Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Working Memory

Working Memory

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...

You might also read

Related Articles

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

Sort by

Same author

Survival prediction modeling for 1-year mortality in patients with ST elevation myocardial infarction (STEMI) undergoing primary percutaneous coronary intervention (PPCI).

Heart & lung : the journal of critical care·2026

Same author

Examining adverse effects in a large clinical trial of rTMS application as a treatment for Alzheimer's disease.

Psychiatry research·2026

Same author

Tumor-Derived Complement C3 Overexpression in STK11-Mutant Lung Adenocarcinoma Drives Tumor Growth and Immune Checkpoint Inhibitor Resistance.

Cancer immunology research·2026

Same author

Multimodal deep learning for objective skill assessment in robot-assisted vesico-urethral anastomosis.

Journal of robotic surgery·2026

Same author

TP53 mutation is associated with improved disease control in patients with advanced RAS wild-type colorectal adenocarcinoma treated with cetuximab and pembrolizumab.

International journal of cancer·2026

Same author

A Two-Level Ensemble Machine Learning Framework for OSA Classification Whilst Awake from Noisy Tracheal Breathing Sounds.

Sensors (Basel, Switzerland)·2026

Same journal

Biological aging increases risk of postoperative morbidity and mortality: an international, multi-cohort study.

npj aging·2026

Same journal

Telomere integrity, epigenetic aging, and genetic burden shape biological aging trajectories in idiopathic pulmonary fibrosis.

npj aging·2026

Same journal

The cancer Alzheimer's disease paradox.

npj aging·2026

Same journal

Motor unit behavior adaptations across lifespan: sex differences in young, middle-aged and old adults.

npj aging·2026

Same journal

The evolution of aging research: from theories to epigenetic reprogramming.

npj aging·2026

Same journal

Association between socioeconomic position and transitions to multimorbidity involving major chronic diseases in Northwest Italy.

npj aging·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Jul 14, 2025

Conducting Concurrent Electroencephalography and Functional Near-Infrared Spectroscopy Recordings with a Flanker Task

Conducting Concurrent Electroencephalography and Functional Near-Infrared Spectroscopy Recordings with a Flanker Task

Published on: May 24, 2020

Developing cognitive workload and performance evaluation models using functional brain network analysis.

Saeed Shadpour¹, Ambreen Shafqat², Serkan Toy³

¹Department of Animal Biosciences, University of Guelph, Guelph, Ontario, N1G 2W1, Canada.

|October 6, 2023

Summary

This summary is machine-generated.

Electroencephalogram (EEG) features from brain networks and spectral analysis can objectively evaluate cognitive workload and performance. This approach may help monitor age-related cognitive changes, potentially through engaging computer games.

More Related Videos

Using Fiberless, Wearable fNIRS to Monitor Brain Activity in Real-world Cognitive Tasks

Using Fiberless, Wearable fNIRS to Monitor Brain Activity in Real-world Cognitive Tasks

Published on: December 2, 2015

Correlating Behavioral Responses to fMRI Signals from Human Prefrontal Cortex: Examining Cognitive Processes Using Task Analysis

Correlating Behavioral Responses to fMRI Signals from Human Prefrontal Cortex: Examining Cognitive Processes Using Task Analysis

Published on: June 20, 2012

Related Experiment Videos

Last Updated: Jul 14, 2025

Conducting Concurrent Electroencephalography and Functional Near-Infrared Spectroscopy Recordings with a Flanker Task

Conducting Concurrent Electroencephalography and Functional Near-Infrared Spectroscopy Recordings with a Flanker Task

Published on: May 24, 2020

Using Fiberless, Wearable fNIRS to Monitor Brain Activity in Real-world Cognitive Tasks

Using Fiberless, Wearable fNIRS to Monitor Brain Activity in Real-world Cognitive Tasks

Published on: December 2, 2015

Correlating Behavioral Responses to fMRI Signals from Human Prefrontal Cortex: Examining Cognitive Processes Using Task Analysis

Correlating Behavioral Responses to fMRI Signals from Human Prefrontal Cortex: Examining Cognitive Processes Using Task Analysis

Published on: June 20, 2012

Area of Science:

Neuroscience
Human-Computer Interaction
Cognitive Science

Background:

Cognition is crucial for daily activities and skill acquisition.
Objective evaluation of cognitive workload and performance is needed.
Electroencephalogram (EEG) offers a non-invasive method for brain activity monitoring.

Purpose of the Study:

To develop models for evaluating cognitive workload and performance using EEG features.
To explore the relationship between EEG data, cognitive tasks, and performance metrics.
To investigate the correlation between age, cognitive workload, and performance.

Main Methods:

Recorded EEG data from 124 brain areas in 26 healthy participants performing robot simulator tasks.
Extracted functional brain network (coherence) and spectral (Power Spectral Density) features from EEG.
Developed linear models using EEG features, subjective workload (SURG-TLX), and objective performance scores.

Main Results:

Combined EEG features from spectral analysis and functional brain networks effectively evaluated cognitive workload and performance.
A significant correlation was found between cognitive workload during a challenging task (Matchboard level 3) and age (0.54, p<0.01).
Findings suggest challenging computer games may aid in identifying age-related cognitive workload changes.

Conclusions:

Objective evaluation and monitoring of cognitive workload and performance are feasible using combined EEG features.
The study highlights a potential link between challenging cognitive tasks, aging, and workload assessment.
This research paves the way for advanced, objective cognitive assessment tools.