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. 2025 Apr 17;12(1):651.
doi: 10.1038/s41597-025-04995-w.

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

Bing He  1 , Hongqiang Zhang  1 , Tong Qin  2 , Bowen Shi  3 , Qiao Wang  1 , Weihua Dong  4
Affiliations

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

Bing He et al. Sci Data. .

Abstract

We introduce the EEGET-RSOD, a simultaneous electroencephalography (EEG) and eye-tracking dataset for remote sensing object detection. This dataset contains EEG and eye-tracking data when 38 remote sensing experts located specific objects in 1,000 remote sensing images within a limited time frame. This task reflects the typical cognitive processes associated with human visual search and object identification in remote sensing imagery. To our knowledge, EEGET-RSOD is the first publicly available dataset to offer synchronized eye-tracking and EEG data for remote sensing images. This dataset will not only advance the study of human visual cognition in real-world environment, but also bridge the gap between human cognition and artificial intelligence, enhancing the interpretability and reliability of AI models in geospatial applications.

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Conflict of interest statement

Competing interests: The authors declare no competing interests.

Figures

Fig. 1
Fig. 1
Experimental setup. (A) Examples of stimuli; (B) Eye tracking and simultaneous EEG acquisition system; (C) Experimental procedure; (D) The position of the EEG electrodes following the 10–20 system.
Fig. 2
Fig. 2
Visualization of single-trial EEG and eye-tracking blink data.
Fig. 3
Fig. 3
The structure of the dataset.
Fig. 4
Fig. 4
Objective detection rate measurement method. These images are used as illustrations. The red dots and lines are the scanpaths of the participant’s eye movement, and the blue areas are the objects that need to be searched.
Fig. 5
Fig. 5
Eye movement indices of the participants on object and non-object images.
Fig. 6
Fig. 6
Saccade direction difference and visualization.
Fig. 7
Fig. 7
Consistency of fixation point distributions.
Fig. 8
Fig. 8
ERPs of different channels.
Fig. 9
Fig. 9
The mean alpha/theta and alpha/beta band power ratio of all the participants on object and non-object images.
Fig. 10
Fig. 10
EEG events based on fixation classification.
See this image and copyright information in PMC

References

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