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Related Experiment Video

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A Methodology for Capturing Joint Visual Attention Using Mobile Eye-Trackers

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Published on: January 18, 2020

Combining Implicit and Explicit Feature Extraction for Eye Tracking: Attention Classification Using a Heterogeneous

Lisa-Marie Vortmann¹, Felix Putze¹

¹Cognitive Systems Lab, Department of Mathematics and Computer Science, University of Bremen, 28359 Bremen, Germany.

Sensors (Basel, Switzerland)

|December 28, 2021

Summary

This summary is machine-generated.

Combining implicit and explicit eye tracking features significantly improves attentional state detection. This hybrid approach enhances machine learning accuracy for eye tracking data classification, outperforming traditional methods.

Keywords:

Gramian angular fields Markov transition fields attention convolutional neural network eye tracking feature extraction heterogeneous feature sets implicit feature learning

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Area of Science:

Cognitive Science
Computer Science
Neuroscience

Background:

Traditional eye tracking analysis uses explicit features like fixations and saccades.
Neural networks trained on implicit raw eye tracking data show superior performance.
A gap exists in leveraging both implicit and explicit feature sets for enhanced classification.

Purpose of the Study:

To integrate implicit and explicit eye tracking features into a single classification model.
To improve the accuracy of attentional state detection using eye tracking data.
To evaluate the performance of combined features against implicit-only and explicit-only approaches.

Main Methods:

A neural network was adapted to process heterogeneous implicit and explicit eye tracking features.
The model predicted internally and externally directed attention for 154 participants.
Classification accuracies were compared across different window lengths and evaluated for person- and task-independence.

Main Results:

Combining implicit and explicit features significantly improved attentional state classification accuracy.
The hybrid approach achieved better-than-chance accuracy for new tasks.
Person-independent classification with combined features outperformed person-dependent classifiers in some settings.

Conclusions:

Integrating implicit and explicit eye tracking features offers a powerful approach for attentional state detection.
This combined method enhances machine learning performance in eye tracking applications.
Future research should consider implicit data representations alongside explicit features for robust eye tracking classification.