PSO Fuzzy XGBoost Classifier Boosted with Neural Gas Features on EEG Signals in Emotion Recognition

TL;DR

This paper presents a novel emotion recognition system that combines Neural Gas Network feature extraction, fuzzy logic, PSO-optimized XGBoost, and neural gas features to improve accuracy on EEG signals.

Contribution

It introduces an integrated approach combining NGN, fuzzy logic, PSO, and XGBoost for enhanced emotion recognition from physiological EEG data, outperforming existing methods.

Findings

Enhanced emotion recognition accuracy with the proposed method.

Outperformed standard benchmarks and feature selection techniques.

Effective integration of NGN, fuzzy logic, and PSO in EEG-based emotion classification.

Abstract

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.