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

Association Areas of the Cortex01:21

Association Areas of the Cortex

5.2K
Association areas are regions of the cerebral cortex that do not have a specific sensory or motor function. Instead, they integrate and interpret information from various sources to enable higher cognitive processes such as memory, learning, and decision-making. Some key association areas include the following:
Prefrontal Association Area: This area is located in the frontal lobe and is involved in planning, decision-making, and moderating social behavior. It connects with primary motor areas,...
5.2K

You might also read

Related Articles

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

Sort by
Same author

Scaled containment control for first/second-order multi-agent systems in a noisy environment.

ISA transactions·2026
Same author

<i>Sinosenecio yaanensis</i> (Asteraceae, Senecioneae), a new species from western Sichuan, China.

PhytoKeys·2025
Same author

SpaIM: single-cell spatial transcriptomics imputation via style transfer.

Nature communications·2025
Same author

Meat species authentication using portable hyperspectral imaging.

Frontiers in nutrition·2025
Same author

Efficacy and safety of brain-computer interface for stroke rehabilitation: an overview of systematic review.

Frontiers in human neuroscience·2025
Same author

SpaIM: Single-cell Spatial Transcriptomics Imputation via Style Transfer.

bioRxiv : the preprint server for biology·2025
Same journal

RETRACTED: Zhang et al. A Novel Framework for Reconstruction and Imaging of Target Scattering Centers via Wide-Angle Incidence in Radar Networks. <i>Sensors</i> 2025, <i>25</i>, 6802.

Sensors (Basel, Switzerland)·2026
Same journal

Enhancing Unsupervised Multi-Source Domain Adaptation for Person Re-Identification via Mixture of Experts and Graph-Based Relation.

Sensors (Basel, Switzerland)·2026
Same journal

Development of an Instrumented Glove for Palmar Pressure Assessment in Kayakers.

Sensors (Basel, Switzerland)·2026
Same journal

Development and Experimental Validation of an Autonomous IoT-Based Monitoring System for Real-Time Water Quality Assessment in the Amazon River.

Sensors (Basel, Switzerland)·2026
Same journal

Semi-Supervised Adversarial Learning Framework for Controller Area Network Bus Intrusion Detection.

Sensors (Basel, Switzerland)·2026
Same journal

Smart Optimization Method for Safety Signs in Innovative Manufacturing Environments Integrating Industrial Field IoT Sensors and Knowledge Graphs.

Sensors (Basel, Switzerland)·2026
See all related articles

Related Experiment Video

Updated: Jun 21, 2025

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

33.7K

A Dynamic Multi-Scale Convolution Model for Face Recognition Using Event-Related Potentials.

Shengkai Li1,2, Tonglin Zhang2,3, Fangmei Yang2

  • 1School of Automation, Qingdao University, Qingdao 266071, China.

Sensors (Basel, Switzerland)
|July 13, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces a novel Dynamic Multi-Scale Convolution model for recognizing familiar and unfamiliar faces using event-related potential (ERP) data. The model achieves high accuracy, offering new insights into brain representations of faces.

Keywords:
familiar and unfamiliar face recognitionmaskmulti-scale

More Related Videos

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

1.5K
Analyzing Neural Activity and Connectivity Using Intracranial EEG Data with SPM Software
06:50

Analyzing Neural Activity and Connectivity Using Intracranial EEG Data with SPM Software

Published on: October 30, 2018

9.5K

Related Experiment Videos

Last Updated: Jun 21, 2025

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

33.7K
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

1.5K
Analyzing Neural Activity and Connectivity Using Intracranial EEG Data with SPM Software
06:50

Analyzing Neural Activity and Connectivity Using Intracranial EEG Data with SPM Software

Published on: October 30, 2018

9.5K

Area of Science:

  • Neuroscience
  • Cognitive Science
  • Computer Science

Background:

  • Event-related potential (ERP) analysis has advanced from statistical methods to machine learning techniques.
  • Challenges remain in linking ERP components to the neural representation of familiar versus unfamiliar faces.

Purpose of the Study:

  • To propose a novel Dynamic Multi-Scale Convolution model for group recognition of familiar and unfamiliar faces using ERP data.
  • To address the complexities in understanding the relationship between ERP components and face representation.

Main Methods:

  • Developed a multi-scale model utilizing generated weight masks for cross-subject face recognition.
  • Employed a variable-length filter generator to dynamically capture features across different time scales in time-series ERP data.

Main Results:

  • The proposed model achieved a balanced accuracy of 93.20% and an F1 score of 88.54%.
  • Demonstrated superior performance compared to state-of-the-art (SOTA) models in comparative experiments.
  • ERP data extracted at different time regions offer data-driven support for ERP component research.

Conclusions:

  • The Dynamic Multi-Scale Convolution model effectively recognizes familiar and unfamiliar faces from ERP data.
  • The approach provides valuable data-driven insights into the neural representation of faces.
  • This method enhances cross-subject face recognition capabilities using advanced signal processing and machine learning.