Intramuscular microelectrode arrays enable highly accurate neural decoding of finger tasks

Grison, Agnese ; Ibáñez, Jaime (Universidad de Zaragoza) ; Muceli, Silvia ; Kundu, Aritra ; Baracat, Farah Ashraf Moustafa Mahmoud ; Indiveri, Giacomo ; Donati, Elisa ; Farina, Dario

Resumen: Decoding the activity of the nervous system is a critical challenge in neuroscience and neural interfacing. In this study, we present a neuromuscular recording system that enables large-scale sampling of muscle activity using microelectrode arrays with over 100 channels embedded in forearm muscles. These arrays captured intramuscular high-density signals that were decoded into patterns of activation of spinal motoneurons. In two healthy participants, we recorded high-density intramuscular activity during single- and multi-digit contractions, revealing distinct motoneuron recruitment patterns specific to each task. Based on these patterns, we achieved perfect classification accuracy (100%) for 12 single- and multi-digit tasks and over approximately 96% accuracy for up to 16 tasks, significantly outperforming state-of-the-art electromyogram classification methods. This intramuscular high-density system and classification method represent an advancement in neural interfacing, with the potential to improve human–computer interaction and the control of assistive technologies, particularly for replacing or restoring impaired motor function.

Idioma: Inglés

DOI: 10.1098/rsfs.2025.0063

Año: 2026

Publicado en: Interface focus 16, 1 (2026), [15 pp.]
ISSN: 2042-8898

Financiación: info:eu-repo/grantAgreement/EC/HORIZON EUROPE/101077693/EU/Extracting the Human Motor Null Space from Muscles - A new framework to measure human neural activity/ECHOES
Financiación: info:eu-repo/grantAgreement/ES/MICINN/CNS2022-135366

Tipo y forma: Artículo (Versión definitiva)

Área (Departamento): Área Teoría Señal y Comunicac. (Dpto. Ingeniería Electrón.Com.)