Pith. sign in

REVIEW 3 major objections 4 minor 56 references

Mind Your Vision: Multimodal Estimation of Refractive Disorders Using Electrooculography and Eye Tracking

T0 review · 3 major / 4 minor · reviewed 2026-08-07 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read This paper claims that combining EOG and eye-tracking signals lets an LSTM classify an individual's induced refractive power with 96.207% mean accuracy, while cross-person accuracy barely clears chance.

desk verdict A transparent feasibility study whose within-subject 96% accuracy is likely inflated by eye-tracker drift and unstated lens ordering; the cross-subject failure is honestly reported. read the letter →

arxiv 2505.18538 v1 pith:FH4OTLQ3 submitted 2025-05-24 eess.IV cs.LG

Xin Wei , Huakun Liu , Yutaro Hirao , Monica Perusquia-Hernandez , Katsutoshi Masai , Hideaki Uchiyama , Kiyoshi Kiyokawa This is my paper · ORCID
classification eess.IVcs.LG
keywords refractiveerrorestimationelectrooculographyeyetrackingmultimodallearningLSTMclassificationdiopterpassivevisionscreeningsubject-dependentvssubject-independentgeneralization
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper asks whether refractive power can be estimated passively from eye movements, without the user answering questions or pressing buttons. Using a public dataset of 37 participants who wore trial lenses spanning -3.0 to +3.0 diopters, the authors trained LSTM classifiers on electrooculography (EOG), video eye tracking, and their combination. In a per-person setting, the combined model reached 96.207% mean accuracy across 13 lens conditions, significantly above both single-modality models. In a cross-person setting, all models fell to about chance, showing that the learned patterns are highly person-specific. The paper's contribution is evidence that eye movement signals carry usable information about induced blur, together with a clear picture of where that information does not yet generalize.

What carries the argument

The machinery is a four-layer unidirectional LSTM with 512 hidden units per layer, fed by an input projection with ReLU and a 0.5 dropout, trained for 250 epochs with cross-entropy loss. Inputs are 101 time-aligned features: four EOG channels plus their sample-to-sample differences (downsampled from 512 Hz to 120 Hz), and 93 eye-tracking features covering pupil geometry, gaze direction and position, fixations, and blink type, with low-confidence samples replaced by NaN before Hampel and median filtering. Temporal alignment across the two recording systems is achieved with event triggers marking trial start and end. In the subject-dependent setting, trial segments from one participant are cross-validated; in the subject-independent setting, leave-one-subject-out is used. The LSTM's role is to capture the temporal dynamics of eye movement that differ across lens conditions, and the multimodal input is what the paper argues supplies complementary information: EOG's fine-grained, stable voltage signals and the eye tracker's richer gaze and pupil features.

What would settle it

Rerun the same LSTM protocol with the eye tracker recalibrated immediately before every lens condition and with chinrest position verified; if the multimodal subject-dependent accuracy falls substantially below 96%, the original result was inflated by condition-correlated device offsets. A second decisive check is to compare accuracy on participants with high versus low gaze confidence: if the model is accurate only for high-confidence participants, low-confidence recordings are not carrying the claimed refractive-state signal.

Watch

Extended reading notes

Core claim

The central claim is that fusing EOG with video-based eye-tracking features yields a personalized classifier that can read an individual's induced refractive state from how they move their eyes. In the subject-dependent scenario, the multimodal LSTM achieved 96.207% mean classification accuracy over 13 diopter conditions, outperforming eye tracking alone (92.432%) and EOG alone (84.451%), with the differences statistically significant (Friedman chi2 = 55.62, p < .001; pairwise Wilcoxon p < .001). The confusion matrices show that residual errors are not random: the model most often confuses lenses of the same magnitude with opposite sign, such as -2.0 D and +2.0 D. In the subject-independent scenario, the same architecture produced only 8.882% mean accuracy, statistically indistinguishable from the 7.692% chance level, and the authors attribute the gap to known difficulties of cross-person physiological signals. They conclude that eye movement data support personalized, passive refractive-power monitoring but do not yet support a generalizable screening model.

Load-bearing premise

The load-bearing premise is that trial lenses worn by healthy participants reproduce the eye movement effects of genuine refractive error, and that the high within-person accuracy reflects those refractive-state-dependent changes rather than recording artifacts such as the eye tracker's unrecalibrated head-position offsets and unstable gaze confidence.

