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EyeBrain: Left and Right Brain Lateralization Activity Classification Through Pupil Diameter and Fixation Duration

T0 review · 2 major / 0 minor · reviewed 2026-05-08 · grok-4.3

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Pith's one-line read Pupil diameter and fixation duration can classify left versus right brain hemisphere activity with an F1 score of 0.894.

desk verdict Pupil diameter and fixation duration separate the chosen left- and right-hemisphere tasks at F1 0.894, but the mapping to actual lateralization rests on untested assumptions about the tasks. read the letter →

arxiv 2604.23562 v1 submitted 2026-04-26 q-bio.NC cs.AIcs.HC

Ko Watanabe , Pooja Pol , Nicolas Gro{ss}mann , Shoya Ishimaru , Andreas Dengel This is my paper · ORCID
classification q-bio.NCcs.AIcs.HC
keywords eye-trackingbrainlateralizationpupildiameterfixationdurationclassificationcognitivemonitoringneurorehabilitationhemisphereactivity
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

The paper establishes that eye-tracking data on pupil size and fixation duration reliably distinguish activity dominated by the left brain hemisphere from that dominated by the right. Left-hemisphere tasks like language and arithmetic produce different ocular patterns than right-hemisphere tasks like drawing or music perception. The authors train a classifier on these metrics during controlled tasks and report strong performance. A reader would care because this points to a low-cost, non-invasive way to monitor which side of the brain is engaged without brain imaging. The work positions these eye signals as practical indicators for applications in cognitive tracking and recovery.

What carries the argument

Binary classification model trained on pupil diameter and fixation duration extracted from eye-tracking recordings during tasks that selectively engage one hemisphere.

What would settle it

Re-running the classification on a new dataset where left- and right-hemisphere tasks are matched for difficulty and arousal levels but the model accuracy falls to near chance.

Watch

Extended reading notes

Core claim

The paper demonstrates that pupil diameter and fixation duration can effectively classify left and right brain hemisphere activities. We obtained a considerably high classification performance, with an F1 score of 0.894. The results suggest that ocular metrics are robust indicators of lateralized brain activity and can be applied in cognitive monitoring and neurorehabilitation.

Load-bearing premise

The chosen tasks activate only one hemisphere at a time and the eye metrics directly reflect that lateralization instead of task difficulty, effort, or general arousal.

Editorial extensions

If this is right

  • Supports non-invasive monitoring of brain lateralization during cognitive tasks.
  • Opens use in neurorehabilitation settings to track recovery of hemispheric function.
  • Enables integration into real-time applications for ongoing cognitive assessment.
  • Extends to broader domains of cognitive and neurological monitoring without specialized equipment.

Reading between the lines

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

  • Wearable eye trackers could one day provide continuous at-home feedback on hemispheric balance.
  • The same signals might help detect imbalances in conditions that disrupt typical lateralization.
  • Further experiments could test whether these metrics also track shifts in attention or fatigue within the same hemisphere.
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Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

2 major / 0 minor

Summary. The manuscript claims that pupil diameter and fixation duration measured via eye-tracking can classify left-hemisphere (language/arithmetic) versus right-hemisphere (drawing/music) brain activity, achieving an F1 score of 0.894, and positions these ocular metrics as robust indicators suitable for cognitive monitoring and neurorehabilitation applications.

Significance. If the central mapping from eye metrics to selective hemispheric activation holds after proper controls, the work could enable low-cost, non-invasive monitoring of lateralized cognitive function with potential utility in neurorehabilitation and real-time applications. The absence of methodological detail prevents assessment of whether this potential is realized.

major comments (2)
  1. [Abstract] Abstract: The reported F1 score of 0.894 is presented with zero information on participant numbers, task design details, cross-validation procedure, baseline comparisons, or statistical controls for confounds (e.g., arousal, difficulty, cognitive load). Without these, it is impossible to determine whether classification performance tracks hemispheric lateralization or shared non-lateralized factors.
  2. [Abstract] The ground-truth labeling of tasks as selectively left- or right-lateralized rests on unvalidated assumptions; no prior fMRI/EEG validation, difficulty-matched controls, or ablation experiments are described to show that performance collapses when non-lateralized variables are equalized.

