Fixational eye movements, including microsaccades, drift, and tremor, provide a high-frequency and largely involuntary signal of oculomotor control, with potential relevance for understanding neurological function. Although large pre-trained time-series foundation models have recently shown strong transfer potential across domains such as energy, weather, finance, and general sensor data, their applicability to fixation-based eye-tracking signals remains underexplored. This proposed project will address this gap by evaluating whether large pre-trained time-series models can be adapted to fixational eye-movement data and whether such adaptation can support clinically motivated downstream analysis. The study will use a two-stage experimental framework. First, multiple pre-trained time-series foundation models and baseline models will be evaluated on a large-scale microsaccade classification task to assess transferability to fixational eye-movement signals. Second, the most promising model family will be examined in a Parkinson’s disease versus healthy control classification setting, using fixation-based clinical eye-tracking data as a downstream application. The expected contribution of the paper is to establish fixational eye movements as a novel and biologically meaningful application domain for time-series foundation models. Rather than presenting a deployable diagnostic system, the work will provide evidence, methodology, and practical guidance for adapting large pre-trained time-series models to small, clinically relevant eye-tracking datasets. The proposed study may open a path toward future foundation-model-based analysis of neurological signals captured through non-invasive eye tracking.