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.

翻译：情感识别是一种通过技术手段从面部表情、语音模式、身体动作及脑电等生理信号等多种数据源中识别与分类人类情感的技术过程。这些生理指标虽数据丰富，却因其复杂性和多变性带来挑战，需要采用复杂的特征选择与提取方法。神经气体网络作为一种无监督学习算法，能有效适应无预设网格结构的输入空间，从而改善从生理数据中提取特征的效果。此外，模糊逻辑的引入通过模拟人类决策的推理机制，使系统能够处理模糊数据。将粒子群优化算法与XGBoost结合，可通过高效的超参数调优与决策过程优化来提升模型性能。本研究探索了神经气体网络、XGBoost、粒子群优化算法和模糊逻辑的集成方法，以利用生理信号增强情感识别效果。我们的研究针对三个关键问题：基于粒子群优化和模糊逻辑的XGBoost改进方案、神经气体网络在特征选择中的有效性，以及PSO-模糊XGBoost分类器与标准基准的性能比较。实验结果表明，所提方法能提升情感识别系统的准确率，并在使用多数分类器时优于其他特征选择技术，这对情感识别技术的理论发展与实际应用均具有重要意义。