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

Labeling Emotion

Labeling Emotion

Emotional labeling is a cognitive process that involves identifying and naming one's emotions, such as anger, fear, happiness, or sadness. It allows individuals to recognize and express their internal emotional states, a critical aspect of emotional regulation and communication. Labeling emotions requires more than mere recognition; it also involves drawing upon memory and contextual cues to understand the current situation and apply a corresponding emotional label. For instance, feeling...

You might also read

Related Articles

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

Sort by

Same author

Loss of Brg1 prevents the progression of pulmonary hypertension by inhibiting Nrf2 expression.

Scientific reports·2026

Same author

Assessing causal relationships between oral microbiota and asthma: evidence from two-sample mendelian randomization analysis.

The Journal of asthma : official journal of the Association for the Care of Asthma·2026

Same author

Zwitterionic Engineering of Vinylene-Linked Covalent Organic Frameworks for Superior Protonic Electrolytes.

Angewandte Chemie (International ed. in English)·2026

Same author

Photoelectron-driven modulation of carbon metabolism enhances hydrogen production in a ZnIn<sub>2</sub>S<sub>4</sub>-Shewanella oneidensis biohybrid system.

Bioresource technology·2026

Same author

The impact of college students' physical exercise on negative emotion: The chain mediating role of self-efficacy and smartphone addiction.

PloS one·2026

Same author

Impact of physical exercise on sleep quality in college students: A Chain mediating role of self-efficacy and emotional control.

PloS one·2026

Same journal

Topological dependence of viral mutation spread in complex host-interaction networks.

Chaos (Woodbury, N.Y.)·2026

Same journal

Multifractal signatures of Hamiltonian chaos in Hyperion's rotational dynamics.

Chaos (Woodbury, N.Y.)·2026

Same journal

Exploring mechanisms for reversal of flow in tunicate hearts.

Chaos (Woodbury, N.Y.)·2026

Same journal

State estimation in spatiotemporal chaos via low-rank StatFEM.

Chaos (Woodbury, N.Y.)·2026

Same journal

Universal response functions in driven dissipative tunneling dynamics.

Chaos (Woodbury, N.Y.)·2026

Same journal

A network-based approach to characterize the dynamics of the coupling field of thermoacoustic oscillators in annular geometry.

Chaos (Woodbury, N.Y.)·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Aug 27, 2025

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

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

Published on: December 15, 2023

Cross-subject emotion recognition using visibility graph and genetic algorithm-based convolution neural network.

Qing Cai¹, Jian-Peng An¹, Hao-Yu Li¹

¹School of Electrical and Information Engineering, Tianjin University, Tianjin 300072, China.

Chaos (Woodbury, N.Y.)

|October 1, 2022

Summary

This summary is machine-generated.

This study optimizes electroencephalogram (EEG) emotion recognition by using a genetic algorithm to select crucial EEG channels, improving accuracy and efficiency for brain-computer interfaces.

More Related Videos

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

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

Published on: July 31, 2016

Related Experiment Videos

Last Updated: Aug 27, 2025

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

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

Published on: December 15, 2023

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

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

Published on: July 31, 2016

Area of Science:

Neuroscience
Computer Science
Biomedical Engineering

Background:

Electroencephalogram (EEG)-based emotion recognition is vital for brain-computer interfaces.
Redundant EEG channels degrade model performance and computational efficiency.
Optimizing EEG channel input is crucial for practical emotion recognition applications.

Purpose of the Study:

To optimize EEG channel input for emotion recognition models.
To enhance the efficiency and accuracy of EEG-based emotion recognition.
To reduce computational resources required for emotion recognition.

Main Methods:

Emotion states were induced using movies, and multi-channel EEG data were collected.
Visibility graphs (VG) were constructed for each EEG channel to extract nonlinear features.
A genetic algorithm (GA) optimized EEG channel selection, with CNN performance as fitness.
Leave-one-subject-out cross-validation was used for cross-subject validation.

Main Results:

The proposed GA-CNN method, using a subset of EEG channels, outperformed CNNs using all channels.
Recognition performance improved significantly with the optimized subset of EEG channels.
The method demonstrated effectiveness in cross-subject emotion recognition tasks.

Conclusions:

The GA-CNN approach effectively identifies optimal EEG channel subsets for emotion recognition.
This method enhances recognition accuracy and computational efficiency in EEG-based BCIs.
The study contributes novel methods for EEG classification using nonlinear features and channel optimization.