Saccade detection using a particle filter

Several experiments have shown that smooth pursuit and saccades interact while tracking an object moving across the visual scene. It was proposed two decades ago that the amplitude of saccades triggered during smooth pursuit ('catch-up saccades') were corrected by a delayed sensory signal to account for the ongoing target displacement during catch-up saccades. However, recent studies used targets with non-constant velocity profiles and suggested that the correction of catch-up saccade amplitude must be done through an internal model of target motion. It is widely accepted that an internal model of target motion is also used by the central nervous system (CNS) to cancel inherent delays between visual input and smooth pursuit motor output, ensuring accurate tracking of moving targets. Our study proposes a new paradigm in which the target switches unexpectedly from one target with a non-constant periodic velocity profile to another with a non-constant aperiodic velocity profile. Our results confirm the hypothesis that the CNS uses an internal model of target motion to correct catch-up saccade amplitude. In addition, we reconcile the sensory delayed and the internal model of target motion hypotheses and show that a common internal model of target motion is shared within the CNS to control smooth pursuit and to correct catch-up saccade amplitude.

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“…Saccades were detected using a particle filter (Daye & Optican, ). Each saccade was visually inspected.…”

Section: Methodsmentioning

confidence: 99%

Switching between two targets with non‐constant velocity profiles reveals shared internal model of target motion

Hainque

Apartis

Daye

2016

Eur J of Neuroscience

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“…The median absolute deviation is a robust estimate of the standard deviation, and can take into account non-normal distributions via a scale factor (Leys et al, 2013;Wilcox, 2012). Previous gaze research has used this measure to detect outliers and clean data (Rütsche, Baumann, Jiang, & Mojon, 2006), while other research has recommended using a median filtering to reduce the influence of noise in general (Daye & Optican, 2014;Liston, Krukowski, & Stone, 2013). We propose using MAD as a threshold estimator in and of itself.…”

Section: Mad Saccade: Statistically Robust Saccade Threshold Estimationmentioning

confidence: 99%

MAD saccade: statistically robust saccade threshold estimation

Voloh¹,

Watson²,

König³

et al. 2019

Preprint

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Saccade detection is a critical step in the analysis of gaze data. A common method for saccade detection is to use a simple threshold for velocity or acceleration values, which is typically estimated from the data using the mean and standard deviation. However, this method has the downside of being influenced by the very signal it is trying to detect, the outlying velocities or accelerations that occur during saccades. We propose instead to use the median absolute deviation (MAD), a robust estimator of the standard deviation that is not influenced by outliers. We modify an algorithm proposed by Nyström and colleagues, and quantify saccade detection performance in both simulated and human data. Our modified algorithm shows a significant and marked improvement in saccade detection, showing both more true positives and less false negatives. We conclude that robust estimators can be widely adopted in other common, automatic gaze classification algorithms due to their ease of implementation.

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“…Also, several SDMs have been developed to handle the noise in the tracked eye-gaze positions. Particle filtering, with a small number of effective particles, was proposed to detect (micro)saccades (Daye & Optican, 2014). More recently, the hidden Markov model was proposed in Mihali, van Opheusden, and Ma (2017), and the Bayesian Inference Method (BIM) was derived accordingly as the microsaccade detector.…”

Section: Introductionmentioning

confidence: 99%

The Neyman Pearson detection of microsaccades with maximum likelihood estimation of parameters

2019

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# Despite the fact that the velocity threshold method is widely applied, the detection of microsaccades continues to be a challenging problem, due to gaze-tracking inaccuracy and the transient nature of microsaccades. Important parameters associated with a saccadic event, e.g., saccade duration, amplitude, and maximum velocity, are sometimes imprecisely estimated, which may lead to biases in inferring the roles of microsaccades in perception and cognition. To overcome the biases and have a better detection algorithm for microsaccades, we propose a novel statistical model for the tracked gaze positions during eye fixations. In this model, we incorporate a parametrization that has been previously applied to model saccades, which allows us to veridically capture the velocity profile of saccadic eye movements. Based on our model, we derive the Neyman Pearson Detector (NPD) for saccadic events. Implemented in conjunction with the maximum likelihood estimation method, our NPD can detect a saccadic event and estimate all parameters simultaneously. Because of its adaptive nature and its statistical optimality, our NPD method was able to better detect microsaccades in some datasets when compared with a recently proposed stateof-the-art method based on convolutional neural networks. NPD also yielded comparable performance with a recently developed Bayesian algorithm, with the added benefit of modeling a more biologically veridical velocity profile of the saccade. As opposed to these algorithms, NPD can lend itself better to online saccade detection, and thus has potential for human-computer interaction applications. Our algorithm is publicly available at https://github.com/hz-zhu/NPD-microsaccade-detection.

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Saccade detection using a particle filter

Cited by 21 publications

References 29 publications

Switching between two targets with non‐constant velocity profiles reveals shared internal model of target motion

Switching between two targets with non‐constant velocity profiles reveals shared internal model of target motion

MAD saccade: statistically robust saccade threshold estimation

The Neyman Pearson detection of microsaccades with maximum likelihood estimation of parameters

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