Open AccessFar-field electric potentials provide access to the output from the spinal cord from wrist-mounted sensors
Author(s) -
Irene Méndez Guerra,
Deren Y. Barsakcioglu,
Ivan Vujaklija,
Daniel Z Wetmore,
Dario Farina
Publication year - 2022
Publication title -
journal of neural engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.594
H-Index - 111
eISSN - 1741-2560
pISSN - 1741-2552
DOI - 10.1088/1741-2552/ac5f1a
Subject(s) - computer science , wrist , interface (matter) , spinal cord , wearable computer , artificial neural network , brain–computer interface , forearm , reliability (semiconductor) , neurophysiology , task (project management) , artificial intelligence , neuroscience , physical medicine and rehabilitation , psychology , medicine , physics , embedded system , engineering , anatomy , power (physics) , systems engineering , bubble , electroencephalography , quantum mechanics , maximum bubble pressure method , parallel computing
Objective . Neural interfaces need to become more unobtrusive and socially acceptable to appeal to general consumers outside rehabilitation settings. Approach . We developed a non-invasive neural interface that provides access to spinal motor neuron activities from the wrist, which is the preferred location for a wearable. The interface decodes far-field potentials present at the tendon endings of the forearm muscles using blind source separation. First, we evaluated the reliability of the interface to detect motor neuron firings based on far-field potentials, and thereafter we used the decoded motor neuron activity for the prediction of finger contractions in offline and real-time conditions. Main results . The results showed that motor neuron activity decoded from the far-field potentials at the wrist accurately predicted individual and combined finger commands and therefore allowed for highly accurate real-time task classification. Significance. These findings demonstrate the feasibility of a non-invasive, neural interface at the wrist for precise real-time control based on the output of the spinal cord.