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

Classification of Neurotransmitters01:30

Classification of Neurotransmitters

Neurotransmitters play a crucial role in the communication between neurons in the autonomic nervous system. Neurons in the autonomic nervous system can be cholinergic or adrenergic depending on the neurotransmitters synthesized. Cholinergic neurons use acetylcholine as their primary neurotransmitter. This includes all the preganglionic fibers of the sympathetic and pre- and postganglionic fibers of the parasympathetic nervous systems. In addition, neurons of the somatic nervous system also use...
Neural Circuits01:25

Neural Circuits

Neural circuits and neuronal pools are two of the main structures found in the nervous system. Neural circuits are networks of neurons that work together to carry out a specific task or process. They consist of interconnected neurons and glial cells, which provide structural and metabolic support.
Neuronal pools are collections of nerve cells with similar functions and interact through chemical and electrical signals. These pools include both interneurons (the central neural circuit nodes that...
Classification of Systems-I01:26

Classification of Systems-I

Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:

You might also read

Related Articles

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

Sort by
Same author

Mediation pathways and complementary roles of DTI-ALPS and free water fraction in glymphatic impairment and cognitive decline.

Journal of Alzheimer's disease : JAD·2026
Same author

Automated Label-Free Classification of Circulating Tumor Cells and White Blood Cells Using Hyperspectral Imaging and Deep Learning on Microfluidic SACA Chip System.

Micromachines·2026
Same author

Integration of Time-Varying Pharmacometric Modeling With Cox Regression for Time-to-Event Analysis in NONMEM.

CPT: pharmacometrics & systems pharmacology·2026
Same author

Distinct effects of empathy on self-other processing revealed by different behavioral and EEG indices.

Cognitive, affective & behavioral neuroscience·2026
Same author

Electroconvulsive Therapy in Solid Organ Transplant Recipients: A Case Illustration and Systematic Review of 18 Cases.

The journal of ECT·2026
Same author

Problematic internet users develop enhanced perceptual processing to offset neural deficits in conflict monitoring.

Scientific reports·2026

Related Experiment Video

Updated: Jun 29, 2026

Transcranial Direct Current Stimulation for Online Gamers
06:01

Transcranial Direct Current Stimulation for Online Gamers

Published on: November 9, 2019

7.9K

Classification of internet addiction using machine learning on electroencephalography synchronization and functional

Hsu-Wen Huang1,2, Po-Yu Li3, Meng-Cin Chen3

  • 1National Center for Geriatrics and Welfare Research, National Health Research Institutes, Zhunan, Taiwan.

Psychological Medicine
|May 16, 2025
PubMed
Summary

This study reveals that electroencephalography (EEG) functional connectivity, analyzed with phase lag index (PLI) and weighted PLI (WPLI), can accurately identify neurophysiological markers of internet addiction (IA). Machine learning models achieved high accuracy in distinguishing individuals with IA from healthy controls.

Keywords:
Internet addictionk-nearest neighbor classificationmachine learningphase lag indexrandom forestsupport vector machineweighted-phase lag index

More Related Videos

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
Electroencephalography Network Indices as Biomarkers of Upper Limb Impairment in Chronic Stroke
06:37

Electroencephalography Network Indices as Biomarkers of Upper Limb Impairment in Chronic Stroke

Published on: July 14, 2023

798

Related Experiment Videos

Last Updated: Jun 29, 2026

Transcranial Direct Current Stimulation for Online Gamers
06:01

Transcranial Direct Current Stimulation for Online Gamers

Published on: November 9, 2019

7.9K
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
Electroencephalography Network Indices as Biomarkers of Upper Limb Impairment in Chronic Stroke
06:37

Electroencephalography Network Indices as Biomarkers of Upper Limb Impairment in Chronic Stroke

Published on: July 14, 2023

798

Area of Science:

  • Neuroscience
  • Computational Psychiatry
  • Medical Imaging

Background:

  • Internet addiction (IA) is a growing concern characterized by excessive internet use leading to distress and cognitive impairment.
  • Understanding the neurophysiological underpinnings of IA is vital for diagnosis, treatment, and prevention.
  • Previous studies on IA neurophysiology show varied findings, necessitating more robust analytical methods.

Purpose of the Study:

  • To identify reliable neurophysiological characteristics of IA using advanced functional connectivity analysis.
  • To evaluate the efficacy of machine learning algorithms in classifying IA based on EEG data.
  • To explore the potential of phase lag index (PLI) and weighted PLI (WPLI) as biomarkers for IA.

Main Methods:

  • Resting-state electroencephalography (EEG) data were collected from 92 participants (42 with IA, 50 healthy controls).
  • Functional connectivity was analyzed using phase lag index (PLI) and weighted PLI (WPLI) to minimize volume conduction effects.
  • Machine learning, specifically Support Vector Machine (SVM), was employed to classify IA using selected EEG features.

Main Results:

  • Support Vector Machine (SVM) achieved 83% accuracy with PLI and 86% accuracy with WPLI.
  • Significant differences in functional connectivity were observed between IA and healthy controls, particularly in delta and gamma frequency bands.
  • The IA group exhibited elevated phase synchronization in specific brain connections.

Conclusions:

  • Functional connectivity analysis combined with machine learning effectively distinguishes individuals with IA from healthy controls using EEG.
  • PLI and WPLI show significant promise as reliable biomarkers for identifying the neurophysiological traits associated with IA.
  • These findings contribute to a better understanding of IA's neurobiological basis and support the development of diagnostic tools.