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Cognitive Theories: Schachter-Singer Theory of Emotion01:20

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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.
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Affective Computing on Machine Learning-Based Emotion Recognition Using a Self-Made EEG Device.

Ngoc-Dau Mai1, Boon-Giin Lee2, Wan-Young Chung1

  • 1Department of Artificial Intelligence Convergence, Pukyong National University, Busan 48513, Korea.

Sensors (Basel, Switzerland)
|August 10, 2021
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Summary
This summary is machine-generated.

This study presents a machine learning method for emotion recognition using wearable electroencephalography (EEG) sensors. The system achieved high accuracy, demonstrating its potential for real-world affective computing applications.

Keywords:
affective computingelectroencephalogram (EEG)emotion recognitionentropy measuresmulti-layer perceptron (MLP)one-dimensional convolutional neural network (1D-CNN)support vector machine (SVM)

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Area of Science:

  • Neuroscience
  • Computer Science
  • Affective Computing

Background:

  • Emotion recognition is crucial for human-computer interaction.
  • Electroencephalography (EEG) offers a direct measure of brain activity.
  • Wearable EEG devices enable real-time affective state monitoring.

Purpose of the Study:

  • To develop and evaluate a machine learning-based affective computing method for emotion recognition.
  • To utilize a custom-designed wearable EEG device and wireless protocol.
  • To assess the performance of different classifiers and feature extraction techniques.

Main Methods:

  • Collected EEG signals from eight subjects using an eight-electrode placement (frontal and temporal lobes).
  • Extracted features using six entropy measures.
  • Classified emotions using Support Vector Machine (SVM), Multi-layer Perceptron (MLP), and 1D Convolutional Neural Network (1D-CNN).
  • Evaluated both subject-dependent and subject-independent classification strategies.

Main Results:

  • The highest average accuracies were 85.81% (subject-dependent) and 78.52% (subject-independent).
  • Optimal performance was achieved using sample entropy and a 1D-CNN classifier.
  • The T8 electrode position (right temporal lobe) was identified as the most critical for emotion classification.

Conclusions:

  • The developed EEG-based affective computing method is feasible and efficient for emotion recognition.
  • Wearable EEG systems show promise for real-world emotion detection applications.
  • Specific EEG channels and entropy measures can significantly improve classification accuracy.