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


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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.

Publication status

published

Book title

Medical Image Computing and Computer Assisted Intervention – MICCAI 2023

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

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Date collected

Date created

Subject

EEG; Interpretable model; Attention mechanism

Organisational unit

03604 - Wattenhofer, Roger / Wattenhofer, Roger check_circle
03979 - Hilliges, Otmar (ehemalig) / Hilliges, Otmar (former)

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Related publications and datasets

Is new version of: 10.48550/arXiv.2308.05768