Editorial extensions

If this is right

  • A personalized eye movement model could monitor refractive state passively over time, since within-subject classification accuracy is high across all 13 lens conditions.
  • Eye tracking alone is a stronger single modality than EOG for this task in the per-person setting, suggesting gaze and pupil features carry more discriminative blur-related information.
  • The systematic confusion between positive and negative lenses of equal magnitude implies that models may first learn blur strength and only secondarily blur sign.
  • Any practical deployment for screening would need per-user calibration or some form of domain adaptation, because untouched cross-person accuracy is essentially chance.
  • The multimodal advantage, although modest in the cross-person setting, persists there (8.882% vs. 8.640% and 7.936%), which the paper reads as evidence that fusion remains the more promising foundation.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • If the eye tracker had been recalibrated before each lens condition, the subject-dependent eye-tracking accuracy might drop, which would indicate that part of the signal was device drift rather than refractive state; this is a direct test of the paper's core interpretation.
  • Collapsing the 13 classes into coarser groups, such as blur magnitude or a simple 'blurred vs. clear' distinction, might survive cross-person transfer better, given that even the per-person errors concentrate on sign.
  • Because EOG alone already reaches 84% per-person accuracy with only eight features, a minimal wearable EOG setup might be sufficient for longitudinal personal monitoring, with eye tracking adding marginal value only when its calibration is reliable.
  • The very low gaze confidence of some participants (e.g., P21 at 0.29 +/- 0.39) raises the possibility that per-person accuracy is carried by the high-confidence majority; checking accuracy stratified by gaze confidence would separate true signal from tracking-quality artifacts.
Share X Bluesky LinkedIn Reddit HN

Signed reviews

No signed human review yet.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

3 major / 4 minor

Summary. The paper trains unidirectional multi-layer LSTM classifiers to estimate refractive power from EOG and video-based eye-tracking signals in 37 participants whose refractive state was manipulated with trial lenses from -3.0D to +3.0D in 0.5D steps (13 classes). Models are evaluated in subject-dependent (per-user cross-validation) and subject-independent (leave-one-subject-out) settings. The multimodal model reaches 96.207% mean accuracy in the subject-dependent setting and significantly outperforms unimodal EOG (84.451%) and eye-tracking (92.432%) models on a Friedman test with Wilcoxon post-hoc comparisons. In the subject-independent setting, all models perform near the 7.692% chance level (multimodal 8.882%), with no significant between-model differences. The authors interpret the results as evidence for the potential and limitations of passive refractive power estimation from eye movement data.

Significance. If the subject-dependent result reflects genuine refractive-state-dependent changes in eye movements, the paper would make a useful contribution to passive vision screening, particularly by showing that combined EOG and eye tracking can be more informative than either modality alone. The manuscript is careful in several respects: the protocol is described in enough detail to be reproduced, the evaluation covers both personalized and cross-subject scenarios, statistical testing is appropriate for the comparisons made, and the limitations (especially eye-tracker non-recalibration and low gaze confidence for some participants) are explicitly acknowledged. However, the central quantitative claim is currently threatened by a temporal confounding that the manuscript itself identifies but does not resolve; therefore the significance of the result cannot be assessed until this is addressed.

