionmentioning

confidence: 99%

The retinal and perceived locus of fixation in the human visual system

et al. 2021

View full text Add to dashboard Cite

show abstract

“…Of the vast number of algorithms available, each has specific strengths and capabilities, as well as varying degrees of complexity and accuracy. Whereas earlier detection algorithms relied on absolute or relative thresholds for position, velocity, or acceleration [e.g., 40, 53, 54], more recent algorithms involve covariance [55], binocular correlation [24], particle filters [56], Bayesian inference [57], or Markov models, Kalman filters, and spanning trees [58, 59], to name just a few. Andersson and colleagues [21] have evaluated the performance of ten different algorithms, among which two algorithms were specifically dedicated to the detection of PSOs [5, 22].…”

Section: Methodsmentioning

confidence: 99%

“…We thank Jan Drewes, Guillaume Masson, and Anna Montagnini for sharing their co-registered search-coil and video-based eye tracking data [39]. Furthermore, we thank Markus Nyström and Richard Andersson [21–23] and Anna-Katharina Hauperich and colleagues [24] for making their annotated datasets publicly available, as well as Ralf Engbert, Petra Sinn, Konstantin Mergenthaler, and Hans Trukenbrod for their R implementation of the Microsaccade Toolbox (http://read.psych.uni-potsdan.de/attachnents/article/140/MS_Toolbox_R.zip). We thank Antonio Sánchez Garcia for his help with the Phantom high speed camera and Sven Ohl and Wiebke Nörenberg for volunteering to provide feedback on an earlier version of the chapter.…”

Section: Acknowledgementsmentioning

confidence: 99%

“…The constant ppd (pixels per degree) transforms eye position in pixel coordinates into degrees of visual angle. b Position, velocity, and acceleration profiles of three saccades of different sizes, of which one is a microsaccade [from a hand-labeled data set, 24]. Saccade amplitudes and durations may be computed based on different offset definitions, namely the onset or offset of the post-saccadic oscillation ( D PSO or D Full ), respectively.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Definition, modeling and detection of saccades in the face of post-saccadic oscillations

Schweitzer

Rolfs²

2021

Preprint

View full text Add to dashboard Cite

When analyzing eye tracking data, one of the central tasks is the detection of saccades. Although many automatic saccade detection algorithms exist, the field still debates how to deal with brief periods of instability around saccade offset, so-called post-saccadic oscillations (PSOs), which are especially prominent in today's widely used video-based eye tracking techniques. There is good evidence that PSOs are caused by inertial forces that act on the elastic components of the eye, such as the iris or the lens. As this relative movement can greatly distort estimates of saccade metrics, especially saccade duration and peak velocity, video-based eye tracking has recurrently been considered unsuitable for measuring saccade kinematics. In this chapter, we review recent biophysical models that describe the relationship between pupil motion and eyeball motion. We found that these models were well capable of accurately reproducing saccade trajectories and implemented a framework for the simulation of saccades, PSOs, and fixations, which can be used - just like datasets hand-labelled by human experts - to evaluate detection algorithms and train statistical models. Moreover, as only pupil and corneal-reflection signals are observable in video-based eye tracking, one may also be able to use these models to predict the unobservable motion of the eyeball. Testing these predictions by analyzing saccade data that was registered with video-based and search-coil eye tracking techniques revealed strong relationships between the two types of measurements, especially when saccade offset is defined as the onset of the PSO. To enable eye tracking researchers to make use of this definition, we present and evaluate two novel algorithms - one based on eye-movement direction inversion, one based on linear classifiers previously trained on simulation data. These algorithms allow for the detection of PSO onset with high fidelity. Even though PSOs may still pose problems for a range of eye tracking applications, the techniques described here may help to alleviate these.

show abstract

“…Microsaccades are modeled as a ballistic motion with the peak velocity proportional to the amplitude of the displacement in log space. The amplitude of each microsaccade is randomly selected from a normal distribution (default: mean of 30 arcmin and a standard deviation of 3 arcmin 75 ). In the model, the occurrences of microsaccades are pre-determined by randomly selecting time intervals from a normal distribution (default: mean of 1.5 seconds and a standard deviation of 0.25 seconds).…”

Section: Methods and Technical Detailmentioning

confidence: 99%

ERICA: Emulated Retinal Image CApture - A tool for testing, training and validating retinal image processing methods

Young

Smithson

2021

Preprint

View full text Add to dashboard Cite

High resolution retinal imaging systems, such as adaptive optics scanning laser ophthalmoscopes (AOSLO), are increasingly being used for clinical and fundamental studies in neuroscience. These systems offer unprecedented spatial and temporal resolution of retinal structures in vivo. However, a major challenge is the development of robust and automated methods for processing and analysing these images. We present ERICA (Emulated Retinal Image CApture), a simulation tool that generates realistic synthetic images of the human cone mosaic, mimicking images that would be captured by an AOSLO, with specified image quality and with corresponding ground truth data. The simulation includes a self-organising mosaic of photoreceptors, the eye movements an observer might make during image capture, and data capture through a real system incorporating diffraction, residual optical aberrations and noise. The retinal photoreceptor mosaics generated by ERICA have a similar packing geometry to human retina, as determined by expert labelling of AOSLO images of real eyes. In the current implementation ERICA outputs convincingly realistic en face images of the cone photoreceptor mosaic but extensions to other imaging modalities and structures are also discussed. These images and associated ground-truth data can be used to develop, test and validate image processing and analysis algorithms or to train and validate machine learning approaches. The use of synthetic images has the advantage that neither access to an imaging system, nor to human participants is necessary for development.

show abstract

What makes a microsaccade? A review of 70 years research prompts a new detection method

Cited by 13 publications

References 75 publications

The retinal and perceived locus of fixation in the human visual system

The retinal and perceived locus of fixation in the human visual system

Definition, modeling and detection of saccades in the face of post-saccadic oscillations

ERICA: Emulated Retinal Image CApture - A tool for testing, training and validating retinal image processing methods

Contact Info

Product

Resources

About