Simulated Author's Rebuttal

2 responses · 1 unresolved

We thank the referee for the detailed and constructive review. We address each major comment below and have revised the manuscript accordingly to improve clarity and rigor.

read point-by-point responses

  1. Referee: [Abstract] Abstract: The reported F1 score of 0.894 is presented with zero information on participant numbers, task design details, cross-validation procedure, baseline comparisons, or statistical controls for confounds (e.g., arousal, difficulty, cognitive load). Without these, it is impossible to determine whether classification performance tracks hemispheric lateralization or shared non-lateralized factors.

    Authors: We agree that the original abstract omitted critical methodological details required to evaluate the results. In the revised manuscript we have expanded the abstract to report participant numbers, task design (language/arithmetic vs. drawing/music), cross-validation procedure, baseline comparisons, and controls for confounds such as arousal, difficulty, and cognitive load. A new Methods section now provides the full experimental protocol, statistical analysis pipeline, and explicit discussion of how non-lateralized factors were addressed. revision: yes

  2. Referee: [Abstract] The ground-truth labeling of tasks as selectively left- or right-lateralized rests on unvalidated assumptions; no prior fMRI/EEG validation, difficulty-matched controls, or ablation experiments are described to show that performance collapses when non-lateralized variables are equalized.

    Authors: Task selection follows well-established findings on hemispheric specialization, but we acknowledge the original text did not sufficiently document supporting evidence or controls. The revision adds citations to prior fMRI and EEG literature validating lateralization of the chosen tasks, describes how tasks were matched for difficulty and cognitive load, and includes additional analyses examining the unique contribution of pupil diameter and fixation duration. Dedicated ablation experiments or new within-subject fMRI/EEG validation, however, would require fresh data collection and are noted as future work. revision: partial

standing simulated objections not resolved
  • New fMRI/EEG validation or full ablation experiments that would require additional participant recruitment and neuroimaging sessions beyond the existing dataset.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: empirical classification with independent task labels and standard ML evaluation

full rationale

The paper reports a supervised classification experiment (pupil diameter + fixation duration → left/right hemisphere label) that yields F1=0.894. Labels are assigned by task type (language/arithmetic vs. drawing/music) rather than derived from the eye metrics themselves. No equations, fitted parameters renamed as predictions, self-citations used as uniqueness theorems, or ansatzes appear in the provided text. The derivation chain is therefore a conventional train/test pipeline whose output is not forced by construction from its inputs. The validity of the task-to-hemisphere mapping is an external empirical assumption, not a self-referential reduction.

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

Only the abstract is available; no methods, equations, or data details are provided, so the ledger cannot be populated with concrete free parameters, axioms, or invented entities from the paper.

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Cite this review

Pith. "Pith review of EyeBrain: Left and Right Brain Lateralization Activity Classification Through Pupil Diameter and Fixation Duration." pith.science (2026). https://pith.science/paper/2604.23562

@misc{pith2026260423562,
  author       = {Pith},
  title        = {Pith review of: EyeBrain: Left and Right Brain Lateralization Activity Classification Through Pupil Diameter and Fixation Duration},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/2604.23562}},
  note         = {Machine review of arXiv:2604.23562}
}
read the original abstract

The relationship between brain lateralization and cognitive functions is well-documented. The left hemisphere primarily handles tasks such as language and arithmetic, while the right hemisphere is involved in creative activities like drawing and music perception. Eye-tracking technology has shown the potential to reveal cognitive states by measuring ocular metrics such as pupil diameter and fixation duration. However, the ability to distinguish lateralized brain activity using these ocular metrics remains underexplored. Here, we demonstrate that pupil diameter and fixation duration can effectively classify left and right brain hemisphere activities. We obtained a considerably high classification performance, with an F1 score of 0.894. The results suggest that ocular metrics are robust indicators of lateralized brain activity and can be applied in cognitive monitoring and neurorehabilitation. Our future work expands on this by integrating these methods into real-time applications EyeBrain, potentially broadening their use across various cognitive and neurological domains.