major comments (3)
  1. [§3.1, §7] The manuscript never states whether the order of the 13 lens conditions was randomized or counterbalanced across participants. Because the eye tracker was calibrated once at session start and not recalibrated (Section 7), and because EOG electrodes and participant state can drift over time, any slow temporal drift in signal quality is perfectly correlated with diopter under a fixed or partially ordered condition sequence. A four-layer LSTM with 512 hidden units can readily memorize such drift, which would explain the near-ceiling 96.207% subject-dependent accuracy even if the lenses had no refractive effect. This is load-bearing for the paper's central claim, so the authors should report the condition ordering and provide control analyses, such as label-shuffled baselines, regression on session time, comparison of early versus late session conditions, or a calibration-offset monitoring analysis.
  2. [§5, §6, Table 6] The acknowledged gaze confidence instability for several participants (P21: 0.29 ± 0.39, P22: 0.30 ± 0.35) and the authors' concession that discriminative patterns may reflect fluctuations in device stability directly undercut the interpretation of the eye-tracking and multimodal results. Confidence values were excluded from the features, but the underlying instability can still propagate into pupil and gaze feature values. To support the claim that the models capture refractive-state-dependent signals rather than recording artifacts, the authors should show that the main accuracy results remain when restricted to participants with high and stable gaze confidence (e.g., P5, P30), or after regressing out time and confidence-related effects.
  3. [Abstract, §4, Table 5] The abstract and Section 4 describe the subject-independent accuracy (8.882%) as 'marginally above chance,' but no statistical test against the chance level of 7.692% is reported. The Friedman test in Table 5 only compares the three models and yields p = 0.482. Since the subject-independent result is used to characterize generalization, the authors should either add an appropriate test against chance (e.g., binomial or permutation test across subjects) or temper the claim to a descriptive observation.
minor comments (4)
  1. [§3.3] The text has several capitalization inconsistencies (e.g., 'We employ' mid-sentence) and the sentence beginning 'To fill this gap, We employ' should be revised.
  2. [§3.1, Reference [52]] The dataset is described as publicly available, but reference [52] is the authors' own work; please clarify the dataset's public availability and provide a URL or repository link if applicable.
  3. [Figure 3, Table 2] Figure 3 reports error bars as standard error of the mean while Table 2 reports ± values without specifying whether they are standard deviations or standard errors; please standardize the notation and caption descriptions.
  4. [§5] The statement that participants with high-quality eye-tracking data also achieved strong classification performance could be quantified (e.g., correlation between mean gaze confidence and accuracy) to support the argument.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the supervised learning pipeline trains and evaluates on held-out trials/subjects, and the only self-reference (the authors' own dataset) is an empirical input, not a derivational premise.

full rationale

The paper's derivation chain is a standard supervised classification pipeline: refractive labels are the 13 trial-lens conditions; EOG and eye-tracking features are aligned preprocessing outputs; an LSTM is trained with cross-entropy loss; and performance is reported on held-out trial segments (subject-dependent, 8-fold cross-validation) and held-out subjects (subject-independent, leave-one-subject-out). No parameter is fitted to the test labels, and the headline accuracies are actual held-out predictions rather than reconstructions of the training objective. The only self-citation that could be flagged is the use of the authors' own dataset [52] as the 'publicly available dataset,' but this is an empirical data source, not a theorem, uniqueness argument, or fitted ansatz, and the central claim does not reduce to it. The limitations the paper itself states—single calibration, possible positional offsets, low gaze confidence for some participants, and the possibility that discriminative patterns reflect device stability fluctuations (Sections 5, 6, and 7)—are threats to construct validity and generalizability, not circularity in the derivation. The subject-independent results being near chance further indicates that the reported within-subject accuracy is not an artifact of a tautological label construction. Under the standards of this review, the absence of derivational circularity warrants a score of 0.

Assumptions & free parameters 3 free parameters · 3 assumptions · 0 invented entities

The central claim rests on the validity of the trial-lens simulation, the assumption that refractive state changes eye-movement patterns measurably, and the accuracy of cross-modal temporal alignment. No new physical entities are introduced. Preprocessing and architecture choices are hand-set free parameters that are not varied.

free parameters (3)
  • LSTM architecture (4 layers, 512 hidden units, dropout 0.5) = 4 layers x 512 units, dropout 0.5
    Chosen by hand; no sensitivity analysis or architecture search is reported.
  • Training hyperparameters (250 epochs, learning rate 0.0002, milestone 200, decay 0.05, batch size 256) = 250 epochs, lr 0.0002, milestone 200, decay 0.05, batch 256
    Fixed choices that affect convergence and final accuracy; no ablation is provided.
  • Preprocessing thresholds (3 SD outlier, confidence 0.5, Hampel 3 MAD, 100 ms window) = 3 SD, 0.5 confidence, 3 MAD, 100 ms
    Arbitrary thresholds that determine which samples are replaced or removed; their impact on the result is not tested.
assumptions (3)
  • domain assumption Simulated refractive error with trial lenses on healthy participants is a valid proxy for real refractive error.
    The dataset [52] uses trial lenses on 37 participants without confirmed refractive errors; no patient cohort is included.
  • domain assumption Eye movement patterns differ systematically with refractive state and are measurable in EOG and eye-tracking signals.
    This motivates the entire approach; cited literature supports related differences, but the assumption is not independently verified in this dataset.
  • domain assumption Temporal alignment via event triggers across EOG and eye tracker is accurate enough for fusion.
    Section 3.3 aligns 512 Hz EOG and 120 Hz eye-tracking data using trigger-relative timestamps; downsampling may lose information.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Mind Your Vision: Multimodal Estimation of Refractive Disorders Using Electrooculography and Eye Tracking." pith.science (2026). https://pith.science/paper/FH4OTLQ3

