ReviewFrontiers in neurorobotics2021
Eye-Tracking Feature Extraction for Biometric Machine Learning.
Review in Frontiers in neurorobotics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
17 citing papers in PubMed, 63 citations in OpenAlex.
- Data-Driven Insights into E-Learning: A Comprehensive Review of Eye-Tracking Applications in Learning Systems.Journal of eye movement research · 2026Review
- Application of Eye-Tracking Technology in Assessing Binocular Vision Function in Paediatric Populations: A Scoping Review.Journal of eye movement research · 2026Review
- Eye Tracking for Rehabilitation and Training in Paediatric Neurodevelopmental Disorders: A Systematic Review.Brain sciences · 2026Review
- Estimating cognitive workload in robot assisted surgery using time and frequency features from EEG epochs with random forest regression.Scientific reports · 2026Article
- Advances in ocular motor and pupil biomarkers for neurological disorders.Brain communications · 2026Review
- Discrimination of Radiologists' Experience Level Using Eye-Tracking Technology and Machine Learning: Case Study.JMIR formative research · 2025Article
- Assessing the rereading effect of digital reading through eye movements using artificial neural networks.Frontiers in psychology · 2025Article
- Multimodal biomarker based on temporal complexity of eye movements and pupil diameter in attention-deficit/hyperactivity disorder.PLOS mental health · 2025Article
- Auxiliary Diagnosis of Children With Attention-Deficit/Hyperactivity Disorder Using Eye-Tracking and Digital Biomarkers: Case-Control Study.JMIR mHealth and uHealth · 2024Article
- Predicting Behaviour Patterns in Online and PDF Magazines with AI Eye-Tracking.Behavioral sciences (Basel, Switzerland) · 2024Article
- Wearable Near-Eye Tracking Technologies for Health: A Review.Bioengineering (Basel, Switzerland) · 2024Review
- Hyperparameter tuning using Lévy flight and interactive crossover-based reptile search algorithm for eye movement event classification.Frontiers in physiology · 2024Article
- Quantifying Dwell Time With Location-based Augmented Reality: Dynamic AOI Analysis on Mobile Eye Tracking Data With Vision Transformer.Journal of eye movement research · 2024Article
- Schizophrenia diagnosis based on diverse epoch size resting-state EEG using machine learning.PeerJ. Computer science · 2024Article
- Blink-To-Live eye-based communication system for users with speech impairments.Scientific reports · 2023Article
- Using machine learning to explore the characteristics of eye movement patterns and relationship with cognition ability of Chinese children aged 1-6 years.Frontiers in human neuroscience · 2023Article
- Eyes on me: Investigating the role and influence of eye-tracking data on user modeling in virtual reality.PloS one · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
contextEye tracking is a technology to measure and determine the eye movements and eye positions of an individual. The eye data can be collected and recorded using an eye tracker. Eye-tracking data offer unprecedented insights into human actions and environments, digitizing how people communicate with computers, and providing novel opportunities to conduct passive biometric-based classification such as emotion prediction. The objective of this article is to review what specific machine learning features can be obtained from eye-tracking data for the classification task.
methodsWe performed a systematic literature review (SLR) covering the eye-tracking studies in classification published from 2016 to the present. In the search process, we used four independent electronic databases which were the IEEE Xplore, the ACM Digital Library, and the ScienceDirect repositories as well as the Google Scholar. The selection process was performed by using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) search strategy. We followed the processes indicated in the PRISMA to choose the appropriate relevant articles.
resultsOut of the initial 420 articles that were returned from our initial search query, 37 articles were finally identified and used in the qualitative synthesis, which were deemed to be directly relevant to our research question based on our methodology.
conclusionThe features that could be extracted from eye-tracking data included pupil size, saccade, fixations, velocity, blink, pupil position, electrooculogram (EOG), and gaze point. Fixation was the most commonly used feature among the studies found.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.