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Updated: May 11, 2025

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A simultaneous EEG and eye-tracking dataset for remote sensing object detection.

Bing He1, Hongqiang Zhang1, Tong Qin2

  • 1Advanced Interdisciplinary Institute of Satellite Applications, State Key Laboratory of Earth Surface Processes and Resource Ecology, Faculty of Geographical Science, Beijing Normal University, Beijing, 100875, China.

Scientific Data
|April 17, 2025
PubMed
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This summary is machine-generated.

We present the first dataset linking electroencephalography (EEG) and eye-tracking for remote sensing object detection. This resource aids understanding human visual cognition and improving AI in geospatial tasks.

Area of Science:

  • Cognitive Science
  • Geospatial Artificial Intelligence
  • Neuroscience

Background:

  • Human visual search and object identification are crucial in remote sensing.
  • Existing datasets lack synchronized neurophysiological and eye-movement data.
  • Understanding cognitive processes enhances AI model interpretability.

Purpose of the Study:

  • Introduce the first synchronized electroencephalography (EEG) and eye-tracking dataset for remote sensing object detection (EEGET-RSOD).
  • Facilitate research into human visual cognition during object identification in remote sensing imagery.
  • Bridge the gap between human cognition and artificial intelligence in geospatial applications.

Main Methods:

  • Collected simultaneous EEG and eye-tracking data from 38 remote sensing experts.

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  • Experts identified specific objects in 1,000 remote sensing images under time constraints.
  • Dataset synchronizes neurophysiological signals with visual search behavior.

    • Main Results:

    • The EEGET-RSOD dataset provides a unique resource for analyzing cognitive processes.
    • It captures expert performance in remote sensing object detection tasks.
    • Enables correlation of brain activity and eye movements with detection accuracy and efficiency.

    Conclusions:

    • EEGET-RSOD is the first publicly available dataset of its kind for remote sensing.
    • It advances the study of human visual cognition in complex, real-world environments.
    • Enhances the interpretability and reliability of AI models in geospatial applications by integrating human cognitive insights.