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

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

1.0K
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...
1.0K
Physiological Theories: Cannon-Bard Theory of Emotion01:22

Physiological Theories: Cannon-Bard Theory of Emotion

2.3K
The Cannon-Bard theory of emotion, proposed by Walter Cannon and Philip Bard, challenges the notion that emotions are solely the result of physiological responses. Instead, this theory suggests that emotional experiences and physiological arousal occur simultaneously but operate through independent mechanisms. This dual response is initiated by the brain, specifically by the thalamus, which plays a critical role in processing sensory information.
Upon perceiving a stimulus, such as a dangerous...
2.3K

You might also read

Related Articles

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

Sort by
Same author

XGBoost odor prediction model: finding the structure-odor relationship of odorant molecules using the extreme gradient boosting algorithm.

Journal of biomolecular structure & dynamics·2023
Same author

Dynamic Functional Connectivity of Emotion Processing in Beta Band with Naturalistic Emotion Stimuli.

Brain sciences·2022
Same author

Cardiac-Brain Dynamics Depend on Context Familiarity and Their Interaction Predicts Experience of Emotional Arousal.

Brain sciences·2022
Same author

A New Method for Deblurring and Denoising of Medical Images using Complex Wavelet Transform.

Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference·2007

Related Experiment Videos

Multimodal fusion framework: a multiresolution approach for emotion classification and recognition from physiological

Gyanendra K Verma1, Uma Shanker Tiwary1

  • 1Indian Institute of Information Technology Allahabad, Deoghat, Jhalwa, Allahabad 211012, India.

Neuroimage
|November 26, 2013
PubMed
Summary

This study proposes a three-dimensional emotion model and uses multimodal physiological signals, including Electroencephalogram (EEG) and peripheral data, to accurately predict emotions. The novel approach achieved high accuracy, demonstrating its potential for emotion recognition.

Keywords:
Discrete wavelet transformsEEGEmotion recognitionKNNMultimodal fusionMultiresolutionPhysiological signalsSVMWavelet transforms

Related Experiment Videos

Area of Science:

  • Neuroscience
  • Affective Computing
  • Signal Processing

Background:

  • Emotion representation is complex, with existing models based on basic emotions, cognitive appraisal, or dimensional approaches.
  • Physiological signals offer objective measures for emotion detection, but effective fusion of multimodal data remains a challenge.

Purpose of the Study:

  • To investigate emotion representation models and propose a minimal continuous dimensional model.
  • To recognize and predict emotions from multimodal physiological signals using a multiresolution approach.

Main Methods:

  • Utilized the DEAP database with 32-channel Electroencephalogram (EEG) and 8 peripheral physiological signals (GSR, BVP, respiration, skin temperature, EMG, EOG).
  • Proposed a three-continuous-dimensional emotion representation model and validated it using clustering on valence, arousal, and dominance values.
  • Developed a novel multimodal fusion approach using Discrete Wavelet Transform for signal analysis and emotion classification.

Main Results:

  • Achieved average accuracies of 81.45% (SVM), 74.37% (MLP), 57.74% (KNN), and 75.94% (MMC) across four classifiers.
  • The highest accuracy of 85.46% for 'Depressing' emotion was obtained using the Support Vector Machine (SVM) classifier.
  • The proposed multimodal fusion method demonstrated high accuracy (85%) in classifying 13 emotions across 32 subjects.

Conclusions:

  • The proposed three-dimensional emotion model is effective for representing emotional states.
  • The multimodal fusion approach using Discrete Wavelet Transform significantly enhances emotion recognition accuracy from physiological signals.
  • This method shows strong potential for developing advanced emotion-aware systems.