@misc{pith2026250518538,
  author       = {Pith},
  title        = {Pith review of: Mind Your Vision: Multimodal Estimation of Refractive Disorders Using Electrooculography and Eye Tracking},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/FH4OTLQ3}},
  note         = {Machine review of arXiv:2505.18538}
}
read the original abstract

Refractive errors are among the most common visual impairments globally, yet their diagnosis often relies on active user participation and clinical oversight. This study explores a passive method for estimating refractive power using two eye movement recording techniques: electrooculography (EOG) and video-based eye tracking. Using a publicly available dataset recorded under varying diopter conditions, we trained Long Short-Term Memory (LSTM) models to classify refractive power from unimodal (EOG or eye tracking) and multimodal configuration. We assess performance in both subject-dependent and subject-independent settings to evaluate model personalization and generalizability across individuals. Results show that the multimodal model consistently outperforms unimodal models, achieving the highest average accuracy in both settings: 96.207\% in the subject-dependent scenario and 8.882\% in the subject-independent scenario. However, generalization remains limited, with classification accuracy only marginally above chance in the subject-independent evaluations. Statistical comparisons in the subject-dependent setting confirmed that the multimodal model significantly outperformed the EOG and eye-tracking models. However, no statistically significant differences were found in the subject-independent setting. Our findings demonstrate both the potential and current limitations of eye movement data-based refractive error estimation, contributing to the development of continuous, non-invasive screening methods using EOG signals and eye-tracking data.

Figures

Figures reproduced from arXiv: 2505.18538 by the authors.

Figure 1
Figure 1. Multimodal classification of refractive error using eye movement data. [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Illustration of the processing techniques. This figure presents a flowchart of the signal processing steps. [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. Classification accuracy of EOG, eye-tracking, and multimodal models for each participant in the subject-dependent [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗
Figures from the paper (3 more)
Figure 4
Figure 4. Figure 4: Confusion matrices for EOG, eye-tracking, and multimodal models in subject-dependent scenario. [PITH_FULL_IMAGE:figures/full_fig_p006_4.png]
Figure 5
Figure 5. Figure 5: Classification accuracy of EOG, eye-tracking, and [PITH_FULL_IMAGE:figures/full_fig_p007_5.png]
Figure 6
Figure 6. Figure 6: Confusion matrices for EOG, eye-tracking, and multimodal models in subject-independent scenario. [PITH_FULL_IMAGE:figures/full_fig_p008_6.png]

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

56 extracted references · 55 canonical work pages

  1. [52]

    Xin Wei, Huakun Liu, Monica Perusquía-Hernández, Katsutoshi Masai, Naoya Isoyama, Hideaki Uchiyama, and Kiyoshi Kiyokawa. 2025. Electrooculography- Based Detection of Refractive Vision Problems

    work page 2025

  2. [1]

    Lekha Mary Abraham, Thomas Kuriakose, Viswanathan Sivanandam, Nithya Venkatesan, Ravi Thomas, and Jayaprakash Muliyil. 2005. Amplitude of Accom- modation and its Relation to Refractive Errors. Indian Journal of Ophthalmology 53, 2 (June 2005), 105

    work page 2005

  3. [2]

    American Optometric Association. [n. d.]. Comprehensive eye exams. https: //www.aoa.org/healthy-eyes/caring-for-your-eyes/eye-exams?sso=y

    work page

  4. [3]

    Rono, Iain A

    Andrew Bastawrous, Hillary K. Rono, Iain A. T. Livingstone, Helen A. Weiss, Stewart Jordan, Hannah Kuper, and Matthew J. Burton. 2015. Development and Validation of a Smartphone-Based Visual Acuity Test (Peek Acuity) for Clinical Practice and Community-Based Fieldwork. JAMA Ophthalmology 133, 8 (2015), 930–937

    work page 2015

  5. [4]

