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Related Concept Videos

Labeling Emotion01:20

Labeling Emotion

107
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...
107

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Enhanced cross-dataset electroencephalogram-based emotion recognition using unsupervised domain adaptation.

Md Niaz Imtiaz1, Naimul Khan1

  • 1Department of Electrical, Computer and Biomedical Engineering, Toronto Metropolitan University, 350 Victoria St, Toronto, ON M5B 2K3, Canada.

Computers in Biology and Medicine
|November 16, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces new methods for emotion recognition using electroencephalogram (EEG) data, improving accuracy across different datasets and reducing computational costs for practical healthcare applications.

Keywords:
Brain–computer interface (BCI)Electroencephalogram (EEG)Emotion recognitionTest-time augmentation (TTA)Unsupervised domain adaptation

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Area of Science:

  • Neuroscience
  • Artificial Intelligence
  • Biomedical Engineering

Background:

  • Emotion recognition using electroencephalogram (EEG) signals is promising for healthcare and brain-computer interfaces (BCIs).
  • Cross-domain EEG emotion recognition faces challenges due to high costs of labeled data and individual signal variability.
  • Existing methods struggle with diverse datasets, leading to negative transfer and limited practical application.

Purpose of the Study:

  • To develop an improved method for classifying EEG-based emotions across domains with different data distributions.
  • To address challenges in cross-dataset emotion recognition, including subject demographics, recording devices, and stimuli variations.
  • To enhance the practical utility of EEG emotion recognition models by reducing computational burden.

Main Methods:

  • Proposed Gradual Proximity-guided Target Data Selection (GPTDS) to select reliable target domain samples for training.
  • Developed Prediction Confidence-aware Test-Time Augmentation (PC-TTA) to optimize inference performance with reduced computational cost.
  • Implemented and evaluated methods on DEAP and SEED datasets for cross-domain emotion classification.

Main Results:

  • Achieved 67.44% accuracy (DEAP to SEED) and 59.68% accuracy (SEED to DEAP), outperforming baselines by 7.09% and 6.07% respectively.
  • Demonstrated effectiveness in detecting both positive and negative emotions.
  • PC-TTA reduced computational time by a factor of 15 compared to traditional Test-Time Augmentation (TTA) approaches.

Conclusions:

  • The proposed GPTDS and PC-TTA methods significantly improve cross-domain EEG emotion recognition accuracy and efficiency.
  • The techniques effectively mitigate challenges posed by data heterogeneity and computational costs in practical applications.
  • This research advances the development of robust and cost-effective affect-sensitive systems for healthcare.