A Deep Learning Approach for the Segmentation of Electroencephalography Data in Eye Tracking Applications
This addresses the need for integrated analysis in neuroscience and eye-tracking applications, offering a novel method that could reduce hardware requirements, though it is incremental in applying computer vision techniques to time-series data.
The paper tackled the problem of segmenting continuous EEG data into ocular events like saccades and fixations without needing separate eye-tracking data, achieving state-of-the-art performance across diverse paradigms and showing generalization to EEG sleep stage segmentation.
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.