arXiv (All)
open access
Neuromotor Hierarchy Network: Physiological Inductive Biases for Robust Generalization in sEMG Decoding
Surface electromyography (sEMG) provides a wearable, noninvasive interface to neuromuscular activity for movement decoding and human-computer interaction. Population-scale decoding remains difficult because the relationship between sEMG and neuromuscular activity varies across users and sessions, wh
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- From Continuous sEMG Signals to Discrete Muscle State Tokens: A Robust and Interpretable Representation Framework#1084144
- EFormer: Temporally Aligned Local Correction for Continuous sEMG-Based Hand Pose Tracking#1143533
- Neuromuscular modularity and behavioral correlates of motor control#275089
- Motor Imagery on Children With DMD on Gait and Balance Functions#994209
- Brain-Language-Action (BLA) Models: Language-Conditioned EEG for Robotics Control#793195
Record · ID 1217088
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive