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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-Leal^1,2, Cecilia Orozco-Romo¹

¹School of Engineering and Sciences, Mechatronics Department, Tecnológico de Monterrey, Monterrey, Mexico.

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|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.

Keywords:

Descartes EEG PCA Random Forest emotion recognition humanities neurohumanities real-time

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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.