Deep learning-based surface electromyography (sEMG) gesture recognition is frequently bottlenecked by data scarcity and limited subject diversity. While synthetic data generation via Generative Adversarial Networks (GANs) and diffusion models has emerged as a promising augmentation strategy, these approaches often face challenges regarding training stability or inference efficiency. To bridge this gap, we propose EMGFlow, a conditional sEMG generation framework. To the best of our knowledge, this is the first study to investigate the application of Flow Matching (FM) and continuous-time generative modeling in the sEMG domain. To validate EMGFlow across three benchmark sEMG datasets, we employ a unified evaluation protocol integrating feature-based fidelity, distributional geometry, and downstream utility. Extensive evaluations show that EMGFlow outperforms conventional augmentation and GAN baselines, and provides stronger standalone utility than the diffusion baselines considered here under the train-on-synthetic test-on-real (TSTR) protocol. Furthermore, by optimizing generation dynamics through advanced numerical solvers and targeted time sampling, EMGFlow achieves improved quality-efficiency trade-offs. Taken together, these results suggest that Flow Matching is a promising and efficient paradigm for addressing data bottlenecks in myoelectric control systems. Our code is available at: https://github.com/Open-EXG/EMGFlow.

翻译：基于深度学习的表面肌电信号（sEMG）手势识别常受限于数据稀缺和受试者多样性不足。尽管通过生成对抗网络（GANs）和扩散模型生成合成数据已成为一种有前景的数据增强策略，但这些方法在训练稳定性或推理效率方面仍面临挑战。为解决这一问题，我们提出了EMGFlow——一种条件式sEMG生成框架。据我们所知，这是首个探索流匹配（Flow Matching, FM）及连续时间生成建模在sEMG领域应用的研究。为在三个基准sEMG数据集上验证EMGFlow，我们采用统一评估协议，综合基于特征保真度、分布几何特性及下游实用性的度量标准。大量评估表明，EMGFlow性能优于传统数据增强方法和GAN基线模型，并在“合成训练-真实测试（TSTR）”协议下，比所考虑的扩散基线模型展现出更强的独立实用性。此外，通过结合先进数值求解器和时序采样优化生成动态过程，EMGFlow实现了质量-效率权衡的改进。综上，这些结果表明流匹配是解决肌电控制系统数据瓶颈的一种有前景且高效的范式。我们的代码开源在：https://github.com/Open-EXG/EMGFlow。