Enhanced Emotion Recognition using EEG Signals: A Comprehensive Analysis of Machine Learning and Ensemble Approaches

Emotion recognition using EEG signals has gained significant attention as a powerful approach to understanding human cognitive and affective states. This study employs machine learning algorithms to analyze EEG wave patterns elicited by emotional stimuli, utilizing advanced data processing and feature extraction methods. Models such as Random Forest, Spiking Neural Networks (SNN), and Gradient Boosting Classifiers were implemented, achieving accuracies of 98.83%, 97.66%, and an ensemble accuracy of 99.53%, respectively, with precision, recall, and F1-scores exceeding 0.99 across all emotion classes. These findings underscore the effectiveness of integrating diverse algorithms in an ensemble framework for robust emotion detection and highlight the potential of machine learning-driven EEG analysis for applications in emotion recognition and human-computer interaction.