A Deep Learning Approach for the Segmentation of Electroencephalography Data in Eye Tracking Applications

• URL: http://arxiv.org/abs/2206.08672v1
• Date: Fri, 17 Jun 2022 10:17:24 GMT
• Title: A Deep Learning Approach for the Segmentation of Electroencephalography Data in Eye Tracking Applications
• Authors: Lukas Wolf, Ard Kastrati, Martyna Beata P{\l}omecka, Jie-Ming Li, Dustin Klebe, Alexander Veicht, Roger Wattenhofer, Nicolas Langer
• Abstract summary: We introduce DETRtime, a novel framework for time-series segmentation of EEG data. Our end-to-end deep learning-based framework brings advances in Computer Vision to the forefront. Our model generalizes well in the task of EEG sleep stage segmentation.
• Score: 56.458448869572294
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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