EyeMap: A fusion-based method for eye movement-based visual attention maps as predictive markers of parkinsonism
Bibliographic record
Abstract
EyeMap is a method for visualizing and classifying eye movement patterns using scanpaths, fixation heatmaps, and gridded Areas of Interest (AOIs). EyeMap combines predictions from modality-specific machine learning and deep learning models using a late-fusion technique to produce interpretable gaze representations. By collecting spatial, temporal, and regional elements of gaze data, the method enhances diagnostic interpretability and enables the detection of Parkinsonian symptoms. This method provides complementary perspectives on gaze behavior, encompassing spatial focus, temporal scan order, and attention allocation across regions of interest. A dataset consisting of visualizations of organized visual tasks completed by both PD patients and healthy controls is created to support the development and validation of this method. EyeMap shows that vision-driven models may detect PD-specific gaze anomalies without the need for manual feature engineering. All implementation steps, from data acquisition to model fusion, are fully described to enable reproducibility and potential adaptation to other gaze-based analysis contexts.1.A structured method was developed to visualize eye-tracking data in three distinct formats2.Classification outputs from separate gaze visualizations were combined using softmax-level fusion3.A new eye-tracking dataset was generated to support method development and reproducibility.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".