Figures

Figures reproduced from arXiv: 2604.23562 by the authors.

Figure 1
Figure 1. Concept design of EyeBrain project. Eye-tracking technology makes statistical information on brain lateralization accessible and easy to understand. Users can understand brain activities as an activity monitoring application. The relationship between brain lateralization and cognitive functions is well-documented. The left hemisphere primarily handles tasks such as language and arithmetic, while the right hemisphere… view at source ↗
Figure 2
Figure 2. Experiment flow. Procedure with step-wise screenshots of the computer during any session of data collection. view at source ↗
Figure 3
Figure 3. Variation of raw and preprocessed pupil diameter signal over time. view at source ↗
Figures from the paper (4 more)
Figure 4
Figure 4. Figure 4: Variation in weighted average F1 score with activity duration for machine-learning models.
Figure 5
Figure 5. Figure 5: Confusion matrix of leave-one-participant-out cross-validation (LOPOCV) for the best performing window size of 90 seconds
Figure 6
Figure 6. Figure 6: Confusion matrix of leave-one-activity-out cross-validation (LOAOCV) for three window sizes visualized for XGBoost classifier.
Figure 7
Figure 7. Figure 7: Top ten most important features according to XGBoost classification score.

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Works this paper leans on

77 extracted references · 77 canonical work pages

  1. [1]

    Tobii AB. 2024. Specifications for the Tobii Eye Tracker 4C. https://help.tobii.com/hc/en-us/articles/213414285-Specifications-for-the-Tobii-Eye- Tracker-4C

    work page arXiv 2024

  2. [2]

    Evgeniy Abdulin and Oleg Komogortsev. 2015. User Eye Fatigue Detection via Eye Movement Behavior. InProceedings of the 33rd Annual ACM Conference Extended Abstracts on Human Factors in Computing Systems(Seoul, Republic of Korea)(CHI EA ’15). Association for Computing Machinery, New York, NY, USA, 1265–1270. https://doi.org/10.1145/2702613.2732812

    work page doi:10.1145/2702613.2732812 2015

  3. [3]

    Samira Aminihajibashi, Thomas Hagen, Maja Dyhre Foldal, Bruno Laeng, and Thomas Espeseth. 2019. Individual differences in resting-state pupil size: Evidence for association between working memory capacity and pupil size variability.International Journal of Psychophysiology140 (2019), 1–7

    work page 2019

  4. [4]

    Gehrer, Krzysztof Krejtz, Andrew T

    Davide Bacchin, Nina A. Gehrer, Krzysztof Krejtz, Andrew T. Duchowski, and Luciano Gamberini. 2023. Gaze-based Metrics of Cognitive Load in a Conjunctive Visual Memory Task. InExtended Abstracts of the 2023 CHI Conference on Human Factors in Computing Systems(Hamburg, Germany) (CHI EA ’23). Association for Computing Machinery, New York, NY, USA, Article 1...

    work page doi:10.1145/3544549.3585650 2023

  5. [5]

    Shlomo Berkovsky, Ronnie Taib, Irena Koprinska, Eileen Wang, Yucheng Zeng, Jingjie Li, and Sabina Kleitman. 2019. Detecting Personality Traits Using Eye-Tracking Data. InProceedings of the 2019 CHI Conference on Human Factors in Computing Systems(Glasgow, Scotland Uk)(CHI ’19). Association for Computing Machinery, New York, NY, USA, 1–12. https://doi.org/...

    work page doi:10.1145/3290605.3300451 2019

  6. [6]