    Andreas Bulling, Daniel Roggen, and Gerhard Tröster. 2009. Wearable EOG goggles: eye-based interaction in everyday environments. In CHI ’09 Extended Abstracts on Human Factors in Computing Systems (CHI EA ’09) . Association for Computing Machinery, New York, NY, USA, 3259–3264

    work page 2009

  6. [5]

    Virginio Cantoni and Marco Porta. 2014. Eye tracking as a computer input and interaction method. In Proceedings of the 15th International Conference on Com- puter Systems and Technologies (CompSysTech ’14) . Association for Computing Machinery, New York, NY, USA, 1–12

    work page 2014

  7. [6]

    Wafic Chahine, Nour Hachem, and Nadine Marie Moacdieh. 2023. Eye Tracking- Based Adaptive Displays: A Review of the Recent Literature. Proceedings of the Human Factors and Ergonomics Society Annual Meeting 67, 1 (Sept. 2023), 927–932. Publisher: SAGE Publications Inc

    work page 2023

  8. [7]

    Zenghai Chen, Hong Fu, Wai-Lun Lo, and Zheru Chi. 2018. Strabis- mus Recognition Using Eye-Tracking Data and Convolutional Neural Net- works. Journal of Healthcare Engineering 2018, 1 (2018), 7692198. _eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1155/2018/7692198

    work page doi:10.1155/2018/7692198 2018

Show all 56 references
  1. [8]

    Crewther

    Deena Ebaid and Sheila G. Crewther. 2020. The Contribution of Oculomotor Functions to Rates of Visual Information Processing in Younger and Older Adults. Scientific Reports 10, 1 (June 2020), 10129. Publisher: Nature Publishing Group

    2020

  2. [9]

    Y. Ebisawa. 1998. Improved video-based eye-gaze detection method. IEEE Trans- actions on Instrumentation and Measurement 47, 4 (Aug. 1998), 948–955

    1998

  3. [10]

    Thomas Eggert. 2007. Eye Movement Recordings: Methods.Neuro-Ophthalmology 40 (2007), 15–34. Publisher: Karger Publishers

    2007

  4. [11]

    Estrany, P

    B. Estrany, P. Fuster, A. Garcia, and Y. Luo. 2008. Human computer interface by EOG tracking. In Proceedings of the 1st international conference on PErvasive Tech- nologies Related to Assistive Environments (PETRA ’08). Association for Computing Machinery, New York, NY, USA, 1–9

    2008

  5. [12]

    Jiahao Fan, Chenglu Sun, Meng Long, Chen Chen, and Wei Chen. 2021. EOGNET: A Novel Deep Learning Model for Sleep Stage Classification Based on Single- Channel EOG Signal. Frontiers in Neuroscience 15 (July 2021). Publisher: Frontiers

    2021

  6. [13]

    Dimitris Giakoumis, Dimitrios Tzovaras, and George Hassapis. 2013. Subject- dependent biosignal features for increased accuracy in psychological stress de- tection. International Journal of Human-Computer Studies 71, 4 (April 2013), 425–439

    2013

  7. [14]

    Jesse Grootjen, Alexandra Sipatchin, Siegfried Wahl, Tonja-Katrin Machulla, Lewis Chuang, and Thomas Kosch. 2023. Assessing Eye Tracking for Continuous Central Field Loss Monitoring. In Proceedings of the 22nd International Conference on Mobile and Ubiquitous Multimedia (MUM ’...

    2023

  8. [15]

    Grossniklaus, John M

    Hans E. Grossniklaus, John M. Nickerson, Henry F. Edelhauser, Louise A. M. K. Bergman, and Lennart Berglin. 2013. Anatomic Alterations in Aging and Age- Related Diseases of the Eye. Investigative Ophthalmology & Visual Science 54, 14 (Dec. 2013), ORSF23–ORSF27

    2013

  9. [16]

    Dan Witzner Hansen and Qiang Ji. 2010. In the Eye of the Beholder: A Survey of Models for Eyes and Gaze. IEEE Transactions on Pattern Analysis and Machine Intelligence 32, 3 (March 2010), 478–500

    2010

  10. [17]

    Hopman, Isabella Irrcher, and Mark D

    Hassan Hazari, Rachel Curtis, Karen Eden, Wilma M. Hopman, Isabella Irrcher, and Mark D. Bona. 2022. Validation of the visual acuity iPad app Eye Chart Pro compared to the standard Early Treatment Diabetic Retinopathy Study chart in a low-vision population. Journal of Telemedi...

