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

Working Memory01:24

Working Memory

156
Working memory refers to a combination of components, including short-term memory and attention, that allow an individual to hold information temporarily as we perform cognitive tasks. It is an essential cognitive function that enables the execution of complex tasks such as problem-solving, comprehension, and reasoning. Unlike short-term memory, which simply involves the storage of information for a brief period, working memory involves the active manipulation and processing of this...
156
Brain Waves01:23

Brain Waves

1.3K
Brain waves are electrical signals generated by the neurons in the brain, which are regularly monitored to measure mental activities. Brain waves and their frequency ranges can be measured using an electroencephalogram or EEG. There are four main types of brain waves, each with distinct characteristics:
1.3K

You might also read

Related Articles

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

Sort by
Same author

Association of PARP1 SNP (rs1136410) with Brain Tumor Risk: Insights from Khyber Pakhtunkhwa.

Asian Pacific journal of cancer prevention : APJCP·2026
Same author

A Lightweight Machine Learning Framework for Post-Stroke Gait Abnormality Classification Using Wearable Gyroscope Features.

Sensors (Basel, Switzerland)·2026
Same author

Comparing intraosseous versus intravenous access for resuscitation in out-of-hospital cardiac arrest: A systematic review and GRADE meta-analysis.

Medicine·2026
Same author

Oral administration of kratom leaf extract alleviates anxiety-like behavior, urinary bladder pain, voiding dysfunction, and bladder hypercontractility via attenuating muscarinic receptor response in male mice exposed to chronic water avoidance stress.

Frontiers in neuroscience·2026
Same author

Low-frequency LFP oscillations (1-9 Hz) changes reward-related brain regions during sugar-based T-maze decision making in mice.

Neuroscience·2026
Same author

A Novel Functional Ingredient Derived From a Mixture of Mulberry (<i>Morus alba</i> L.) Leaves and Butterfly Pea (<i>Clitoria ternatea</i> L.) Flowers Enhances Rapid Eye Movement Sleep, Cognitive Function, and Anxiolytic Behavior via GABA<sub>A</sub> Receptor-Dependent Mechanism in Rats.

Oxidative medicine and cellular longevity·2026
Same journal

Behavioral characterization of bulbar sensorimotor function in a rat model of Alexander disease.

Behavioural brain research·2026
Same journal

Prenatal Exposure to High- but Not Low-Molecular-Weight Poly(I:C) Produces Selective Sociability Deficits in Offspring.

Behavioural brain research·2026
Same journal

Understanding vulnerability through variability: a longitudinal twin study linking sex differences in neurodiversity, neurodevelopment and X-linked genetic mechanisms.

Behavioural brain research·2026
Same journal

Hippocampal plasticity predicts behavioral lateralization and stress resilience in laying hen chicks.

Behavioural brain research·2026
Same journal

Effects of retatrutide on learning and memory in streptozotocin-induced male diabetic rats.

Behavioural brain research·2026
Same journal

Bacopa-enriched formulation enhances memory and synaptic plasticity in a rat model of vascular dementia.

Behavioural brain research·2026
See all related articles

Related Experiment Video

Updated: Jun 25, 2025

Assessment of Age-related Changes in Cognitive Functions Using EmoCogMeter, a Novel Tablet-computer Based Approach
10:13

Assessment of Age-related Changes in Cognitive Functions Using EmoCogMeter, a Novel Tablet-computer Based Approach

Published on: February 14, 2014

13.7K

Complexity measures reveal age-dependent changes in electroencephalogram during working memory task.

Hamad Javaid1, Muhammad Nouman2, Dania Cheaha3

  • 1Department of Biomedical Sciences and Biomedical Engineering, Faculty of Medicine, Prince of Songkla University, Hat Yai, Songkhla 90110, Thailand; Department of Psychology, Faculty of Health and Life Sciences, University of Exeter, Exeter, Ex4 4QG, United Kingdom.

Behavioural Brain Research
|May 28, 2024
PubMed
Summary
This summary is machine-generated.

Brain aging reduces electroencephalogram (EEG) signal complexity, detectable using fractal dimension measures. Machine learning accurately distinguishes age groups, highlighting complexity as a marker for healthy aging.

Keywords:
AgingComplexityEEGMachine learningWorking memory task

More Related Videos

Utilizing Electroencephalography Measurements for Comparison of Task-Specific Neural Efficiencies: Spatial Intelligence Tasks
06:57

Utilizing Electroencephalography Measurements for Comparison of Task-Specific Neural Efficiencies: Spatial Intelligence Tasks

Published on: August 9, 2016

11.4K
Examining the Characteristics of Episodic Memory using Event-related Potentials in Patients with Alzheimer's Disease
11:01

Examining the Characteristics of Episodic Memory using Event-related Potentials in Patients with Alzheimer's Disease

Published on: August 30, 2011

13.6K

Related Experiment Videos

Last Updated: Jun 25, 2025

Assessment of Age-related Changes in Cognitive Functions Using EmoCogMeter, a Novel Tablet-computer Based Approach
10:13

Assessment of Age-related Changes in Cognitive Functions Using EmoCogMeter, a Novel Tablet-computer Based Approach

Published on: February 14, 2014

13.7K
Utilizing Electroencephalography Measurements for Comparison of Task-Specific Neural Efficiencies: Spatial Intelligence Tasks
06:57

Utilizing Electroencephalography Measurements for Comparison of Task-Specific Neural Efficiencies: Spatial Intelligence Tasks

Published on: August 9, 2016

11.4K
Examining the Characteristics of Episodic Memory using Event-related Potentials in Patients with Alzheimer's Disease
11:01

Examining the Characteristics of Episodic Memory using Event-related Potentials in Patients with Alzheimer's Disease

Published on: August 30, 2011

13.6K

Area of Science:

  • Neuroscience
  • Biomedical Engineering
  • Signal Processing

Background:

  • Electroencephalogram (EEG) signal alterations reflect aging and cognitive decline.
  • Early detection of brain aging is crucial for preventing neurodegenerative diseases like Alzheimer's.

Purpose of the Study:

  • To analyze EEG signal complexity changes during healthy aging.
  • To differentiate between middle-aged and elderly individuals using EEG features.
  • To evaluate machine learning classifiers for age-based EEG signal classification.

Main Methods:

  • Recorded EEG signals from middle-aged and elderly healthy subjects during a working memory task.
  • Extracted complexity features: Higuchi's fractal dimension (HFD), Katz's fractal dimension (KFD), sample entropy, and Hjorth parameters.
  • Employed machine learning classifiers: multilayer perceptron (MLP), support vector machine (SVM), K-nearest neighbour (KNN), and logistic model tree (LMT).

Main Results:

  • Higuchi's fractal dimension (HFD), Katz's fractal dimension (KFD), and Hjorth complexity showed significant correlation with age.
  • EEG signal complexity decreased from middle age to elderly groups.
  • MLP achieved the highest accuracy (93.75% overall, 92.50% in posterior regions) in distinguishing age groups.

Conclusions:

  • Fractal dimension and Hjorth complexity are sensitive indicators of reduced EEG complexity in healthy aging.
  • Complexity features of EEG signals are appropriate for monitoring healthy brain aging.
  • Machine learning, particularly MLP, effectively classifies age groups based on EEG complexity.