    Jeffrey Binder. 2000. The new neuroanatomy of speech perception. , 2371–2372 pages

    work page 2000

  7. [7]

    Iuliia Brishtel, Anam Ahmad Khan, Thomas Schmidt, Tilman Dingler, Shoya Ishimaru, and Andreas Dengel. 2020. Mind wandering in a multimodal reading setting: Behavior analysis & automatic detection using eye-tracking and an eda sensor.Sensors20, 9 (2020), 2546

    work page 2020

  8. [8]

    Andreas Bulling, Jamie A Ward, Hans Gellersen, and Gerhard Tröster. 2009. Eye movement analysis for activity recognition. InProceedings of the 11th international conference on Ubiquitous computing. 41–50

    work page 2009

Show all 77 references
  1. [9]

    Roxanne L Canosa. 2009. Real-world vision: Selective perception and task.ACM Transactions on Applied Perception (TAP)6, 2 (2009), 1–34

    2009

  2. [10]

    Youngjun Cho. 2021. Rethinking Eye-blink: Assessing Task Difficulty through Physiological Representation of Spontaneous Blinking. InProceedings of the 2021 CHI Conference on Human Factors in Computing Systems(Yokohama, Japan)(CHI ’21). Association for Computing Machinery, New ...

    2021 doi

  3. [11]

    Michael C Corballis. 2014. Left brain, right brain: facts and fantasies.PLoS biology12, 1 (2014), e1001767

    2014

  4. [12]

    Inès Daguet, Didier Bouhassira, and Claude Gronfier. 2019. Baseline pupil diameter is not a reliable biomarker of subjective sleepiness.Frontiers in Neurology10 (2019), 108

    2019

  5. [13]

    Mirella Dapretto and Susan Y Bookheimer. 1999. Form and content: dissociating syntax and semantics in sentence comprehension.Neuron24, 2 (1999), 427–432

    1999

  6. [14]

    Duchowski, Krzysztof Krejtz, Izabela Krejtz, Cezary Biele, Anna Niedzielska, Peter Kiefer, Martin Raubal, and Ioannis Giannopoulos

    Andrew T. Duchowski, Krzysztof Krejtz, Izabela Krejtz, Cezary Biele, Anna Niedzielska, Peter Kiefer, Martin Raubal, and Ioannis Giannopoulos

  7. [15]

    InProceedings of the 2018 CHI Conference on Human Factors in Computing Systems(Montreal QC, Canada)(CHI ’18)

    The Index of Pupillary Activity: Measuring Cognitive Load vis-à-vis Task Difficulty with Pupil Oscillation. InProceedings of the 2018 CHI Conference on Human Factors in Computing Systems(Montreal QC, Canada)(CHI ’18). Association for Computing Machinery, New York, NY, USA, 1–1...

    2018 doi

  8. [16]

    Margaret G Funnell, Mary K Colvin, and Michael S Gazzaniga. 2007. The calculating hemispheres: Studies of a split-brain patient.Neuropsychologia 45, 10 (2007), 2378–2386

    2007

  9. [17]

    Hristijan Gjoreski, Ifigeneia Mavridou, James Archer William Archer, Andrew Cleal, Simon Stankoski, Ivana Kiprijanovska, Mohsen Fatoorechi, Piotr Walas, John Broulidakis, Martin Gjoreski, and Charles Nduka. 2023. OCOsense Glasses – Monitoring Facial Gestures and Expressions fo...

    2023 doi

  10. [18]

    1983.Music and the right hemisphere

    H Gordon. 1983.Music and the right hemisphere. Vol. 3. Academic Press London

    1983

  11. [19]

    Janine Grimmer, Laura Simon, and Jan Ehlers. 2021. The Cognitive Eye: Indexing Oculomotor Functions for Mental Workload Assessment in Cognition-Aware Systems. InExtended Abstracts of the 2021 CHI Conference on Human Factors in Computing Systems(Yokohama, Japan)(CHI EA ’21). As...

