Bilateral Latent Synergy-Informed Residual sEMG Reconstruction for Incomplete Hand Gesture Recognition in Hemiparetic Patients

Accurate decoding of incomplete hand gestures from residual paretic surface electromyography (sEMG) remains challenging after stroke because voluntary myoelectric activity is often weak, non-stationary, and contaminated by pathological co-activation. This study proposes a Latent Synergy-Informed Purification Network (LSP-Net) for reference-guided reconstruction of residual paretic sEMG. Specifically, LSP-Net jointly models synchronous paretic- and non-paretic-side sEMG, using the non-paretic signal as both bilateral reference information and a paired waveform-level supervision target for latent residual compensation, without assuming it to represent the physiological ground truth of the paretic side. The reconstructed sEMG representations are then evaluated through a wavelet-integrated CNN-Transformer for incomplete hand gesture intention recognition. Nine participants with post-stroke hemiplegia at Brunnstrom stage III or higher performed nine bilateral hand and wrist movements. Under subject-specific calibration, LSP-Net achieved an average Pearson correlation coefficient of 0.955 and an NRMSE of 0.016, indicating close reconstruction consistency with the paired non-paretic supervision targets. Furthermore, the reconstructed representations achieved an average gesture-intention recognition accuracy of 93.62%. These results suggest that latent synergy-informed reconstruction can provide more discriminative and stable sEMG representations for subject-specific post-stroke intention decoding.