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

Attention-Deficit/Hyperactivity Disorder01:30

Attention-Deficit/Hyperactivity Disorder

Attention-deficit/hyperactivity disorder (ADHD) is a neurodevelopmental disorder characterized by persistent inattention, hyperactivity, and impulsivity. It affects approximately 5-8% of children globally, with around 60-70% of cases persisting into adulthood. ADHD has significant implications for educational attainment, social interactions, and occupational success.
Diagnostic Criteria and Symptoms
To diagnose ADHD, symptoms must manifest before age 12 and be evident across multiple settings.
Modeling in Therapy01:26

Modeling in Therapy

Modeling, a key technique in therapy, uses observational learning to help clients acquire and practice new skills by watching therapists demonstrate desired behaviors. This approach, rooted in Albert Bandura's concept of vicarious learning, plays a significant role in therapeutic interventions for various psychological conditions, including social anxiety, ADHD, and depression.
Participant Modeling
Participant modeling involves therapists demonstrating calm and effective behaviors in situations...

You might also read

Related Articles

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

Sort by
Same author

Entropy and Complexity in QEEG Reveal Visual Processing Signatures in Autism: A Neurofeedback-Oriented and Clinical Differentiation Study.

Brain sciences·2025
Same author

Entropy, complexity, and spectral features of EEG signals in autism and typical development: a quantitative approach.

Frontiers in psychiatry·2025
Same author

Managers' Action-Guiding Mental Models towards Mental Health-Related Organizational Interventions-A Systematic Review of Qualitative Studies.

International journal of environmental research and public health·2022
Same author

Effective refractive error coverage in adults aged 50 years and older: estimates from population-based surveys in 61 countries.

The Lancet. Global health·2022
Same author

BCC-Cu nanoparticles: from a transient to a stable allotrope by tuning size and reaction conditions.

Physical chemistry chemical physics : PCCP·2022
Same author

Applying risk matrices for assessing the risk of psychosocial hazards at work.

Frontiers in public health·2022

Related Experiment Video

Updated: May 14, 2026

Event Related Potentials (ERPs) and other EEG Based Methods for Extracting Biomarkers of Brain Dysfunction: Examples from Pediatric Attention Deficit/Hyperactivity Disorder (ADHD)
10:02

Event Related Potentials (ERPs) and other EEG Based Methods for Extracting Biomarkers of Brain Dysfunction: Examples from Pediatric Attention Deficit/Hyperactivity Disorder (ADHD)

Published on: March 12, 2020

Machine learning approach for classification of ADHD adults.

Aleksandar Tenev1, Silvana Markovska-Simoska2, Ljupco Kocarev2

  • 1Faculty for Computer Science and Engineering, University of Skopje, Former Yugoslav Republic of Macedonia.

International Journal of Psychophysiology : Official Journal of the International Organization of Psychophysiology
|January 31, 2013
PubMed
Summary

Machine learning enhances the classification of adult attention deficit hyperactivity disorder (ADHD) subtypes using EEG power spectra. This approach improves distinguishing ADHD from controls and identifying ADHD subtypes.

Keywords:
ADHDEEG power spectraKarnaugh mapSupport vector machines

More Related Videos

The Adventures of Fundi Intervention Based on the Cognitive and Emotional Processing in Attention Deficit Hyperactive Disorder Patients
05:48

The Adventures of Fundi Intervention Based on the Cognitive and Emotional Processing in Attention Deficit Hyperactive Disorder Patients

Published on: June 12, 2020

Using Brain Activation (nir-HEG/Q-EEG) and Execution Measures (CPTs) in a ADHD Assessment Protocol
13:09

Using Brain Activation (nir-HEG/Q-EEG) and Execution Measures (CPTs) in a ADHD Assessment Protocol

Published on: April 1, 2018

Related Experiment Videos

Last Updated: May 14, 2026

Event Related Potentials (ERPs) and other EEG Based Methods for Extracting Biomarkers of Brain Dysfunction: Examples from Pediatric Attention Deficit/Hyperactivity Disorder (ADHD)
10:02

Event Related Potentials (ERPs) and other EEG Based Methods for Extracting Biomarkers of Brain Dysfunction: Examples from Pediatric Attention Deficit/Hyperactivity Disorder (ADHD)

Published on: March 12, 2020

The Adventures of Fundi Intervention Based on the Cognitive and Emotional Processing in Attention Deficit Hyperactive Disorder Patients
05:48

The Adventures of Fundi Intervention Based on the Cognitive and Emotional Processing in Attention Deficit Hyperactive Disorder Patients

Published on: June 12, 2020

Using Brain Activation (nir-HEG/Q-EEG) and Execution Measures (CPTs) in a ADHD Assessment Protocol
13:09

Using Brain Activation (nir-HEG/Q-EEG) and Execution Measures (CPTs) in a ADHD Assessment Protocol

Published on: April 1, 2018

Area of Science:

  • Neuroscience
  • Machine Learning
  • Biomedical Engineering

Background:

  • Attention deficit hyperactivity disorder (ADHD) is a complex neurodevelopmental disorder.
  • Accurate classification of ADHD subtypes is crucial for effective treatment.
  • Electroencephalography (EEG) measures brain activity, offering potential biomarkers.

Purpose of the Study:

  • To develop and evaluate machine learning techniques for classifying adult ADHD subtypes.
  • To investigate the efficacy of combining multiple classifiers for improved diagnostic accuracy.
  • To analyze EEG power spectra under various conditions for ADHD classification.

Main Methods:

  • Utilized machine learning, specifically combining multiple support vector machine (SVM) classifiers.
  • EEG power spectra data from 117 adults (67 ADHD, 50 controls) were analyzed.
  • Data were collected under four conditions: resting (eyes open/closed) and two cognitive tasks.

Main Results:

  • The combined classifier approach significantly improved the discrimination between ADHD and control groups.
  • The method also enhanced the ability to differentiate between various ADHD subtypes.
  • Karnaugh map-derived logical expressions were used to combine classifier outputs.

Conclusions:

  • Machine learning ensemble methods show promise for objective ADHD diagnosis using EEG.
  • This approach offers a potential tool for more precise identification of ADHD subtypes.
  • Further research can explore larger datasets and diverse populations.