Jove
Visualize
Contact Us

Related Concept Videos

Physiology of Emotion01:20

Physiology of Emotion

4.5K
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...
4.5K
Labeling Emotion01:20

Labeling Emotion

976
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...
976
Cognitive Theories: Schachter-Singer Theory of Emotion01:20

Cognitive Theories: Schachter-Singer Theory of Emotion

2.5K
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...
2.5K

You might also read

Related Articles

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

Sort by
Same author

Spin radical enhanced magnetocapacitance effect in intermolecular excited states.

The journal of physical chemistry. B·2013
Same author

Recent developments in stir bar sorptive extraction.

Analytical and bioanalytical chemistry·2013
Same author

Discovery of MK-8742: an HCV NS5A inhibitor with broad genotype activity.

ChemMedChem·2013
Same author

Magnetic polycarbonate microspheres for tumor-targeted delivery of tumor necrosis factor.

Drug delivery·2013
Same author

A study on validity of cortical alpha connectivity for schizophrenia.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2013
Same author

Myosin light chain 2-based selection of human iPSC-derived early ventricular cardiac myocytes.

Stem cell research·2013
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 Experiment Video

Updated: Apr 13, 2026

Assessing the Multiple Dimensions of Engagement to Characterize Learning: A Neurophysiological Perspective
13:57

Assessing the Multiple Dimensions of Engagement to Characterize Learning: A Neurophysiological Perspective

Published on: July 1, 2015

12.4K

FedKDC: Consensus-Driven Knowledge Distillation for Personalized Federated Learning in EEG-Based Emotion Recognition.

Xihang Qiu, Wanyong Qiu, Ye Zhang

    IEEE Journal of Biomedical and Health Informatics
    |April 16, 2025
    PubMed
    Summary

    Federated learning (FL) for electroencephalogram (EEG) emotion recognition is improved by FedKDC. This framework tackles data and model heterogeneity, enhancing accuracy and convergence speed in smart healthcare.

    More Related Videos

    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

    2.5K
    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

    1.3K

    Related Experiment Videos

    Last Updated: Apr 13, 2026

    Assessing the Multiple Dimensions of Engagement to Characterize Learning: A Neurophysiological Perspective
    13:57

    Assessing the Multiple Dimensions of Engagement to Characterize Learning: A Neurophysiological Perspective

    Published on: July 1, 2015

    12.4K
    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

    2.5K
    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

    1.3K

    Area of Science:

    • Artificial Intelligence
    • Machine Learning
    • Neuroscience

    Background:

    • Federated learning (FL) enables secure, decentralized training for electroencephalogram (EEG)-based emotion recognition.
    • Traditional FL struggles with model and data heterogeneity in healthcare, impacting convergence and performance.
    • Heterogeneity arises from varied computational resources and diverse EEG data across institutions.

    Purpose of the Study:

    • To propose FedKDC, a novel FL framework addressing heterogeneity challenges in EEG emotion recognition.
    • To enhance model convergence speed and reduce performance degradation caused by data and model variations.
    • To improve the security and efficiency of collaborative EEG data analysis in smart healthcare.

    Main Methods:

    • Developed FedKDC, a framework integrating clustered knowledge distillation (CKD) with consensus-based distributed learning.
    • Implemented intraclass distillation for faster convergence and interclass distillation to mitigate heterogeneity.
    • Introduced DriftGuard mechanism to combat client drift and an entropy reducer for aggregated knowledge.

    Main Results:

    • FedKDC demonstrated effectiveness on SEED, SEED-IV, SEED-FRA, and SEED-GER datasets under heterogeneous conditions.
    • Achieved a maximum average accuracy of 85.2% in emotion recognition, outperforming existing FL frameworks.
    • Showcased superior convergence efficiency, characterized by faster and more stable convergence rates.

    Conclusions:

    • FedKDC effectively addresses model and data heterogeneity in FL for EEG emotion recognition.
    • The proposed framework offers enhanced accuracy, faster convergence, and improved stability.
    • FedKDC represents a significant advancement for secure and efficient collaborative emotion recognition in smart healthcare.