Early Diagnosis of Neurodegenerative Diseases Using Video-Oculography
15th International Symposium on Communication Systems, Networks and Digital Signal Processing, CSNDSP 2026, Edinburgh, England, 15 - 17 July 2026, (Full Text)
- Publication Type: Conference Paper / Full Text
- Doi Number: 10.1109/csndsp68462.2026.11654439
- City: Edinburgh
- Country: England
- Keywords: eye-tracking, IAPS, machine learning, neurodegenerative disease, Video-oculography
- Galatasaray University Affiliated: Yes
Abstract
Video-oculography (VOG) provides a fast, noninvasive way to quantify eye-movement and pupil responses linked to how the brain samples and interprets visual information. We propose a task-free, emotionally valenced viewing paradigm for automatic screening of neurodegenerative disorders. Participants freely viewed a curated set of IAPS (International Affective Picture System) images spanning positive/negative/neutral valence and face/object content while gaze and pupil signals were recorded. From these recordings, we derived compact oculomotor and pupillary descriptors that capture spatial viewing allocation, eye movements, pupil dynamics, and frequency dynamics, and used them to discriminate healthy controls from amnestic mild cognitive impairment (aMCI), behavioral-variant frontotemporal dementia (bvFTD), and posterior cortical atrophy (PCA). Best configurations achieved mean subject-level accuracies of 0.871 ±0.085 for healthy controls (CTR) versus aMCI, 0.817±0.060 for CTR versus bvFTD, and 0.786 ± 0.060 for CTR versus PCA, supporting affect-sensitive free-viewing VOG as a low-burden complementary screening signal.