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

Light-FER: A Lightweight Facial Emotion Recognition System on Edge Devices.

Sensors (Basel, Switzerland)·2022
Same author

Automatic Cancer Cell Taxonomy Using an Ensemble of Deep Neural Networks.

Cancers·2022
Same author

A Novel Multistage Transfer Learning for Ultrasound Breast Cancer Image Classification.

Diagnostics (Basel, Switzerland)·2022
Same author

Classification of the Sidewalk Condition Using Self-Supervised Transfer Learning for Wheelchair Safety Driving.

Sensors (Basel, Switzerland)·2022
Same author

Gaze in the Dark: Gaze Estimation in a Low-Light Environment with Generative Adversarial Networks.

Sensors (Basel, Switzerland)·2020
Same author

Motor Imagery EEG Classification Using Capsule Networks.

Sensors (Basel, Switzerland)·2019

Related Experiment Video

Updated: Dec 21, 2025

Stimulus-specific Cortical Visual Evoked Potential Morphological Patterns
09:42

Stimulus-specific Cortical Visual Evoked Potential Morphological Patterns

Published on: May 12, 2019

6.3K

Prediction of Visual Memorability with EEG Signals: A Comparative Study.

Sang-Yeong Jo1, Jin-Woo Jeong1

  • 1Department of Computer Engineering, Kumoh National Institute of Technology, Gumi 39177, Korea.

Sensors (Basel, Switzerland)
|May 14, 2020
PubMed
Summary

Predicting image memorability using electroencephalography (EEG) signals shows promise. This biological feedback approach, while challenging, offers new avenues for understanding visual memory and content design.

Keywords:
deep learningelectroencephalographymachine learningvisual memorability

More Related Videos

Applications of EEG Neuroimaging Data: Event-related Potentials, Spectral Power, and Multiscale Entropy
11:15

Applications of EEG Neuroimaging Data: Event-related Potentials, Spectral Power, and Multiscale Entropy

Published on: June 27, 2013

34.3K
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

8.6K

Related Experiment Videos

Last Updated: Dec 21, 2025

Stimulus-specific Cortical Visual Evoked Potential Morphological Patterns
09:42

Stimulus-specific Cortical Visual Evoked Potential Morphological Patterns

Published on: May 12, 2019

6.3K
Applications of EEG Neuroimaging Data: Event-related Potentials, Spectral Power, and Multiscale Entropy
11:15

Applications of EEG Neuroimaging Data: Event-related Potentials, Spectral Power, and Multiscale Entropy

Published on: June 27, 2013

34.3K
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

8.6K

Area of Science:

  • Cognitive Science
  • Neuroscience
  • Computer Vision

Background:

  • Visual memorability is crucial for multimedia design and advertising.
  • Existing methods predict memorability using visual features or semantic information.
  • Electroencephalography (EEG) signals have been explored for text memorability prediction.

Purpose of the Study:

  • To predict the visual memorability of images using human biological feedback (EEG signals).
  • To evaluate the effectiveness of EEG signals as a predictor for image memorability.

Main Methods:

  • A visual memory task was designed where subjects recalled images after a 30-minute delay.
  • EEG signals were recorded during the memory task to capture biological feedback.
  • Various classification models, including deep convolutional neural networks and classical methods, were trained using EEG data.

Main Results:

  • EEG-based prediction of image memorability was found to be challenging.
  • The study demonstrated the potential of using biological feedback for memorability prediction.
  • Performance comparison of different classification models was conducted.

Conclusions:

  • Predicting visual memorability from EEG signals is a challenging but promising research direction.
  • This approach offers significant opportunities for advancing multimedia content design and understanding.
  • Further research is needed to optimize EEG-based memorability prediction models.