Dataset Information

A Deep Learning-Based Approach to Video-Based Eye Tracking for Human Psychophysics.

ABSTRACT: Real-time gaze tracking provides crucial input to psychophysics studies and neuromarketing applications. Many of the modern eye-tracking solutions are expensive mainly due to the high-end processing hardware specialized for processing infrared-camera pictures. Here, we introduce a deep learning-based approach which uses the video frames of low-cost web cameras. Using DeepLabCut (DLC), an open-source toolbox for extracting points of interest from videos, we obtained facial landmarks critical to gaze location and estimated the point of gaze on a computer screen via a shallow neural network. Tested for three extreme poses, this architecture reached a median error of about one degree of visual angle. Our results contribute to the growing field of deep-learning approaches to eye-tracking, laying the foundation for further investigation by researchers in psychophysics or neuromarketing.

SUBMITTER: Zdarsky N

PROVIDER: S-EPMC8333872 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature

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Publications

A Deep Learning-Based Approach to Video-Based Eye Tracking for Human Psychophysics.

Zdarsky Niklas N Treue Stefan S Esghaei Moein M

Frontiers in human neuroscience 20210721

Real-time gaze tracking provides crucial input to psychophysics studies and neuromarketing applications. Many of the modern eye-tracking solutions are expensive mainly due to the high-end processing hardware specialized for processing infrared-camera pictures. Here, we introduce a deep learning-based approach which uses the video frames of low-cost web cameras. Using DeepLabCut (DLC), an open-source toolbox for extracting points of interest from videos, we obtained facial landmarks critical to g ...[more]

PMID: 34366813

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Project description:BackgroundThe oculomotor circuit spans many cortical and subcortical areas that have been implicated in psychiatric disease. This, combined with previous findings, suggests that eye tracking may be a useful method to investigate eating disorders. Therefore, this study aimed to assess oculomotor behaviors in youth with and without an eating disorder.MethodsFemale youth with and without an eating disorder completed a structured task involving randomly interleaved pro-saccade (toward at a stimulus) and anti-saccade (away from stimulus) trials with video-based eye tracking. Differences in saccades (rapid eye movements between two points), eye blinks and pupil were examined.ResultsYouth with an eating disorder (n = 65, Mage = 17.16 ± 3.5 years) were compared to healthy controls (HC; n = 65, Mage = 17.88 ± 4.3 years). The eating disorder group was composed of individuals with anorexia nervosa (n = 49), bulimia nervosa (n = 7) and other specified feeding or eating disorder (n = 9). The eating disorder group was further divided into two subgroups: individuals with a restrictive spectrum eating disorder (ED-R; n = 43) or a bulimic spectrum eating disorder (ED-BP; n = 22). In pro-saccade trials, the eating disorder group made significantly more fixation breaks than HCs (F(1,128) = 5.33, p = 0.023). The ED-BP group made the most anticipatory pro-saccades, followed by ED-R, then HCs (F(2,127) = 3.38, p = 0.037). Groups did not differ on rate of correct express or regular latency pro-saccades. In anti-saccade trials, groups only significantly differed on percentage of direction errors corrected (F(2, 127) = 4.554, p = 0.012). The eating disorder group had a significantly smaller baseline pupil size (F(2,127) = 3.60, p = 0.030) and slower pro-saccade dilation velocity (F(2,127) = 3.30, p = 0.040) compared to HCs. The ED-R group had the lowest blink probability during the intertrial interval (ITI), followed by ED-BP, with HCs having the highest ITI blink probability (F(2,125) = 3.63, p = 0.029).ConclusionsThese results suggest that youth with an eating disorder may have different oculomotor behaviors during a structured eye tracking task. The oculomotor behavioral differences observed in this study presents an important step towards identifying neurobiological and cognitive contributions towards eating disorders.

Dataset Information

A Deep Learning-Based Approach to Video-Based Eye Tracking for Human Psychophysics.

Publications

A Deep Learning-Based Approach to Video-Based Eye Tracking for Human Psychophysics.

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