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A Deep Learning Approach for the Segmentation of Electroencephalography Data in Eye Tracking Applications
Lukas Wolf · Ard Kastrati · Martyna Plomecka · Jieming Li · Dustin Klebe · Alexander Veicht · Roger Wattenhofer · Nicolas Langer

Tue Jul 19 02:20 PM -- 02:25 PM (PDT) @ Room 301 - 303
in APP: Neuroscience, Cognitive Science »

The collection of eye gaze information provides a window into many critical aspects of human cognition, health and behaviour. Additionally, many neuroscientific studies complement the behavioural information gained from eye tracking with the high temporal resolution and neurophysiological markers provided by electroencephalography (EEG). One of the essential eye-tracking software processing steps is the segmentation of the continuous data stream into events relevant to eye-tracking applications, such as saccades, fixations, and blinks. Here, we introduce DETRtime, a novel framework for time-series segmentation that creates ocular event detectors that do not require additionally recorded eye-tracking modality and rely solely on EEG data. Our end-to-end deep-learning-based framework brings recent advances in Computer Vision to the forefront of the times series segmentation of EEG data. DETRtime achieves state-of-the-art performance in ocular event detection across diverse eye-tracking experiment paradigms. In addition to that, we provide evidence that our model generalizes well in the task of EEG sleep stage segmentation.

Keywords: APP: Time Series · APP: Neuroscience, Cognitive Science

Author Information

Lukas Wolf (ETH Zürich)
Ard Kastrati (ETH Zurich)
Martyna Plomecka (University of Zurich)
Jieming Li (ETH Zürich)
Dustin Klebe (ETH Zurich)
Alexander Veicht (ETH Zürich)
Roger Wattenhofer (ETH Zurich)
Nicolas Langer (University of Zurich)

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