基于监督神经气体网络的生理情绪识别数据合成数据生成
Synthetic Data Generation by Supervised Neural Gas Network for Physiological Emotion Recognition Data
AI总结：
针对生理信号情绪识别中的数据稀缺问题，本研究提出基于监督神经气体（SNG）网络的合成数据生成方法，在保持数据内在模式的同时显著提升处理速度，并在多数对比模型中表现更优。
摘要：
数据稀缺仍是使用生理信号进行情绪识别领域的一项重大挑战，因为隐私问题和后勤限制往往阻碍了获取全面且多样化数据集。这一局限限制了鲁棒情绪识别模型的开发与泛化，使得有效的合成数据生成方法的需求更为迫切。从脑电（EEG）、心电（ECG）和皮电（GSR）等生理信号中识别情绪，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，本研究提出了一种使用监督神经气体（Supervised Neural Gas, SNG）网络进行合成数据生成的创新方法，该方法在速度上相比 Conditional VAE、Conditional GAN、diffusion model 和 Variational LSTM 等既有模型展现出显著优势。神经气体网络以基于拓扑和特征空间邻近性组织数据的适应性而闻名，为生成保留生理情绪数据内在模式的、贴近现实的合成数据集提供了鲁棒框架。我们对 SNG 的实现高效处理输入数据，生成紧密模仿原始数据分布的合成实例，这一点通过对比精度评估得到了验证。在实验中，尽管我们的方法并未在所有模型上全面胜出，但它在大多数被评估模型中取得了更优性能，并在处理时间上提供了显著改善。这些结果凸显了在情绪识别应用中使用 SNG 网络进行快速、高效且有效合成数据生成的潜力。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