    2022

  11. [18]

    Hong Hua, Prasanna Krishnaswamy, and Jannick P. Rolland. 2006. Video-based eyetracking methods and algorithms in head-mounted displays. Optics Express 14, 10 (May 2006), 4328–4350. Publisher: Optica Publishing Group

    2006

  12. [19]

    Gan Huang, Zhiheng Zhao, Shaorong Zhang, Zhenxing Hu, Jiaming Fan, Meisong Fu, Jiale Chen, Yaqiong Xiao, Jun Wang, and Guo Dan. 2023. Discrepancy between inter- and intra-subject variability in EEG-based motor imagery brain-computer interface: Evidence from multiple perspectiv...

    2023

  13. [20]

    Robert J. K. Jacob and Keith S. Karn. 2003. Commentary on Section 4 - Eye Tracking in Human-Computer Interaction and Usability Research: Ready to Deliver the Promises. In The Mind’s Eye , J. Hyönä, R. Radach, and H. Deubel (Eds.). North-Holland, Amsterdam, 573–605

    2003

  14. [21]

    Moritz Kassner, William Patera, and Andreas Bulling. 2014. Pupil: An Open Source Platform for Pervasive Eye Tracking and Mobile Gaze-based Interaction

    2014

  15. [22]

    Ceren Kaya, Okan Erkaymaz, Orhan Ayar, and Mahmut Özer. 2018. Classification of refractive disorders from electrooculogram (EOG) signals by using data min- ing techniques. In 2018 26th Signal Processing and Communications Applications Conference (SIU). 1–4

    2018

  16. [23]

    Carla Lanca, Chi Pui Pang, and Andrzej Grzybowski. 2023. Editorial: Refractive errors: public health challenges and interventions. Frontiers in Public Health 11 (Oct. 2023), 1289173

    2023

  17. [24]

    Erik Lindén. 2021. Calibration in deep-learning eye tracking. (2021). https://urn. kb.se/resolve?urn=urn:nbn:se:kth:diva-295566 Publisher: KTH Royal Institute of Technology

    2021

  18. [25]

    ELWIN MARG. 1951. DEVELOPMENT OF ELECTRO-OCULOGRAPHY: Stand- ing Potential of the Eye in Registration of Eye Movement. A.M.A. Archives of Ophthalmology 45, 2 (Feb. 1951), 169–185

    1951

  19. [26]

    Baker, and Galit Yovel

    Eyal Mehoudar, Joseph Arizpe, Chris I. Baker, and Galit Yovel. 2014. Faces in the eye of the beholder: Unique and stable eye scanning patterns of individual observers. Journal of Vision 14, 7 (June 2014), 6

    2014

  20. [27]

    Kateryna Melnyk, Lee Friedman, Dmytro Katrychuk, and Oleg Komogortsev

  21. [28]

    Ayako Miyahira, Kiichiro Morita, Hiroshi Yamaguchi, Yoshifumi Morita, and Hisao Maeda. 2000. Gender differences and reproducibility in exploratory eye movements of normal subjects. Psychiatry and Clinical Neurosciences 54, 1 (2000), 31–36. _eprint: https://onlinelibrary.wiley....

    2000

  22. [29]

    O. H. Mowrer, T. C. Ruch, and N. E. Miller. 1935. The corneo-retinal potential difference as the basis of the galvanometric method of recording eye movements. American Journal of Physiology-Legacy Content 114, 2 (Dec. 1935), 423–428. doi:10. 1152/ajplegacy.1935.114.2.423 Publi...

    1935

  23. [30]

    D Munoz, J Broughton, J Goldring, and Irene Armstrong. 1998. Age-related performance of human subjects on saccadic eye movements. Experimental brain research. Experimentelle Hirnforschung. Expérimentation cérébrale 121 (Sept. 1998), 391–400

    1998

  24. [31]

    Geethika Muralidharan, Eduardo Martínez-Enríquez, Judith Birkenfeld, Miriam Velasco-Ocana, Pablo Pérez-Merino, and Susana Marcos. 2019. Morphological changes of human crystalline lens in myopia. Biomedical Optics Express 10, 12 Conference acronym ’XX, June 03–05, 2018, Woodsto...

