  Emotion recognition is the technology-driven process of identifying and
categorizing human emotions from various data sources, such as facial
expressions, voice patterns, body motion, and physiological signals, such as
EEG. These physiological indicators, though rich in data, present challenges
due to their complexity and variability, necessitating sophisticated feature
selection and extraction methods. NGN, an unsupervised learning algorithm,
effectively adapts to input spaces without predefined grid structures,
improving feature extraction from physiological data. Furthermore, the
incorporation of fuzzy logic enables the handling of fuzzy data by introducing
reasoning that mimics human decision-making. The combination of PSO with
XGBoost aids in optimizing model performance through efficient hyperparameter
tuning and decision process optimization. This study explores the integration
of Neural-Gas Network (NGN), XGBoost, Particle Swarm Optimization (PSO), and
fuzzy logic to enhance emotion recognition using physiological signals. Our
research addresses three critical questions concerning the improvement of
XGBoost with PSO and fuzzy logic, NGN's effectiveness in feature selection, and
the performance comparison of the PSO-fuzzy XGBoost classifier with standard
benchmarks. Acquired results indicate that our methodologies enhance the
accuracy of emotion recognition systems and outperform other feature selection
techniques using the majority of classifiers, offering significant implications
for both theoretical advancement and practical application in emotion
recognition technology.
