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

Effects of Environmental Arsenic Exposure on the Morphology of Multiple Organs in Female Mice During Pre-Pregnancy, Gestation, and Lactation.

Journal of applied toxicology : JAT·2026
Same author

Association of 24-Hour Computed Tomography Infarct Density on Functional Outcomes in Stroke: Secondary Analysis From the AcT Trial.

Journal of the American Heart Association·2026
Same author

Age-adjustment of the combined early ischemic change and collateral extent score for outcomes after endovascular therapy.

AJNR. American journal of neuroradiology·2026
Same author

Intracranial Hemorrhage Patterns and Outcomes in Minor Stroke: Analysis of the TEMPO-2 Trial.

Stroke·2026
Same author

LAE-Net: Large Pretrained Models Assistant Text-Guided Image Editing Adversarial Network.

IEEE transactions on visualization and computer graphics·2026
Same author

DAS-YOLOv13: Dual-Axis Attention and Feature Fusion Model for Wafer Surface Defect Detection.

Sensors (Basel, Switzerland)·2026
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: Mar 14, 2026

Conscious and Non-conscious Representations of Emotional Faces in Asperger's Syndrome
08:31

Conscious and Non-conscious Representations of Emotional Faces in Asperger's Syndrome

Published on: July 31, 2016

14.7K

ReliefF-Based EEG Sensor Selection Methods for Emotion Recognition.

Jianhai Zhang1, Ming Chen2, Shaokai Zhao3

  • 1College of Computer Science, Hangzhou Dianzi University, Hangzhou 310018, China. jhzhang@hdu.edu.cn.

Sensors (Basel, Switzerland)
|September 27, 2016
PubMed
Summary
This summary is machine-generated.

This study optimized emotion recognition using electroencephalogram (EEG) signals by selecting fewer scalp sensors. ReliefF-based channel selection effectively reduced channels while maintaining high accuracy for practical brain-computer interfaces.

Keywords:
EEGReliefFemotion recognitionsensor selection

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

34.5K
Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention
06:37

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Published on: December 15, 2023

5.6K

Related Experiment Videos

Last Updated: Mar 14, 2026

Conscious and Non-conscious Representations of Emotional Faces in Asperger's Syndrome
08:31

Conscious and Non-conscious Representations of Emotional Faces in Asperger's Syndrome

Published on: July 31, 2016

14.7K
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

34.5K
Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention
06:37

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Published on: December 15, 2023

5.6K

Area of Science:

  • Neuroscience and Biomedical Engineering
  • Human-Computer Interaction

Background:

  • Electroencephalogram (EEG) signals offer direct insight into brain dynamics related to emotional states.
  • Emotion recognition from EEG is crucial for advanced Human-Computer Interaction (HCI) and wearable technology.
  • Using numerous EEG channels increases computational load and user inconvenience.

Purpose of the Study:

  • To investigate ReliefF-based channel selection methods for optimizing EEG-based emotion recognition.
  • To reduce the number of EEG channels required without significantly compromising classification accuracy.
  • To identify subject-independent channels crucial for emotion processing.

Main Methods:

  • Systematic investigation of three ReliefF-based channel selection strategies using the DEAP dataset.
  • Classification of four emotional states (joy, fear, sadness, relaxation) using Support Vector Machine (SVM).
  • Evaluation of channel selection performance based on F-score and accuracy metrics.

Main Results:

  • Channel selection strategies evaluating channels individually showed superior performance in reducing channel count with minimal accuracy loss.
  • Reducing channels to eight, with adjusted weights, resulted in a slight accuracy decrease (58.51% ± 10.05%) compared to 19 channels (59.13% ± 11.00%).
  • Subject-independent channels selected from frontal and parietal lobes showed discriminative information for emotion processing, consistent with existing literature.

Conclusions:

  • ReliefF-based channel selection is effective for reducing EEG channels in emotion recognition systems.
  • Optimized channel selection contributes to the development of practical and user-friendly EEG-based emotion recognition systems.
  • Identified subject-independent channels provide valuable insights into the neural basis of emotion processing.