S. F. Atashzar

This study evaluates the test-retest reliability of non-parametric lower-limb functional muscle networks in healthy subjects for controlled and lightly-controlled tasks.

This paper compares linear and nonlinear muscle networks during dynamic lower-limb tasks, finding that nonlinear networks better capture synergistic patterns in complex tasks like sit-to-stand.

This paper evaluates functional perilaryngeal muscle networks in three voice disorder patients, finding altered connectivity and asymmetry compared to healthy controls, suggesting asymmetry as a biomarker for vocal hyperfunction.

This paper introduces deep data augmentation for transient HD-sEMG to improve gesture prediction and robustness against electrode shift in neurorobotics applications.

This paper introduces X-MyoNET, a deep learning model that uses transient surface electromyography (sEMG) signals for biometric identification, achieving 74% accuracy with a compact model.

This paper introduces heterogeneous temporal dilation in LSTM for transient-phase gesture prediction using HD-sEMG, achieving state-of-the-art accuracy, faster training, and better convergence.