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

Labeling Emotion01:20

Labeling Emotion

582
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...
582
Physiology of Emotion01:20

Physiology of Emotion

3.0K
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...
3.0K
Neural Circuits01:25

Neural Circuits

2.6K
Neural circuits and neuronal pools are two of the main structures found in the nervous system. Neural circuits are networks of neurons that work together to carry out a specific task or process. They consist of interconnected neurons and glial cells, which provide structural and metabolic support.
Neuronal pools are collections of nerve cells with similar functions and interact through chemical and electrical signals. These pools include both interneurons (the central neural circuit nodes that...
2.6K

You might also read

Related Articles

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

Sort by
Same author

MedIntelliCare: neurodynamic-inspired AI for medical decision support by integrating retrieval-augmented generation with multimodal cognitive processing.

Cognitive neurodynamics·2026
Same author

Multi-DNBiTM: preterm labor prediction from electrohysterography signals using multi-head attention-enabled deep learning framework.

Computer methods in biomechanics and biomedical engineering·2025
See all related articles

Related Experiment Video

Updated: Jan 7, 2026

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

5.2K

C2DGCN: cross-connected distributive learning-enabled graph convolutional network for human emotion recognition using

Puja Cholke1, Shailaja Uke2, Jyoti Jayesh Chavhan3

  • 1Department of Information Technology, Vishwakarma Institute of Technology, Pune, Maharashtra 411037 India.

Cognitive Neurodynamics
|December 29, 2025
PubMed
Summary

This study introduces a novel Cross-Connected Distributive Learning-enabled Graph Convolutional Network (C2DGCN) for accurate emotion recognition from Electroencephalography (EEG) signals. The C2DGCN effectively reduces complexity and enhances feature extraction, achieving high accuracy.

Keywords:
Brain activityCross-connected distributive learningDeep learningElectroencephalogramEmotion recognition

More Related Videos

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

9.4K
Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms
08:51

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms

Published on: November 1, 2019

6.0K

Related Experiment Videos

Last Updated: Jan 7, 2026

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

5.2K
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

9.4K
Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms
08:51

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms

Published on: November 1, 2019

6.0K

Area of Science:

  • Neuroscience
  • Artificial Intelligence
  • Signal Processing

Background:

  • Emotion recognition is crucial for human-computer interaction.
  • Electroencephalography (EEG) offers accurate brain activity characterization for emotion recognition.
  • Existing deep learning methods for EEG-based emotion recognition struggle with complex features and overfitting.

Purpose of the Study:

  • To propose an effective Cross-Connected Distributive Learning-enabled Graph Convolutional Network (C2DGCN) for enhanced emotion recognition using EEG signals.
  • To address the limitations of computational complexity and overfitting in current deep learning models.
  • To improve the accuracy and efficiency of emotion recognition from brain activity.

Main Methods:

  • Developed a novel C2DGCN architecture integrating cross-connected distributive learning for extensive feature sharing and integration.
  • Employed Statistical Time-Frequency Signal descriptors for complex feature extraction and mitigation of overfitting.
  • Validated the model on SEED-IV and DEAP datasets for robust performance evaluation.

Main Results:

  • The C2DGCN achieved high accuracy (97.73% on SEED-IV, 97.66% on DEAP), sensitivity (98.32% on SEED-IV, 97.25% on DEAP), specificity (98.22% on SEED-IV, 98.07% on DEAP), and precision (98.32% on SEED-IV, 97.98% on DEAP).
  • Demonstrated significant reduction in computational complexity compared to existing methods.
  • Effectively captured complex EEG features and mitigated overfitting issues.

Conclusions:

  • The proposed C2DGCN model offers a computationally efficient and highly accurate solution for EEG-based emotion recognition.
  • The integration of cross-connected distributive learning and statistical time-frequency descriptors proves effective in overcoming limitations of previous approaches.
  • This research advances the field of affective computing through improved brain-computer interface capabilities.