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

You might also read

Related Articles

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

Sort by
Same author

Spatial Immune Model of Alveolar Lung Infection (SIMALI) Identifies Structural Determinants of Lung Inflammation.

Research square·2026
Same author

Adaptive Skills May Moderate the Association between Prenatal Stress Exposure and Limbic Brain Activation: A Developmental Functional Magnetic Resonance Imaging Study of Superstorm Sandy Exposure.

Developmental neuroscience·2026
Same author

Evaluation of QUASAR Insight Phantom for daily imaging QA of MRgRT linacs.

Journal of applied clinical medical physics·2026
Same author

An integrative approach to bilingual cognition: preliminary insights into phonetic learning and sensorimotor adaptation.

Frontiers in human neuroscience·2025
Same author

Behavioral moderators of <i>In-utero</i> superstorm sandy exposure and fronto-limbic cortical development-potential role of adaptiveness in clinical intervention strategies, a pilot study.

Frontiers in psychiatry·2025
Same author

Seeking the Amygdala: Novel Use of Diffusion Tensor Imaging to Delineate the Basolateral Amygdala.

Biomedicines·2023

Related Experiment Video

Updated: Jul 4, 2025

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms
08:51

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms

Published on: November 1, 2019

5.6K

Brain Activity is Influenced by How High Dimensional Data are Represented: An EEG Study of Scatterplot Diagnostic

Ronak Etemadpour1,2,3, Sonali Shintree3, A Duke Shereen4

  • 1Verus Research, 6100 Uptown Blvd NE, Suite 260, Albuquerque, New Mexico 87110 USA.

Journal of Healthcare Informatics Research
|January 26, 2024
PubMed
Summary

Graph-theoretic Scatterplot Diagnostic (Scagnostics) helps manage complex data visualizations. Brain activity patterns reveal which Scagnostic measures best aid human decision-making from visual data.

Keywords:
Brain activitiesCognitionEEGERPMultidimensional data visualizationScagnostic measuresScatterplot features

More Related Videos

High-density Electroencephalographic Acquisition in a Rodent Model Using Low-cost and Open-source Resources
12:39

High-density Electroencephalographic Acquisition in a Rodent Model Using Low-cost and Open-source Resources

Published on: November 26, 2016

15.9K
Basics of Multivariate Analysis in Neuroimaging Data
06:35

Basics of Multivariate Analysis in Neuroimaging Data

Published on: July 24, 2010

16.9K

Related Experiment Videos

Last Updated: Jul 4, 2025

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms
08:51

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms

Published on: November 1, 2019

5.6K
High-density Electroencephalographic Acquisition in a Rodent Model Using Low-cost and Open-source Resources
12:39

High-density Electroencephalographic Acquisition in a Rodent Model Using Low-cost and Open-source Resources

Published on: November 26, 2016

15.9K
Basics of Multivariate Analysis in Neuroimaging Data
06:35

Basics of Multivariate Analysis in Neuroimaging Data

Published on: July 24, 2010

16.9K

Area of Science:

  • Human-Computer Interaction
  • Neuroscience
  • Data Visualization

Background:

  • Multidimensional data visualization often uses scatterplot matrices (SPLOMs).
  • The quadratic increase in scatterplots for high-dimensional data overwhelms human decision-making.
  • Scagnostics offer a method to extract salient scatterplot features for manageable data exploration.

Purpose of the Study:

  • To investigate brain activity using electroencephalography (EEG) during decision-making tasks involving Scagnostics.
  • To determine how different Scagnostics measures influence neural activation patterns.
  • To identify which visual data measures best support human decision-making.

Main Methods:

  • Participants made decisions based on scatterplots categorized by four Scagnostics measures: Clumpy, Monotonic, Striated, and Stringy.
  • Electroencephalography (EEG) recorded brain activity during these decision-making tasks.
  • Analysis focused on correlating neural activation with the difficulty of visual discrimination.

Main Results:

  • Easier visual discrimination tasks activated visual sensory cortices in the occipital lobe.
  • More difficult discrimination tasks recruited parietal and frontal regions associated with ambiguity resolution.
  • Distinct brain activation patterns were observed for different Scagnostics measures.

Conclusions:

  • Neural activity patterns can predict the effectiveness of specific Scagnostics measures in aiding human decisions.
  • The findings suggest a link between cognitive load, visual discrimination difficulty, and brain region activation.
  • EEG can serve as a biomarker for understanding human perception of visual data analytics.