    2021 doi

  12. [20]

    Grootjen, Henrike Weingärtner, and Sven Mayer

    Jesse W. Grootjen, Henrike Weingärtner, and Sven Mayer. 2024. Uncovering and Addressing Blink-Related Challenges in Using Eye Tracking for Interactive Systems. InProceedings of the CHI Conference on Human Factors in Computing Systems(Honolulu, HI, USA)(CHI ’24). Association fo...

    2024 doi

  13. [21]

    Yoichiro Hisadome, Tianyi Wu, Jiawei Qin, and Yusuke Sugano. 2024. Rotation-Constrained Cross-View Feature Fusion for Multi-View Appearance- Based Gaze Estimation. InProceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (W ACV). 5985–5994

    2024

  14. [22]

    Andrew Housholder, Jonathan Reaban, Aira Peregrino, Georgia Votta, and Tauheed Khan Mohd. 2021. Evaluating accuracy of the Tobii eye tracker

    2021

  15. [23]

    Springer, 379–390

    InInternational Conference on Intelligent Human Computer Interaction. Springer, 379–390

  16. [24]

    Brockmole, and Sidney K

    Stephen Hutt, Kristina Krasich, James R. Brockmole, and Sidney K. D’Mello. 2021. Breaking out of the Lab: Mitigating Mind Wandering with Gaze- Based Attention-Aware Technology in Classrooms. InProceedings of the 2021 CHI Conference on Human Factors in Computing Systems(Yokoham...

    2021 doi

  17. [25]

    Shoya Ishimaru, Kai Kunze, Koichi Kise, Jens Weppner, Andreas Dengel, Paul Lukowicz, and Andreas Bulling. 2014. In the blink of an eye: combining head motion and eye blink frequency for activity recognition with google glass. InProceedings of the 5th augmented human internatio...

    2014

  18. [26]

    Hassan Ismail Fawaz, Germain Forestier, Jonathan Weber, Lhassane Idoumghar, and Pierre-Alain Muller. 2019. Deep learning for time series classification: a review.Data mining and knowledge discovery33, 4 (2019), 917–963

    2019

  19. [27]

    Siddhartha Joshi and Joshua I Gold. 2020. Pupil size as a window on neural substrates of cognition.Trends in cognitive sciences24, 6 (2020), 466–480

    2020

  20. [28]

    Siddhartha Joshi, Yin Li, Rishi M Kalwani, and Joshua I Gold. 2016. Relationships between pupil diameter and neuronal activity in the locus coeruleus, colliculi, and cingulate cortex.Neuron89, 1 (2016), 221–234

    2016

  21. [29]

    Daniel Kahneman and Jackson Beatty. 1966. Pupil diameter and load on memory.Science154, 3756 (1966), 1583–1585

    1966

  22. [30]

    Ard Kastrati, Martyna Beata Plomecka, Roger Wattenhofer, and Nicolas Langer. 2021. Using deep learning to classify saccade direction from brain activity. InACM Symposium on Eye Tracking Research and Applications. 1–6

    2021

  23. [31]

    Andrew Kirk and Andrew Kertesz. 1989. Hemispheric contributions to drawing.Neuropsychologia27, 6 (1989), 881–886

    1989

  24. [32]

    Xiang-Zhen Kong, Samuel R Mathias, Tulio Guadalupe, ENIGMA Laterality Working Group, David C Glahn, Barbara Franke, Fabrice Crivello, Nathalie Tzourio-Mazoyer, Simon E Fisher, Paul M Thompson, et al. 2018. Mapping cortical brain asymmetry in 17,141 healthy individuals worldwid...

    2018

  25. [33]

    Xiang-Zhen Kong, Merel C Postema, Tulio Guadalupe, Carolien de Kovel, Premika SW Boedhoe, Martine Hoogman, Samuel R Mathias, Daan Van Rooij, Dick Schijven, David C Glahn, et al. 2022. Mapping brain asymmetry in health and disease through the ENIGMA consortium.Human brain mappi...

