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

06/17/2022
by   Lukas Wolf, et al.
0

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.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
12/22/2013

A Survey on Eye-Gaze Tracking Techniques

Study of eye-movement is being employed in Human Computer Interaction (H...
research
01/04/2022

Exploiting EEG Signals for Eye Motion Tracking

Human eye tracking devices can help to investigate principles of process...
research
08/09/2023

An Interpretable and Attention-based Method for Gaze Estimation Using Electroencephalography

Eye movements can reveal valuable insights into various aspects of human...
research
02/19/2023

Electrode Clustering and Bandpass Analysis of EEG Data for Gaze Estimation

In this study, we validate the findings of previously published papers, ...
research
01/27/2021

Efficient Video Summarization Framework using EEG and Eye-tracking Signals

This paper proposes an efficient video summarization framework that will...
research
07/31/2022

Vector-Based Data Improves Left-Right Eye-Tracking Classifier Performance After a Covariate Distributional Shift

The main challenges of using electroencephalogram (EEG) signals to make ...
research
03/13/2020

A Neural Architecture for Detecting Confusion in Eye-tracking Data

Encouraged by the success of deep learning in a variety of domains, we i...

Please sign up or login with your details

Forgot password? Click here to reset