    2019

  25. [32]

    Naidoo, Janet Leasher, Rupert R

    Kovin S. Naidoo, Janet Leasher, Rupert R. Bourne, Seth R. Flaxman, Jost B. Jonas, Jill Keeffe, Hans Limburg, Konrad Pesudovs, Holly Price, Richard A. White, Tien Y. Wong, Hugh R. Taylor, Serge Resnikoff, and for the Vision Loss Expert Group of the Global Burden of Disease Stud...

    2016

  26. [33]

    Barath S Narayan, Sreyas Janamanchi, Achintya Harsha, Nitheezkant R, Prashanth Jonna, and Madhav Rao. 2024. EOG-Assist: Real-Time EOG-Based Eye Tracking for Assistive Systems. In 2024 18th International Conference on Control, Automation, Robotics and Vision (ICARCV) . 795–800....

    2024

  27. [34]

    National Eye Institute. [n. d.]. Types of refractive errors. https://www. nei.nih.gov/learn-about-eye-health/eye-conditions-and-diseases/refractive- errors/types-refractive-errors

  28. [35]

    Nisha Nixon, Peter B. M. Thomas, and Pete R. Jones. 2023. Feasibility study of an automated Strabismus screening Test using Augmented Reality and Eye-tracking (STARE). Eye 37, 17 (Dec. 2023), 3609–3614. Publisher: Nature Publishing Group

    2023

  29. [36]

    Koki Ono, Kiyoaki Arakawa, Daisuke Yamada, and Keigo Matsumoto. 2023. A scoping review of eye-tracking metrics as an indicator of negative mental health- related outcomes and its possible applicability in remote work situations. Envi- ronmental and Occupational Health Practice...

    2023

  30. [37]

    Shuyi Ouyang, Chunwen Zheng, Zhanjie Lin, Xiaoni Zhang, Haojun Li, Ying Fang, Yijun Hu, Honghua Yu, and Guanrong Wu. 2022. Risk factors of falls in elderly patients with visual impairment. Frontiers in Public Health 10 (Aug. 2022), 984199

    2022

  31. [38]

    Tuan D. Pham. 2021. Time–frequency time–space LSTM for robust classification of physiological signals. Scientific Reports 11, 1 (March 2021), 6936. Publisher: Nature Publishing Group

    2021

  32. [39]

    Naidoo, and Ving Fai Chan

    Govender-Poonsamy Pirindhavellie, Ai Chee Yong, Khathutshelo Percy Mashige, Kovin S. Naidoo, and Ving Fai Chan. 2023. The impact of spectacle correction on the well-being of children with vision impairment due to uncorrected refractive error: a systematic review. BMC Public He...

    2023

  33. [40]

    Ethan Pirso, Jude Mitchell, and Curtis Baker. 2024. MEASURING REFRACTIVE ERROR USING CONTINUOUS PSYCHOPHYSICS AND EYE TRACKING. Journal of Vision 24, 10 (Sept. 2024), 1459

    2024

  34. [41]

    Moniz, Mohamed Akrout, and Allon Jacobs

    Prethy Rao, Rajeev Ramchandran, Kira Baldonado, Stephan Hüsler, Marina Sutter, Larissa S. Moniz, Mohamed Akrout, and Allon Jacobs. 2024. Patient perspectives on accessing eye-related healthcare from rural communities. Eye 38, 17 (Dec. 2024), 3389–3391. Publisher: Nature Publis...

    2024

  35. [42]

    Bullimore, Loraine T

    Kathryn Richdale, Mark A. Bullimore, Loraine T. Sinnott, and Karla Zadnik. 2016. The Effect of Age, Accommodation and Refractive Error on the Adult Human Eye. Optometry and vision science : official publication of the American Academy of Optometry 93, 1 (Jan. 2016), 3–11

    2016

  36. [43]

    Raghu Reddy

    Shivang Shekar, Pranav Reddy Pesaladinne, Sai Anirudh Karre, and Y. Raghu Reddy. 2020. VREye: Exploring Human Visual Acuity Test Using Virtual Reality. In Virtual, Augmented and Mixed Reality. Industrial and Everyday Life Applications, Jessie Y. C. Chen and Gino Fragomeni (Eds...

