Exploring Synergies in Brain-Machine Interfaces: Compression vs. Performance.

ABSTRACT: Individuals with severe neurological injuries often rely on assistive technologies, but current methods have limitations in accurately decoding multi-degree-of-freedom (DoF) movements. Intracortical brain-machine interfaces (iBMIs) use neural signals to provide a more natural control method, but currently struggle with higher-DoF movements-something the brain handles effortlessly. It has been theorized that the brain simplifies high-DoF movement through muscle synergies, which link multiple muscles to function as a single unit. These synergies have been studied using dimensionality reduction techniques like principal component analysis (PCA), non-negative matrix factorization (NMF), and demixed PCA (dPCA) and successfully used to reduce noise and improve offline decoder stability in non-in

SUBMITTER: Cubillos LH 

PROVIDER: S-EPMC11838491 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature