Accurate and responsive myoelectric prosthesis control typically relies on complex, dense multi-sensor arrays, which limits consumer accessibility. This paper presents a novel, data-efficient deep learning framework designed to achieve precise and accurate control using minimal sensor hardware. Leveraging an external dataset of 8 subjects, our approach implements a hybrid Transformer optimized for sparse, two-channel surface electromyography (sEMG). Unlike standard architectures that use fixed positional encodings, we integrate Time2Vec learnable temporal embeddings to capture the stochastic temporal warping inherent in biological signals. Furthermore, we employ a normalized additive fusion strategy that aligns the latent distributions of spatial and temporal features, preventing the destructive interference common in standard implementations. A two-stage curriculum learning protocol is utilized to ensure robust feature extraction despite data scarcity. The proposed architecture achieves a state-of-the-art multi-subject F1-score of 95.7% $\pm$ 0.20% for a 10-class movement set, statistically outperforming both a standard Transformer with fixed encodings and a recurrent CNN-LSTM model. Architectural optimization reveals that a balanced allocation of model capacity between spatial and temporal dimensions yields the highest stability. Furthermore, while direct transfer to a new unseen subject led to poor accuracy due to domain shifts, a rapid calibration protocol utilizing only two trials per gesture recovered performance from 21.0% $\pm$ 2.98% to 96.9% $\pm$ 0.52%. By validating that high-fidelity temporal embeddings can compensate for low spatial resolution, this work challenges the necessity of high-density sensing. The proposed framework offers a robust, cost-effective blueprint for next-generation prosthetic interfaces capable of rapid personalization.

翻译：精确且响应迅速的肌电假肢控制通常依赖于复杂的密集多传感器阵列，这限制了消费者的可及性。本文提出了一种新颖的数据高效深度学习框架，旨在使用最少的传感器硬件实现精确控制。利用包含8名受试者的外部数据集，我们的方法实现了一种针对稀疏双通道表面肌电信号优化的混合Transformer。与使用固定位置编码的标准架构不同，我们集成了Time2Vec可学习时序嵌入来捕捉生物信号固有的随机时序扭曲。此外，我们采用了一种归一化加性融合策略，该策略对齐了空间与时间特征的潜在分布，避免了标准实现中常见的破坏性干扰。采用两阶段课程学习协议以确保在数据稀缺情况下的鲁棒特征提取。所提出的架构在10类动作集上实现了最先进的多受试者F1分数95.7% $\pm$ 0.20%，在统计上显著优于采用固定编码的标准Transformer和循环CNN-LSTM模型。架构优化表明，在空间与时间维度之间平衡分配模型容量可获得最高的稳定性。此外，尽管由于域偏移导致直接迁移到新的未见受试者时准确率较低（21.0% $\pm$ 2.98%），但采用每个手势仅需两次试验的快速校准协议可将性能恢复至96.9% $\pm$ 0.52%。通过验证高保真时序嵌入能够补偿低空间分辨率，这项工作对高密度传感的必要性提出了挑战。所提出的框架为能够快速个性化的下一代假肢接口提供了一个鲁棒且经济高效的蓝图。