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Related Concept Videos

Labeling Emotion01:20

Labeling Emotion

237
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...
237

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Related Experiment Video

Updated: Sep 13, 2025

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

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Cross-subject EEG signals-based emotion recognition using contrastive learning.

Ahmed Mohammed Alghamdi1, M Usman Ashraf2, Adel A Bahaddad3

  • 1Department of Software Engineering, College of Computer Science and Engineering, University of Jeddah, 21493, Jeddah, Saudi Arabia. amalghamdi@uj.edu.sa.

Scientific Reports
|August 3, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces a novel cross-subject contrastive learning (CSCL) scheme to improve emotion recognition using electroencephalography (EEG) brain-computer interfaces (BCIs). The CSCL method effectively addresses individual differences in EEG signals, enhancing cross-subject generalization for BCI applications.

Keywords:
Artificial IntelligenceCNNDeep learningEnsemble learning

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Area of Science:

  • Affective Computing
  • Neuroscience
  • Machine Learning

Background:

  • Electroencephalography (EEG) signals are crucial for objective emotion recognition in brain-computer interfaces (BCIs).
  • A significant challenge in EEG-based emotion recognition is the variability of signals across different subjects.
  • Existing methods struggle with generalizing across individuals, limiting BCI application reliability.

Purpose of the Study:

  • To develop a cutting-edge cross-subject contrastive learning (CSCL) scheme for robust EEG signal representation.
  • To directly address the generalization challenge across subjects in EEG-based emotion recognition.
  • To improve the effectiveness of BCIs in recognizing emotions despite individual differences.

Main Methods:

  • Introduced a novel cross-subject contrastive learning (CSCL) scheme for EEG signal representation.
  • Employed emotions and stimulus contrastive losses within hyperbolic space to capture complex patterns.
  • Designed CSCL to learn representations that effectively distinguish signals from different brain regions.

Main Results:

  • Evaluated the CSCL scheme on five datasets (SEED, CEED, FACED, MPED), achieving high accuracy rates (e.g., 97.70% on SEED).
  • Demonstrated strong effectiveness in addressing cross-subject variability in EEG signals.
  • Showcased the scheme's ability to handle label noise in EEG-based emotion recognition systems.

Conclusions:

  • The proposed CSCL scheme significantly enhances cross-subject generalization for EEG-based emotion recognition.
  • CSCL offers a promising approach to overcome individual differences in EEG signals for BCI applications.
  • This method provides a more reliable and objective system for affective computing.