    2022

  26. [34]

    Thomas Kosch, Mariam Hassib, Daniel Buschek, and Albrecht Schmidt. 2018. Look into my Eyes: Using Pupil Dilation to Estimate Mental Workload for Task Complexity Adaptation. InExtended Abstracts of the 2018 CHI Conference on Human Factors in Computing Systems(Montreal QC, Canad...

    2018 doi

  27. [35]

    Krzysztof Krejtz, Andrew T Duchowski, Anna Niedzielska, Cezary Biele, and Izabela Krejtz. 2018. Eye tracking cognitive load using pupil diameter and microsaccades with fixed gaze.PloS one13, 9 (2018), e0203629

    2018

  28. [36]

    Mariska E Kret and Elio E Sjak-Shie. 2019. Preprocessing pupil size data: Guidelines and code.Behavior research methods51 (2019), 1336–1342

    2019

  29. [37]

    Michal T Kucewicz, Jaromir Dolezal, Vaclav Kremen, Brent M Berry, Laura R Miller, Abigail L Magee, Vratislav Fabian, and Gregory A Worrell. 2018. Pupil size reflects successful encoding and recall of memory in humans.Scientific reports8, 1 (2018), 4949

    2018

  30. [38]

    Sharon Leal and Aldert Vrij. 2008. Blinking during and after lying.Journal of Nonverbal Behavior32 (2008), 187–194

    2008

  31. [39]

    Jia Zheng Lim, James Mountstephens, and Jason Teo. 2022. Eye-tracking feature extraction for biometric machine learning.Frontiers in neurorobotics 15 (2022), 796895

    2022

  32. [40]

    Simon P Liversedge and John M Findlay. 2000. Saccadic eye movements and cognition.Trends in cognitive sciences4, 1 (2000), 6–14

    2000

  33. [41]

    Anat London, Inbal Benhar, and Michal Schwartz. 2013. The retina as a window to the brain—from eye research to CNS disorders.Nature Reviews Neurology9, 1 (2013), 44–53

    2013

  34. [42]

    Samantha Mann, Aldert Vrij, and Ray Bull. 2002. Suspects, lies, and videotape: An analysis of authentic high-stake liars.Law and human behavior 26 (2002), 365–376

    2002

  35. [43]

    Sebastiaan Mathôt, Jasper Fabius, Elle Van Heusden, and Stefan Van der Stigchel. 2018. Safe and sensible preprocessing and baseline correction of pupil-size data.Behavior research methods50 (2018), 94–106

    2018

  36. [44]

    Tomoyo Morita, Minoru Asada, and Eiichi Naito. 2020. Right-hemispheric dominance in self-body recognition is altered in left-handed individuals. Neuroscience425 (2020), 68–89

    2020

  37. [45]

    Peter R Murphy, Redmond G O’connell, Michael O’sullivan, Ian H Robertson, and Joshua H Balsters. 2014. Pupil diameter covaries with BOLD activity in human locus coeruleus.Human brain mapping35, 8 (2014), 4140–4154

    2014

  38. [46]

    Shivsevak Negi and Ritayan Mitra. 2020. Fixation duration and the learning process: An eye tracking study with subtitled videos.Journal of Eye Movement Research13, 6 (2020)

    2020

  39. [47]

    Simon Neubauer, Philipp Gunz, Nadia A Scott, Jean-Jacques Hublin, and Philipp Mitteroecker. 2020. Evolution of brain lateralization: A shared hominid pattern of endocranial asymmetry is much more variable in humans than in great apes.Science Advances6, 7 (2020), eaax9935

    2020

  40. [48]

    Nikolai Nikolaenko. 2003. Artistic thinking and cerebral asymmetry.Acta Neuropsychologica1, 2 (2003)

    2003

  41. [49]

    Cladek, Mindy K

    Emma Ning, Andrea T. Cladek, Mindy K. Ross, Sarah Kabir, Amruta Barve, Ellyn Kennelly, Faraz Hussain, Jennifer Duffecy, Scott L. Langenecker, Theresa Nguyen, Theja Tulabandhula, John Zulueta, Olusola A. Ajilore, Alexander P. Demos, and Alex Leow. 2023. Smartphone-derived Virtu...

