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

You might also read

Related Articles

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

Sort by
Same author

Neurodivergent influenceability in agentic AI as a contingent solution to the AI alignment problem.

PNAS nexus·2026
Same author

An Operational Framework for Affect-Adjacent Structure in Plant-Environment Interaction.

Bioengineering (Basel, Switzerland)·2026
Same author

Male and female contributions to diversity among birdwing butterfly images.

Communications biology·2024
Same author

Rebuilding the Habitable Zone from the Bottom up with Computational Zones.

Astrobiology·2024
Same author

The Ethics of Life as It Could Be: Do We Have Moral Obligations to Artificial Life?

Artificial life·2024
Same author

Autopoiesis: Foundations of life, cognition, and emergence of self/other.

Bio Systems·2023

Related Experiment Video

Updated: Nov 16, 2025

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

14.1K

Cross-Subject EEG-Based Emotion Recognition Through Neural Networks With Stratified Normalization.

Javier Fdez1, Nicholas Guttenberg1, Olaf Witkowski1

  • 1Cross Labs, Cross Compass Ltd., Tokyo, Japan.

Frontiers in Neuroscience
|February 22, 2021
PubMed
Summary

A novel stratified normalization technique improves emotion recognition from electroencephalography (EEG) signals by reducing individual differences. This method enhances cross-subject emotion classification accuracy in machine learning models.

Keywords:
EEGSEED datasetaffective computingcross-subjectdeep learningemotion recognitionfeature normalizationstratified normalization

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

9.3K
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

1.8K

Related Experiment Videos

Last Updated: Nov 16, 2025

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

14.1K
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

9.3K
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

1.8K

Area of Science:

  • Neuroscience
  • Machine Learning
  • Biomedical Engineering

Background:

  • Emotion recognition from physiological signals is a growing field with numerous applications.
  • Electroencephalography (EEG) is a promising non-invasive and low-cost method for emotion detection.
  • Inter-participant variability in EEG data necessitates complex calibration for cross-subject emotion classification.

Purpose of the Study:

  • To introduce a new participant-based feature normalization method called stratified normalization.
  • To address the challenge of inter-participant variability in EEG-based emotion recognition.
  • To improve the performance of deep neural networks for cross-subject emotion classification.

Main Methods:

  • Developed and applied stratified normalization for feature normalization in deep neural networks.
  • Utilized the SEED dataset, comprising 62-channel EEG recordings from 15 participants.
  • Compared stratified normalization with standard batch normalization for training.

Main Results:

  • Stratified normalization significantly outperformed standard batch normalization in cross-subject emotion classification.
  • The multitaper method for EEG feature extraction yielded the highest performance.
  • Achieved 91.6% accuracy for binary (positive/negative) and 79.6% for ternary (positive/negative/neutral) emotion classification.

Conclusions:

  • Stratified normalization effectively reduces inter-participant variability while preserving emotion-related information in EEG signals.
  • The proposed method offers significant benefits for developing robust cross-subject EEG-based emotion recognition systems.
  • This research highlights the potential of stratified normalization for advancing affective computing.