Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Cluster Sampling Method01:20

Cluster Sampling Method

11.0K
Appropriate sampling methods ensure that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a cluster sample, divide the population into clusters (groups) and then randomly select some of the clusters. All the members from these clusters are in the cluster sample. For example, if you randomly sample four departments from your...
11.0K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Traveling-wave transcranial alternating current stimulation (twtACS) causally links neural timing to cognitive function.

Proceedings of the National Academy of Sciences of the United States of America·2026
Same author

Neural representations of beliefs in a multi-dimensional inference task.

bioRxiv : the preprint server for biology·2026
Same author

The Primate Hippocampus Constructs a Temporal Scaffold Anchored to Behavioral Events.

bioRxiv : the preprint server for biology·2025
Same author

Dynamic prefrontal coupling coordinates adaptive decision-making.

Research square·2025
Same author

Parallel patterns of age-related working memory impairment in marmosets and macaques.

Aging·2025
Same author

Visual Exploration and the Primate Hippocampal Formation.

Hippocampus·2024

Related Experiment Video

Updated: May 3, 2026

Characterizing the Relationship Between Eye Movement Parameters and Cognitive Functions in Non-demented Parkinson's Disease Patients with Eye Tracking
07:26

Characterizing the Relationship Between Eye Movement Parameters and Cognitive Functions in Non-demented Parkinson's Disease Patients with Eye Tracking

Published on: September 26, 2019

7.4K

A nonparametric method for detecting fixations and saccades using cluster analysis: removing the need for arbitrary

Seth D König1, Elizabeth A Buffalo2

  • 1Wallace H. Coulter Department of Biomedical Engineering at the Georgia Institute of Technology and Emory University, 313 Ferst Drive, Atlanta, GA 30332, USA; Yerkes National Primate Research Center, 954 Gatewood Road, Atlanta, GA 30329, USA; Graduate Program in Neurobiology and Behavior, University of Washington, Seattle, WA 98195, USA.

Journal of Neuroscience Methods
|February 11, 2014
PubMed
Summary

Cluster Fix, a new eye-tracking algorithm, uses k-means clustering to accurately segment fixations and saccades in complex behavioral experiments. This method improves detection of small saccades and precise transition times, outperforming traditional threshold-based approaches.

Keywords:
Cluster analysisEye trackingFixationsSaccade detectionViewing behavior

More Related Videos

A Visual Guide to Sorting Electrophysiological Recordings Using 'SpikeSorter'
10:31

A Visual Guide to Sorting Electrophysiological Recordings Using 'SpikeSorter'

Published on: February 10, 2017

10.5K
Eye Tracking Young Children with Autism
09:03

Eye Tracking Young Children with Autism

Published on: March 27, 2012

50.0K

Related Experiment Videos

Last Updated: May 3, 2026

Characterizing the Relationship Between Eye Movement Parameters and Cognitive Functions in Non-demented Parkinson's Disease Patients with Eye Tracking
07:26

Characterizing the Relationship Between Eye Movement Parameters and Cognitive Functions in Non-demented Parkinson's Disease Patients with Eye Tracking

Published on: September 26, 2019

7.4K
A Visual Guide to Sorting Electrophysiological Recordings Using 'SpikeSorter'
10:31

A Visual Guide to Sorting Electrophysiological Recordings Using 'SpikeSorter'

Published on: February 10, 2017

10.5K
Eye Tracking Young Children with Autism
09:03

Eye Tracking Young Children with Autism

Published on: March 27, 2012

50.0K

Area of Science:

  • Neuroscience
  • Behavioral Science
  • Computational Biology

Background:

  • Eye tracking is crucial for behavioral experiments in humans and primates.
  • Complex experimental paradigms yield variable eye movement data.
  • Traditional methods for segmenting fixations and saccades may be insufficient.

Purpose of the Study:

  • To introduce a novel algorithm, Cluster Fix, for improved eye movement segmentation.
  • To leverage k-means clustering for differentiating between fixations and saccades.
  • To address limitations of threshold-based methods in complex eye-tracking data.

Main Methods:

  • Cluster Fix employs k-means cluster analysis on four state-space parameters: distance, velocity, acceleration, and angular velocity.
  • The algorithm identifies natural divisions within scan path data to segment fixations and saccades.
  • Cluster Fix dynamically adjusts cluster number and size based on individual scan path variability.

Main Results:

  • Cluster Fix successfully detects small saccades previously indistinguishable from noisy fixations.
  • Local analysis of fixations enabled precise determination of saccade transition times.
  • The algorithm identifies natural data divisions, eliminating the need for predefined thresholds.

Conclusions:

  • Cluster Fix offers precise identification of saccade onset and offset, vital for analyzing saccade-related neural activity.
  • The algorithm demonstrates higher sensitivity compared to threshold-based methods.
  • Increased computational time is a trade-off for Cluster Fix's enhanced sensitivity and precision.