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

Physiology of Emotion01:20

Physiology of Emotion

2.1K
The physiology of emotions is a multifaceted process involving the autonomic nervous system, brain structures, hormones, and neurotransmitters. This intricate interplay dictates how emotions manifest in the body and influence behavior.
Autonomic Nervous System
The autonomic nervous system (ANS) plays a critical role in emotional responses by regulating involuntary physiological functions. It consists of two main components: the sympathetic and parasympathetic systems. The sympathetic system...
2.1K
Labeling Emotion01:20

Labeling Emotion

408
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...
408
Cognitive Theories: Schachter-Singer Theory of Emotion01:20

Cognitive Theories: Schachter-Singer Theory of Emotion

856
Stanley Schachter and Jerome Singer proposed the two-factor theory of emotion, which emphasizes the interplay between physiological arousal and cognitive labeling in forming emotional experiences. This theory suggests that emotions are not simply a result of physiological responses but rather a combination of these responses and the individual's cognitive interpretation of them.
Physiological Arousal and Cognitive Labeling
According to this theory, when an individual experiences...
856

You might also read

Related Articles

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

Sort by
Same author

Neuro-Ocular Amyloid Characterization in Alzheimer's Disease via Cross-Site PET-MRI and Hierarchical Cross-Attention Driven Multimodal Representation Learning.

NeuroImage·2026
Same author

Negative prompt-guided optimization: Enhancing soft prompt generalization in vision-language models.

Neural networks : the official journal of the International Neural Network Society·2026
Same author

CSF-net: a color space fusion network with self-attention-driven feature learning for feline ocular diseases classification.

Frontiers in veterinary science·2026
Same author

E2T: EEG-to-Trajectory Transformer for Motor Imagery-Based Fully-DoF Motion Prediction.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society·2026
Same author

Leveraging contextual confidence for smarter retrieval in large language models.

Neural networks : the official journal of the International Neural Network Society·2026
Same author

SSF-SET: A Discrete EEG Token-based Framework for Sleep Stage Forecasting.

IEEE journal of biomedical and health informatics·2026

Related Experiment Video

Updated: Nov 1, 2025

Exploring the Use of Isolated Expressions and Film Clips to Evaluate Emotion Recognition by People with Traumatic Brain Injury
05:51

Exploring the Use of Isolated Expressions and Film Clips to Evaluate Emotion Recognition by People with Traumatic Brain Injury

Published on: May 15, 2016

9.2K

WeDea: A New EEG-Based Framework for Emotion Recognition.

Sun-Hee Kim, Hyung-Jeong Yang, Ngoc Anh Thi Nguyen

    IEEE Journal of Biomedical and Health Informatics
    |June 22, 2021
    PubMed
    Summary
    This summary is machine-generated.

    Researchers developed the WeDea dataset to analyze emotions using electroencephalography (EEG) signals. This new resource enables real-time emotion recognition from physiological data, advancing neuroscience applications.

    More Related Videos

    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

    14.0K
    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

    4.6K

    Related Experiment Videos

    Last Updated: Nov 1, 2025

    Exploring the Use of Isolated Expressions and Film Clips to Evaluate Emotion Recognition by People with Traumatic Brain Injury
    05:51

    Exploring the Use of Isolated Expressions and Film Clips to Evaluate Emotion Recognition by People with Traumatic Brain Injury

    Published on: May 15, 2016

    9.2K
    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

    14.0K
    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

    4.6K

    Area of Science:

    • Neuroscience
    • Machine Learning
    • Signal Processing

    Background:

    • Advancements in sensing technologies and machine learning enable emotion identification through physiological signals like electroencephalography (EEG).
    • There is a growing demand for real-time acquisition and analysis of EEG signals across various sectors, including automotive, robotics, healthcare, and customer service.
    • Existing datasets may not fully capture the nuances required for comprehensive emotion analysis using EEG.

    Purpose of the Study:

    • To introduce the WeDea (Wireless-based EEG Data for emotion analysis) dataset, a novel EEG dataset based on discrete emotion theory.
    • To propose a new analytical framework for emotion recognition using the WeDea dataset.
    • To facilitate real-time emotion analysis and advance neuroscience research.

    Main Methods:

    • Collected the WeDea dataset using a portable headset device, measuring EEG signals from 30 subjects.
    • Utilized video clips selected by 15 volunteers as emotional stimuli, eliciting five distinct emotional states.
    • Recorded data while subjects watched 79 different video clips, creating a multi-way dataset.
    • Designed and implemented a framework for human emotional state recognition based on the collected EEG data.

    Main Results:

    • The WeDea dataset provides a comprehensive resource for emotion analysis, capturing physiological responses to diverse emotional stimuli.
    • The proposed analysis framework demonstrated practical effectiveness in recognizing different types of human emotions from EEG data.
    • The results indicate the dataset's potential for real-world applications and further scientific investigation.

    Conclusions:

    • The WeDea dataset is a valuable and promising resource for advancing emotion analysis through EEG signal processing.
    • The developed framework shows potential for accurate real-time emotion recognition, applicable in various domains.
    • This work contributes to the field of neuroscience by providing a new tool for studying human emotional states.