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

Observation of Altermagnetic Order Switching in Bulk MnTe by Polarized Neutron Diffraction.

Physical review letters·2026
Same author

The role of FOXM1 in tumor immunology: implications for cancer treatment strategies.

Human cell·2026
Same author

Observation of Switchable Chiral Magnons in an Altermagnet.

Physical review letters·2026
Same author

Interannual Dynamics of Macrobenthic Communities near a Coastal Nuclear Power Plant: Environmental Drivers and Risks of Cooling Source Blockage.

Biology·2026
Same author

Pharmacological effects, classification, genetic and molecular studies of different chemotypes essential oil of <i>Perilla frutescens</i> (L.) Britt.: A review.

Journal of pharmaceutical analysis·2026
Same author

Local plasticity underlies the reorganization of cortical circuit dynamics during motor learning.

Current biology : CB·2026

Related Experiment Video

Updated: May 10, 2025

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

13.5K

Data Uncertainty (DU)-Former: An Episodic Memory Electroencephalography Classification Model for Pre- and

Xianglong Wan1,2, Zheyuan Liu1,2, Yiduo Yao1,3

  • 1School of Intelligence Science and Technology, University of Science and Technology Beijing, Beijing 100083, China.

Bioengineering (Basel, Switzerland)
|April 26, 2025
PubMed
Summary

This study introduces DU-former, a novel method for assessing episodic memory training using electroencephalography (EEG). DU-former enhances EEG signal classification, improving accuracy for cognitive function evaluation.

Keywords:
DU-formerelectroencephalogramepisodic memory training assessmentvirtual reality

More Related Videos

Combining Behavior and EEG to Study the Effects of Mindfulness Meditation on Episodic Memory
08:16

Combining Behavior and EEG to Study the Effects of Mindfulness Meditation on Episodic Memory

Published on: May 11, 2020

7.1K
Author Spotlight: Investigating the Impact of Emotional Prosodies on Voice Recognition and Perception
05:48

Author Spotlight: Investigating the Impact of Emotional Prosodies on Voice Recognition and Perception

Published on: August 9, 2024

1.3K

Related Experiment Videos

Last Updated: May 10, 2025

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

13.5K
Combining Behavior and EEG to Study the Effects of Mindfulness Meditation on Episodic Memory
08:16

Combining Behavior and EEG to Study the Effects of Mindfulness Meditation on Episodic Memory

Published on: May 11, 2020

7.1K
Author Spotlight: Investigating the Impact of Emotional Prosodies on Voice Recognition and Perception
05:48

Author Spotlight: Investigating the Impact of Emotional Prosodies on Voice Recognition and Perception

Published on: August 9, 2024

1.3K

Area of Science:

  • Neuroscience
  • Cognitive Science
  • Machine Learning

Background:

  • Episodic memory training is vital for cognitive enhancement and mitigating age-related memory decline.
  • Electroencephalography (EEG) provides objective neural activity assessment but faces classification challenges due to signal variability and complexity.
  • Existing methods like SVMs and CNNs struggle with feature extraction and uncertainty in EEG data for memory assessment.

Purpose of the Study:

  • To introduce DU-former, a novel deep learning model for robust EEG classification in episodic memory assessment.
  • To improve feature extraction and enhance model resilience to noise and data uncertainty in EEG signals.
  • To evaluate the effectiveness of DU-former in capturing changes in neural activity post-episodic memory training.

Main Methods:

  • Developed DU-former incorporating data uncertainty (DU) learning by modeling input features as Gaussian distributions.
  • Utilized a reparameterization module to predict mean and variance for robust feature representation.
  • Conducted an episodic memory training experiment with 17 participants over 28 days, collecting behavioral and EEG data.

Main Results:

  • Behavioral data indicated significant improvements in task completion time and object recognition accuracy.
  • DU-former demonstrated significant improvements in EEG classification accuracy post-training.
  • The model showed enhanced robustness in handling complex and noisy EEG data.

Conclusions:

  • DU-former offers a more efficient and robust method for EEG signal classification in episodic memory assessment.
  • Uncertainty learning is a valuable approach for improving the performance of EEG-based cognitive assessments.
  • The findings support the utility of DU-former for evaluating the efficacy of cognitive training interventions.