    2023 doi

  42. [50]

    Sou Nobukawa, Aya Shirama, Tetsuya Takahashi, Toshinobu Takeda, Haruhisa Ohta, Mitsuru Kikuchi, Akira Iwanami, Nobumasa Kato, and Shigenobu Toda. 2021. Pupillometric complexity and symmetricity follow inverted-U curves against baseline diameter due to crossed locus coeruleus p...

    2021

  43. [51]

    2024.The lateralized brain: The neuroscience and evolution of hemispheric asymmetries

    Sebastian Ocklenburg and Onur Güntürkün. 2024.The lateralized brain: The neuroscience and evolution of hemispheric asymmetries. Elsevier

    2024

  44. [52]

    Manuel Oliva and Andrey Anikin. 2018. Pupil dilation reflects the time course of emotion recognition in human vocalizations.Scientific reports8, 1 (2018), 4871. 19 Conference acronym ’XX, June 03–05, 2018, Woodstock, NY Watanabe and Pol et al

    2018

  45. [53]

    Isabelle Peretz and Robert J Zatorre. 2005. Brain organization for music processing.Annu. Rev. Psychol.56, 1 (2005), 89–114

    2005

  46. [54]

    Philippe Pinel and Stanislas Dehaene. 2010. Beyond hemispheric dominance: brain regions underlying the joint lateralization of language and arithmetic to the left hemisphere.Journal of Cognitive Neuroscience22, 1 (2010), 48–66

    2010

  47. [55]

    PupilLabs. 2024. Specifications for the Pupil Labs Core Eye Tracker. https://pupil-labs.com/products/core

    2024

  48. [56]

    PupilLabs. 2024. Specifications for the Pupil Labs Invisible Eye Tracker. https://pupil-labs.com/products/invisible

    2024

  49. [57]

    Felix Putze, Tilman Ihrig, Tanja Schultz, and Wolfgang Stuerzlinger. 2020. Platform for studying self-repairing auto-corrections in mobile text entry based on brain activity, gaze, and context. InProceedings of the 2020 CHI Conference on Human Factors in Computing Systems. 1–13

    2020

  50. [58]

    Azhar Quddus, Ali Shahidi Zandi, Laura Prest, and Felix JE Comeau. 2021. Using long short term memory and convolutional neural networks for driver drowsiness detection.Accident Analysis & Prevention156 (2021), 106107

    2021

  51. [59]

    GS Rajshekar Reddy, Michael J Proulx, Leanne Hirshfield, and Anthony Ries. 2024. Towards an Eye-Brain-Computer Interface: Combining Gaze with the Stimulus-Preceding Negativity for Target Selections in XR. InProceedings of the CHI Conference on Human Factors in Computing Systems. 1–17

    2024

  52. [60]

    Lesley J Rogers. 2021. Brain lateralization and cognitive capacity.Animals11, 7 (2021), 1996

    2021

  53. [61]

    Lesley J Rogers. 2024. Lateralization of Brain Function. InOxford Research Encyclopedia of Psychology

    2024

  54. [62]

    David Rozado and Andreas Dunser. 2015. Combining EEG with pupillometry to improve cognitive workload detection.Computer48, 10 (2015), 18–25

    2015

  55. [63]

    Carlo Semenza, Margarete Delazer, Laura Bertella, Alessia Granà, Ileana Mori, Fabio M Conti, Riccardo Pignatti, Lisa Bartha, Frank Domahs, Thomas Benke, et al. 2006. Is math lateralised on the same side as language? Right hemisphere aphasia and mathematical abilities.Neuroscie...

