PyNeon: A Python package for the analysis of Neon multimodal mobile eye-tracking data

Area of Science:

• Cognitive Science
• Human-Computer Interaction
• Neuroscience

Background:

• Mobile eye-tracking enables real-world human behavior and cognition studies.
• Analyzing dynamic, multimodal eye-tracking data presents significant challenges in alignment, integration, and interpretation.

Purpose of the Study:

• Introduce PyNeon, a Python package to streamline mobile eye-tracking data analysis.
• Provide accessible APIs for reading, preprocessing, epoching, and exporting Neon eye-tracking data.
• Support advanced video processing, including real-world coordinate mapping and dynamic scanpath estimation.

Main Methods:

• Developed PyNeon as a versatile, community-oriented Python package.
• Implemented APIs for data handling and preprocessing.
• Integrated advanced video processing capabilities for spatial and temporal analysis.

Main Results:

• PyNeon offers streamlined analysis of mobile eye-tracking, motion, and video data.
• The package facilitates mapping eye movements to real-world coordinates.
• Dynamic scanpath estimation is supported for enhanced behavioral analysis.

Conclusions:

• PyNeon provides an open-source, extendable framework for mobile eye-tracking data analysis.
• The package simplifies complex data integration and interpretation.
• PyNeon serves as a foundation for developing higher-level applications in behavioral research.