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

Physiology of Emotion

Physiology of Emotion

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...

You might also read

Related Articles

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

Sort by

Same author

Divergent Trajectories of the Burden of MASLD Attributable to High Fasting Plasma Glucose in China and Globally: A GBD Study 1990-2021 and Projections to 2046.

Gastroenterology research and practice·2026

Same author

Cellular Heterogeneity in Erectile Dysfunction: An In-silico Analysis Revealing PER1, BHLHE40, NFIL3, and KLF10 as Circadian Rhythm Genes.

Current medicinal chemistry·2026

Same author

A Roadmap to Navigate the Future of Neural Engineering.

Journal of neural engineering·2026

Same author

Extracellular electron uptake by Geobacter from solid donors: Biocorrosion and biohybrid systems.

Biotechnology advances·2026

Same author

Digitalization-greening coupling coordination on ESG performance in energy enterprises: Mechanisms, heterogeneity and economic consequences.

Journal of environmental management·2026

Same author

Simulation of field test on temperature variation induced by single-well leachate pumping in landfills.

Waste management & research : the journal of the International Solid Wastes and Public Cleansing Association, ISWA·2026

Same journal

Cortex-anchored sensor-space harmonics for event-related EEG.

Journal of neural engineering·2026

Same journal

Neural mechanisms of mixed speech and grasp representation in sensorimotor cortices.

Journal of neural engineering·2026

Same journal

Developing a binary communication protocol between biological neural networks using virtual white matter.

Journal of neural engineering·2026

Same journal

Spatiotemporally distinctive astrocytic and neuronal responses to repetitive intracortical microstimulation.

Journal of neural engineering·2026

Same journal

A neural mass modelling framework for evaluating EEG source localisation of seizure activity.

Journal of neural engineering·2026

Same journal

Functional and effective connectivity methods from SEEG for characterizing epileptogenic networks in refractory epilepsy: a comprehensive review and future directions.

Journal of neural engineering·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Oct 5, 2025

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

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

Published on: May 15, 2016

Investigating EEG-based functional connectivity patterns for multimodal emotion recognition.

Xun Wu¹, Wei-Long Zheng^1,2, Ziyi Li¹

¹Center for Brain-Like Computing and Machine Intelligence, Department of Computer Science and Engineering, Shanghai Jiao Tong University, 800 Dongchuan Rd., Shanghai 200240, People's Republic of China.

Journal of Neural Engineering

|January 31, 2022

Summary

This summary is machine-generated.

This study introduces a new method for emotion recognition using electroencephalography (EEG) functional connectivity networks. The novel approach significantly improves accuracy by analyzing brain network features, outperforming single-channel methods.

Keywords:

EEG affective brain-computer interface brain functional connectivity network eye movement multimodal deep learning multimodal emotion recognition

More Related Videos

Brain Imaging Investigation of the Neural Correlates of Emotion Regulation

Brain Imaging Investigation of the Neural Correlates of Emotion Regulation

Published on: August 26, 2011

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: Oct 5, 2025

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

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

Published on: May 15, 2016

Brain Imaging Investigation of the Neural Correlates of Emotion Regulation

Brain Imaging Investigation of the Neural Correlates of Emotion Regulation

Published on: August 26, 2011

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
Machine Learning
Affective Computing

Background:

Existing emotion recognition from electroencephalography (EEG) often overlooks the functional connectivity between brain regions.
Single-channel feature extraction methods limit the comprehensive understanding of brain activity during emotional states.

Purpose of the Study:

To propose a novel algorithm for selecting emotion-relevant critical subnetworks from EEG functional connectivity networks.
To investigate the efficacy of EEG functional connectivity network features (strength, clustering coefficient, eigenvector centrality) for emotion recognition.
To develop a multimodal emotion recognition model integrating EEG connectivity features with eye movement data.

Main Methods:

Constructed brain networks using correlations between pairs of EEG signals.
Developed a critical subnetwork selection algorithm by averaging brain network matrices for specific emotion labels.
Employed deep canonical correlation analysis to integrate EEG connectivity features and eye movement data in a multimodal model.
Evaluated the model on SEED, SEED-V, and DEAP public datasets.

Main Results:

The proposed strength feature demonstrated superior performance compared to state-of-the-art single-channel features.
Achieved high classification accuracies: 95.08±6.42% (SEED), 84.51±5.11% (SEED-V), and 85.34±2.90% (arousal) / 86.61±3.76% (valence) (DEAP).
Networks constructed with 18 channels yielded performance comparable to 62-channel networks, simplifying practical application.

Conclusions:

The proposed EEG functional connectivity networks combined with an emotion-relevant critical subnetwork selection algorithm offer a successful approach to emotion recognition.
This method effectively captures inter-channel information, advancing the field of affective computing.
The findings suggest a more efficient and effective method for emotion recognition using reduced channel setups.