标题： 通过监督神经气体网络生成合成数据用于生理情绪识别数据 显示英文标题

标题： Synthetic Data Generation by Supervised Neural Gas Network for Physiological Emotion Recognition Data

摘要： 数据稀缺仍然是使用生理信号进行情感识别领域的一个重大挑战，由于隐私问题和后勤限制，获取全面且多样的数据集通常受到阻碍。 这一局限性限制了鲁棒情感识别模型的发展和泛化，使得有效合成数据生成方法的需求更加迫切。 从脑电图（EEG）、心电图（ECG）和皮肤电反应（GSR）等生理信号中进行情感识别，在增强人机交互和理解人类情感状态方面发挥着关键作用。 利用这些信号，本研究介绍了一种创新的合成数据生成方法，使用监督神经气体（SNG）网络，该方法在速度上相对于条件变分自编码器（Conditional VAE）、条件生成对抗网络（Conditional GAN）、扩散模型和变分LSTM等已建立的模型表现出显著的优势。 神经气体网络以其在基于拓扑和特征空间接近性组织数据方面的适应性而闻名，为生成保留生理情感数据内在模式的真实世界合成数据集提供了稳健的框架。 我们实现的SNG高效处理输入数据，创建出与原始数据分布密切相似的合成实例，并通过比较准确度评估进行了证明。 在实验中，虽然我们的方法并未在所有模型中普遍优于所有模型，但它在大多数被评估的模型中表现优异，并在处理时间上提供了显著改进。 这些结果突显了使用SNG网络在情感识别应用中快速、高效和有效的合成数据生成的潜力。 显示英文摘要

摘要： Data scarcity remains a significant challenge in the field of emotion recognition using physiological signals, as acquiring comprehensive and diverse datasets is often prevented by privacy concerns and logistical constraints. This limitation restricts the development and generalization of robust emotion recognition models, making the need for effective synthetic data generation methods more critical. Emotion recognition from physiological signals such as EEG, ECG, and GSR plays a pivotal role in enhancing human-computer interaction and understanding human affective states. Utilizing these signals, this study introduces an innovative approach to synthetic data generation using a Supervised Neural Gas (SNG) network, which has demonstrated noteworthy speed advantages over established models like Conditional VAE, Conditional GAN, diffusion model, and Variational LSTM. The Neural Gas network, known for its adaptability in organizing data based on topological and feature-space proximity, provides a robust framework for generating real-world-like synthetic datasets that preserve the intrinsic patterns of physiological emotion data. Our implementation of the SNG efficiently processes the input data, creating synthetic instances that closely mimic the original data distributions, as demonstrated through comparative accuracy assessments. In experiments, while our approach did not universally outperform all models, it achieved superior performance against most of the evaluated models and offered significant improvements in processing time. These outcomes underscore the potential of using SNG networks for fast, efficient, and effective synthetic data generation in emotion recognition applications.