Unified Categorization for Eyetracking in Python
Categorization for Eyetracking in Python
This repository was developed for Peter König’s Neurobiopsychology Lab at the Institute of Cognitive Science, Osnabrück. Its aim is to provide easy access to different automated gaze classification algorithms and to generate a unified, simplistic, and elegant way of handling Eyetracking data.
Currently available gaze classification algorithms are:
- NSLR-HMM: Pekkanen, J., & Lappi, O. (2017). A new and general approach to signal denoising and eye movement classification based on segmented linear regression. Scientific reports, 7(1), 1-13.
- REMoDNaV: Dar *, A. H., Wagner *, A. S. & Hanke, M. (2019). REMoDNaV: Robust Eye Movement Detection for Natural Viewing. bioRxiv. DOI: 10.1101/619254
- I-DT dispersion-based algorithm: Salvucci, D. D., & Goldberg, J. H. (2000). Identifying fixations and saccades in eye-tracking protocols. In Proceedings of the 2000 symposium