Searching for Optimal EMG Latent Subspace with Discriminant DoF-wise Distributions for Subject-generic Model

Recognizing hand gestures from surface electromyography (sEMG) signals is crucial for neural interfaces and human-machine interaction. However, developing subject-generic models remains challenging due to substantial inter-subject variability. Complicating matters further, the muscle groups driving gestures with varying degrees of freedom (DoFs) often overlap, producing highly convoluted feature distributions across subjects and DoFs. To address these challenges, we introduce a multi-branch autoencoder (AE) architecture that disentangles sEMG features into two latent subspaces: a DoF-specific (subject-invariant) space and a subject-specific (DoF-invariant) space. We systematically compare our approach against well-established feature projection methods: principal component analysis (PCA), kernel PCA (KPCA), linear discriminant analysis (LDA), kernel discriminant analysis (KDA), and a conventional AE, as well as two style-independent feature transformation methods: canonical correlation analysis (CCA) and spectral regression discriminant analysis (SRDA). Experimental results on 20 subjects across multiple days demonstrate that our multi-branch AE markedly improves DoF discrimination while maintaining subject invariance, leading to consistently higher inter-subject classification accuracy for all common classifiers. These findings underscore the potential of our approach for robust, user-independent sEMG-based gesture recognition.