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This paper is in the following e-collection/theme issue:

Reviews on Innovation in Rehabilitation and Assistive Technologies (142) Neurology and Neurosciences (396) Cognitive and Neurorehabilitation (446) Computer-Aided Diagnosis (435) Neurorehabilitation and Technology (59) Neurotech Innovations, Diagnostic Tools and Techniques (76)

Published on in Vol 13 (2026)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/81276, first published .
Laptop displaying a systematic review of eye-tracking technologies for cognitive assessment after acquired brain injury.

Eye-Tracking Technologies for Cognitive Assessment After Acquired Brain Injury: Systematic Review

Eye-Tracking Technologies for Cognitive Assessment After Acquired Brain Injury: Systematic Review

Authors of this article:

Andrea Calderone1 Author Orcid Image ;   Rosaria De Luca1 Author Orcid Image ;   Francesco Corallo1 Author Orcid Image ;   Rosalia Calapai1 ;   Alessio Mirabile1 Author Orcid Image ;   Angelo Quartarone1 Author Orcid Image ;   Alessandro Marco De Nunzio2, 3 Author Orcid Image ;   Carmela Casella4 Author Orcid Image ;   Rocco Salvatore Calabrò1 Author Orcid Image
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Journals

  1. Osińska V, Mikołajewski D, Szalach A, Osińska A. Enhancing VR User Modelling with Pupillometry: An Eye-Tracking-Driven Machine Learning Approach. Applied Sciences 2026;16(19):9639 View