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Predicting Exact Valence and Arousal Values from EEG.

Filipe Galvão¹, Soraia M Alarcão¹, Manuel J Fonseca¹

¹LASIGE, Faculdade de Ciências, Universidade de Lisboa, 1749-016 Lisboa, Portugal.

Sensors (Basel, Switzerland)

|June 2, 2021

Summary

This study introduces a novel model for predicting precise valence and arousal values from electroencephalography (EEG) signals. The model achieves high accuracy in emotion recognition, advancing affective computing research.

Keywords:

EEG arousal and valence prediction comparative study emotion recognition

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Area of Science:

Affective Computing
Neuroscience
Machine Learning

Background:

Emotion recognition from physiological signals, especially electroencephalography (EEG), is a growing field.
Current methods often classify limited emotional states, lacking prediction of continuous valence and arousal values.

Purpose of the Study:

To develop a subject-independent model for predicting exact valence and arousal values.
To identify optimal features, brain waves, and machine learning models for precise emotion prediction.

Main Methods:

Systematic analysis of features, brain waves (alpha, beta, gamma bands), and machine learning models.
Utilized a K-Nearest Neighbors (KNN) regressor with K=1 and Manhattan distance.
Employed differential asymmetry from the alpha band as a key feature.

Main Results:

Achieved low prediction error for valence and arousal (MAE < 0.06, RMSE < 0.16).
Demonstrated strong correlation between predicted and actual values (PCC > 0.80).
Attained 84.4% accuracy in identifying four distinct emotional classes.

Conclusions:

Established features, brain waves, and models used in emotion classification can be applied to predict continuous valence and arousal values.
The proposed model shows significant potential for more nuanced emotion recognition.
Validated findings across DEAP, AMIGOS, and DREAMER datasets.