    2020

  37. [44]

    Vargo, Aman Gupta, George Chernyshov, Kai Kunze, and Tilman Dingler

    Benjamin Tag, Andrew W. Vargo, Aman Gupta, George Chernyshov, Kai Kunze, and Tilman Dingler. 2019. Continuous Alertness Assessments: Using EOG Glasses to Unobtrusively Monitor Fatigue Levels In-The-Wild. In Proceedings of the 2019 CHI Conference on Human Factors in Computing S...

    2019

  38. [45]

    Toner, Michael J

    Keri N. Toner, Michael J. Lynn, T. Rowan Candy, and Amy K. Hutchinson. 2014. The Handy Eye Check: a mobile medical application to test visual acuity in children. Journal of the American Association for Pediatric Ophthalmology and Strabismus (JAAPOS) 18, 3 (2014), 258–260. Publ...

    2014

  39. [46]

    Andrés Ubeda, Eduardo Iañez, and José M. Azorin. 2011. Wireless and Portable EOG-Based Interface for Assisting Disabled People. IEEE/ASME Transactions on Mechatronics 16, 5 (Oct. 2011), 870–873

    2011

  40. [47]

    Robison, and Ashley L

    Nash Unsworth, Matthew K. Robison, and Ashley L. Miller. 2019. Individual differences in baseline oculometrics: Examining variation in baseline pupil di- ameter, spontaneous eye blink rate, and fixation stability. Cognitive, Affective, & Behavioral Neuroscience 19, 4 (Aug. 201...

    2019

  41. [48]

    Dyuthi S Varier and Venkatasubramanian Krishnamoorthy. 2014. An electroocu- logram based real time system for measurement and analysis of visual stimuli for detecting strabismus and nystagmus. In 2014 International Conference on Advances in Computing, Communications and Inform...

    2014

  42. [49]

    Kerri Walter and Peter Bex. 2021. Cognitive load influences oculomotor behavior in natural scenes. Scientific Reports 11, 1 (June 2021), 12405. Publisher: Nature Publishing Group

    2021

  43. [50]

    Ru Wang, Ruijia Chen, Anqiao Erica Cai, Zhiyuan Li, Sanbrita Mondal, and Yuhang Zhao. 2025. Characterizing Visual Intents for People with Low Vision through Eye Tracking

    2025

  44. [51]

    Xin Wei, Huakun Liu, Monica Perusquía-Hernández, Katsutoshi Masai, Naoya Isoyama, Hideaki Uchiyama, and Kiyoshi Kiyokawa. 2023. Unobtrusive Refractive Power Monitoring: Using EOG to Detect Blurred Vision. In 2023 45th Annual International Conference of the IEEE Engineering in ...

    2023

  45. [53]

    Robert P. L. Wisse, Marc B. Muijzer, Francesco Cassano, Daniel A. Godefrooij, Yves F. D. M. Prevoo, and Nienke Soeters. 2019. Validation of an Independent Web-Based Tool for Measuring Visual Acuity and Refractive Error (the Manifest versus Online Refractive Evaluation Trial): ...

    2019

  46. [54]

    Grzegorz Zieliński, Anna Matysik-Woźniak, Anna Pankowska, Radosław Pietura, Robert Rejdak, and Kamil Jonak. 2023. High Myopia and Thickness of Extraocular and Masticatory Muscles—7T MRI, Preliminary Study.Journal of Clinical Medicine 12, 12 (June 2023), 4166

    2023

  47. [55]

    Grzegorz Zieliński, Marcin Wójcicki, Maria Rapa, Anna Matysik-Woźniak, Michał Baszczowski, Michał Ginszt, Monika Litko-Rola, Jacek Szkutnik, Ingrid Różyło- Kalinowska, Robert Rejdak, and Piotr Gawda. 2022. Masticatory Muscle Thick- ness and Activity Correlates to Eyeball Lengt...

    2022

  48. [2025]

    arXiv:2501.00597 [cs]

    Gaze Prediction as a Function of Eye Movement Type and Individual Differences. arXiv:2501.00597 [cs]

    arXiv

Pith tools

Reviewed August 7, 2026 · model on record in the stance chip above.