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Real-time EEG-based emotion recognition for neurohumanities: perspectives from principal component analysis and
Miguel Alejandro Blanco-Ríos1, Milton Osiel Candela-Leal1,2, Cecilia Orozco-Romo1
1School of Engineering and Sciences, Mechatronics Department, Tecnológico de Monterrey, Monterrey, Mexico.
Frontiers in Human Neuroscience
|March 28, 2024
Summary
This study developed a real-time emotion recognition system using electroencephalography (EEG) to enhance humanities education in immersive spaces. The system accurately classifies emotions, improving learning experiences.
Area of Science:
- Neuroscience
- Educational Technology
- Humanities
Background:
- Lack of interactive tools for humanities education.
- Need for integrating emotional monitoring into immersive learning environments.
- Proposal to bridge technology and humanities pedagogy.
Purpose of the Study:
- Develop a real-time, EEG-based emotion recognition system.
- Integrate emotional data into an interactive Neurohumanities Lab platform.
- Enhance learning experiences in immersive humanities contexts.
Main Methods:
- Developed a machine learning (ML) model for real-time emotion recognition.
- Utilized electroencephalography (EEG) for data acquisition.
- Employed Principal Component Analysis (PCA), Power Spectral Density (PSD), Random Forests (RF), and Extra-Trees for feature extraction and model evaluation.
- Achieved emotion classification based on Valence, Arousal, and Dominance (VAD) and Descartes' six passions.
Main Results:
- The Extra-Trees model achieved 94% accuracy in emotion recognition, surpassing existing literature (88%).
- Real-time VAD estimations were provided every 5 seconds.
- The system successfully adapted to classify six of Descartes' primary passions.
- The VAD model supports classification of over 15 distinct emotions.
Conclusions:
- The developed EEG-based system offers a novel approach to emotional monitoring in immersive humanities education.
- This technology can create more engaging and personalized learning experiences.
- The system's flexibility allows for broader applications in emotion recognition and affective computing.
