CapsFormer: A Dual-Stream Causal-Aware Capsule-Transformer Network for EMG Signal Representation Learning

Electromyography (EMG) signals are widely applied in prosthetic control, rehabilitation training, and human-machine interaction. This places stringent requirements on gesture recognition algorithms to balance long-range temporal modeling with local pose invariance. Existing approaches typically trade off between local and global features and lack causally consistent interpretability, resulting in insufficient generalization across subjects and sessions. To address these shortcomings, we propose a novel dual-stream causal Capsule-Transformer network (CapsFormer). In the Transformer stream, we employ a "causal attention" in the self-attention mechanism to explicitly block all future information, ensuring that each time-step representation depends solely on itself and prior signals; in the Capsule stream, we leverage dynamic routing to capture local part-whole pose vectors, enhancing robustness against electrode shifts and muscle deformations. The two streams' features are then integrated in a fusion module and trained end-to-end. To validate the model's effectiveness, we evaluate it on a multi-subject dataset; results demonstrate that CapsFormer outperforms state-of-the-art models in recognition accuracy, cross-subject robustness, and interpretability. This work not only offers a new paradigm for efficient EMG signal representation but also supports causally consistent temporal signal analysis and interpretable deep learning methods, bearing significant implications for intelligent prosthetic control and human-machine interfaces.