An Interpretable and Attention-based Method for Gaze Estimation Using Electroencephalography
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Author / Producer
Date
2023-10
Publication Type
Conference Paper
ETH Bibliography
yes
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Abstract
Eye movements can reveal valuable insights into various aspects of human mental processes, physical well-being, and actions. Recently, several datasets have been made available that simultaneously record EEG activity and eye movements. This has triggered the development of various methods to predict gaze direction based on brain activity. However, most of these methods lack interpretability, which limits their technology acceptance. In this paper, we leverage a large data set of simultaneously measured Electroencephalography (EEG) and Eye tracking, proposing an interpretable model for gaze estimation from EEG data. More specifically, we present a novel attention-based deep learning framework for EEG signal analysis, which allows the network to focus on the most relevant information in the signal and discard problematic channels. Additionally, we provide a comprehensive evaluation of the presented framework, demonstrating its superiority over current methods in terms of accuracy and robustness. Finally, the study presents visualizations that explain the results of the analysis and highlights the potential of attention mechanism for improving the efficiency and effectiveness of EEG data analysis in a variety of applications.
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Publication status
published
External links
Book title
Medical Image Computing and Computer Assisted Intervention – MICCAI 2023
Journal / series
Volume
14221
Pages / Article No.
734 - 743
Publisher
Springer
Event
26th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2023)
Edition / version
Methods
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Geographic location
Date collected
Date created
Subject
EEG; Interpretable model; Attention mechanism
Organisational unit
03604 - Wattenhofer, Roger / Wattenhofer, Roger
03979 - Hilliges, Otmar (ehemalig) / Hilliges, Otmar (former)
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Funding
Related publications and datasets
Is new version of: 10.48550/arXiv.2308.05768