    2006

  56. [64]

    Harshita Sharma, Lior Drukker, Aris T Papageorghiou, and J Alison Noble. 2021. Machine learning-based analysis of operator pupillary response to assess cognitive workload in clinical ultrasound imaging.Computers in biology and medicine135 (2021), 104589

    2021

  57. [65]

    Namrata Srivastava, Joshua Newn, and Eduardo Velloso. 2018. Combining low and mid-level gaze features for desktop activity recognition. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies2, 4 (2018), 1–27

    2018

  58. [66]

    Niklas Stein, Gianni Bremer, and Markus Lappe. 2022. Eye tracking-based lstm for locomotion prediction in vr. In2022 IEEE conference on virtual reality and 3D user interfaces (VR). IEEE, 493–503

    2022

  59. [67]

    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. InProceedings of the 2019 CHI Conference on Human Factors in Computing Sy...

    2019 doi

  60. [68]

    Taichi Tanaka, Isao Nambu, Yoshiko Maruyama, and Yasuhiro Wada. 2022. Sliding-window normalization to improve the performance of machine- learning models for real-time motion prediction using electromyography.Sensors22, 13 (2022), 5005

    2022

  61. [69]

    Arthur W Toga and Paul M Thompson. 2003. Mapping brain asymmetry.Nature Reviews Neuroscience4, 1 (2003), 37–48

    2003

  62. [70]

    Lisa-Marie Vortmann and Felix Putze. 2021. Combining implicit and explicit feature extraction for eye tracking: attention classification using a heterogeneous input.Sensors21, 24 (2021), 8205

    2021

  63. [71]

    SF Walker. 1980. Lateralization of functions in the vertebrate brain: A review.British journal of psychology71, 3 (1980), 329–367

    1980

  64. [72]

    Ko Watanabe, Tanuja Sathyanarayana, Andreas Dengel, and Shoya Ishimaru. 2023. Engauge: Engagement gauge of meeting participants estimated by facial expression and deep neural network.IEEE Access11 (2023), 52886–52898

    2023

  65. [73]

    Ko Watanabe, Yusuke Soneda, Yuki Matsuda, Yugo Nakamura, Yutaka Arakawa, Andreas Dengel, and Shoya Ishimaru. 2021. DisCaaS: Micro Behavior Analysis on Discussion by Camera as a Sensor.Sensors21, 17 (2021). https://doi.org/10.3390/s21175719

    2021 doi

  66. [74]

    Jie Xu, Yang Wang, Fang Chen, and Eric Choi. 2011. Pupillary response based cognitive workload measurement under luminance changes. InIFIP Conference on Human-Computer Interaction. Springer, 178–185

    2011

  67. [75]

    Zihan Yan, Yufei Wu, Yang Zhang, and Xiang ’Anthony’ Chen. 2022. EmoGlass: an End-to-End AI-Enabled Wearable Platform for Enhancing Self-Awareness of Emotional Health. InProceedings of the 2022 CHI Conference on Human Factors in Computing Systems(New Orleans, LA, USA) (CHI ’22...

    2022 doi

  68. [76]

    Yvonne Yau, Thomas Hinault, Madeline Taylor, Paul Cisek, Lesley K Fellows, and Alain Dagher. 2021. Evidence and urgency related EEG signals during dynamic decision-making in humans.Journal of Neuroscience41, 26 (2021), 5711–5722

    2021

  69. [77]

    Xucong Zhang, Yusuke Sugano, and Andreas Bulling. 2019. Evaluation of Appearance-Based Methods and Implications for Gaze-Based Applications. InProceedings of the 2019 CHI Conference on Human Factors in Computing Systems(Glasgow, Scotland Uk)(CHI ’19). Association for Computing...

    2019 doi

Pith tools

Reviewed May 8, 2026 · model on record in the stance chip above.