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Investigating Non-Visual Eye Movements Non-Intrusively: Comparing Manual and Automatic Annotation Styles.

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Summary

This study explores annotating non-visual eye-movements (NVEMs). Two methods, manual and algorithmic, show good consistency and potential for mapping, advancing NVEM research.

Keywords:
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Area of Science:

  • Cognitive Neuroscience
  • Ophthalmology
  • Human-Computer Interaction

Background:

  • Non-visual eye-movements (NVEMs) are crucial for understanding cognitive processes but are under-explored due to annotation challenges.
  • Conventional eye-trackers struggle with NVEMs as they are not tied to specific visual targets, necessitating new annotation approaches.

Purpose of the Study:

  • To present and evaluate two distinct methods for annotating non-visual eye-movement data.
  • To assess the consistency of each annotation approach and the compatibility between them.

Main Methods:

  • Manual annotation of NVEM data using a grid-based system in ELAN.
  • Algorithmic annotation of NVEM data using a Cartesian coordinate-based system derived from OpenFace.

Main Results:

  • Both the manual grid-based and algorithmic coordinate-based approaches demonstrated good overall consistency.
  • Preliminary evidence suggests the possibility of mapping algorithmic gaze estimations (OpenFace) onto the manual coding grid.

Conclusions:

  • The developed methods offer viable solutions for annotating NVEM data, overcoming limitations of traditional eye-tracking.
  • Further research can build upon these findings to enhance the study of cognitive processes through NVEM analysis.