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Related Concept Videos

Cognitive Theories: Schachter-Singer Theory of Emotion01:20

Cognitive Theories: Schachter-Singer Theory of Emotion

261
Stanley Schachter and Jerome Singer proposed the two-factor theory of emotion, which emphasizes the interplay between physiological arousal and cognitive labeling in forming emotional experiences. This theory suggests that emotions are not simply a result of physiological responses but rather a combination of these responses and the individual's cognitive interpretation of them.
Physiological Arousal and Cognitive Labeling
According to this theory, when an individual experiences...
261

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Attention-Based PSO-LSTM for Emotion Estimation Using EEG.

Hayato Oka1, Keiko Ono2, Adamidis Panagiotis3

  • 1Master's Program in Information and Computer Science, Doshisha University, Kyoto 610-0394, Japan.

Sensors (Basel, Switzerland)
|January 8, 2025
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Summary

This study enhances electroencephalogram (EEG)-based emotion recognition using AI. A novel model combining Long Short-Term Memory (LSTM) with attention and Particle Swarm Optimization (PSO) significantly improved accuracy on benchmark datasets.

Keywords:
DEAPEEGLSTMPSOSEEDattention mechanismemotion estimationfour-class classificationthree-class classification

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Area of Science:

  • Artificial Intelligence
  • Neuroscience
  • Machine Learning

Background:

  • Emotion recognition has applications in healthcare, advertising, and driving.
  • Electroencephalogram (EEG)-based methods offer higher accuracy than facial or vocal analysis due to resistance to manipulation.
  • Existing EEG emotion recognition models can be improved through advanced feature extraction and parameter optimization.

Purpose of the Study:

  • To enhance the accuracy of electroencephalogram (EEG)-based emotion estimation.
  • To introduce a novel AI model that emphasizes temporal features and efficient parameter optimization.
  • To improve emotion recognition technology through advanced machine learning techniques.

Main Methods:

  • A hybrid model combining Long Short-Term Memory (LSTM) with an attention mechanism was developed.
  • Particle Swarm Optimization (PSO) was employed to optimize key LSTM parameters (units, batch size, dropout rate).
  • The model was evaluated on the DEAP and SEED benchmark datasets for emotion recognition.

Main Results:

  • The proposed model achieved 0.9409 accuracy on the DEAP dataset, exceeding the previous state-of-the-art.
  • An accuracy of 0.9732 was attained on the SEED dataset, positioning it among the highest reported results.
  • The integration of attention mechanisms and PSO significantly boosted EEG-based emotion estimation performance.

Conclusions:

  • The novel LSTM-attention-PSO model demonstrates superior performance in EEG-based emotion recognition.
  • This approach effectively leverages temporal features and optimizes model parameters for enhanced accuracy.
  • The findings contribute to the advancement of AI-driven emotion recognition technologies.