CLRGaze: Contrastive Learning of Representations for Eye Movement Signals
This work advances machine learning for eye movements and similar biosignals by providing a general representation learning method, though it is incremental as it applies existing contrastive learning techniques to a specific domain.
The paper tackled the problem of ambiguous eye movement signals requiring feature engineering by proposing a self-supervised contrastive learning method to learn feature vectors, achieving up to 97.3% accuracy on biometric tasks with a linear classifier.
Eye movements are intricate and dynamic biosignals that contain a wealth of cognitive information about the subject. However, these are ambiguous signals and therefore require meticulous feature engineering to be used by machine learning algorithms. We instead propose to learn feature vectors of eye movements in a self-supervised manner. We adopt a contrastive learning approach and propose a set of data transformations that encourage a deep neural network to discern salient and granular gaze patterns. This paper presents a novel experiment utilizing six eye-tracking data sets despite different data specifications and experimental conditions. We assess the learned features on biometric tasks with only a linear classifier, achieving 84.6% accuracy on a mixed dataset, and up to 97.3% accuracy on a single dataset. Our work advances the state of machine learning for eye movements and provides insights into a general representation learning method not only for eye movements but also for similar biosignals.