Unsupervised detection of microsaccades in a high-noise regime

Can Microsaccades Be Used for Biometrics?

Human eyes are in constant motion. Even when we fix our gaze on a certain point, our eyes continue to move. When looking at a point, scientists have distinguished three different fixational eye movements (FEM)-microsaccades, drift and tremor. The main goal of this paper is to investigate one of these FEMs-microsaccades-as a source of information for biometric analysis. The paper argues why microsaccades are preferred for biometric analysis over the other two fixational eye movements. The process of microsaccades' extraction is described. Thirteen parameters are defined for microsaccade analysis, and their derivation is given. A gradient algorithm was used to solve the biometric problem. An assessment of the weights of the different pairs of parameters in solving the biometric task was made.

Current Challenges Supporting School-Aged Children with Vision Problems: A Rapid Review

Many children have undetected vision problems or insufficient visual information processing that may be a factor in lower academic outcomes. The aim of this paper is to contribute to a better understanding of the importance of vision screening for school-aged children, and to investigate the possibilities of how eye-tracking (ET) technologies can support this. While there are indications that these technologies can support vision screening, a broad understanding of how to apply them and by whom, and if it is possible to utilize them at schools, is lacking. We review interdisciplinary research on performing vision investigations, and discuss current challenges for technology support. The focus is on exploring the possibilities of ET technologies to better support screening and handling of vision disorders, especially by non-vision experts. The data orginate from a literature survey of peer-reviewed journals and conference articles complemented by secondary sources, following a rapid review methodology. We highlight current trends in supportive technologies for vision screening, and identify the involved stakeholders and the research studies that discuss how to develop more supportive ET technologies for vision screening and training by non-experts.

Human-level saccade detection performance using deep neural networks

Saccades are ballistic eye movements that rapidly shift gaze from one location of visual space to another. Detecting saccades in eye movement recordings is important not only for studying the neural mechanisms underlying sensory, motor, and cognitive processes, but also as a clinical and diagnostic tool. However, automatically detecting saccades can be difficult, particularly when such saccades are generated in coordination with other tracking eye movements, like smooth pursuits, or when the saccade amplitude is close to eye tracker noise levels, like with microsaccades. In such cases, labeling by human experts is required, but this is a tedious task prone to variability and error. We developed a convolutional neural network to automatically detect saccades at human-level accuracy and with minimal training examples. Our algorithm surpasses state of the art according to common performance metrics and could facilitate studies of neurophysiological processes underlying saccade generation and visual processing. NEW & NOTEWORTHY Detecting saccades in eye movement recordings can be a difficult task, but it is a necessary first step in many applications. We present a convolutional neural network that can automatically identify saccades with human-level accuracy and with minimal training examples. We show that our algorithm performs better than other available algorithms, by comparing performance on a wide range of data sets. We offer an open-source implementation of the algorithm as well as a web service.