Implantable brain–computer interface for neuroprosthetic-enabled volitional hand grasp restoration in spinal cord injury. Issue 4 (21st October 2021)

• Record Type: Journal Article
• Authors: Cajigas, Iahn; Davis, Kevin C; Meschede-Krasa, Benyamin; Prins, Noeline W; Gallo, Sebastian; Naeem, Jasim Ahmad; Palermo, Anne; Wilson, Audrey; Guerra, Santiago; Parks, Brandon A; Zimmerman, Lauren; Gant, Katie; Levi, Allan D; Dietrich, W Dalton; Fisher, Letitia; Vanni, Steven; Tauber, John Michael; Garwood, Indie C; Abel, John H; Brown, Emery N; Ivan, Michael E; Prasad, Abhishek; Jagid, Jonathan
• Is Part Of: Brain communications. Volume 3:Issue 4(2021)
• Publication Date: 2021-10-21
• Subjects: brain–computer interface -- spinal cord injury -- functional electrical stimulation -- cervical quadriplegia -- electrocorticography; 616
• DOI: 10.1093/braincomms/fcab248
• Languages: English
• ISSNs: 2632-1297

Abstract

Abstract: Loss of hand function after cervical spinal cord injury severely impairs functional independence. We describe a method for restoring volitional control of hand grasp in one 21-year-old male subject with complete cervical quadriplegia (C5 American Spinal Injury Association Impairment Scale A) using a portable fully implanted brain–computer interface within the home environment. The brain–computer interface consists of subdural surface electrodes placed over the dominant-hand motor cortex and connects to a transmitter implanted subcutaneously below the clavicle, which allows continuous reading of the electrocorticographic activity. Movement-intent was used to trigger functional electrical stimulation of the dominant hand during an initial 29-weeks laboratory study and subsequently via a mechanical hand orthosis during in-home use. Movement-intent information could be decoded consistently throughout the 29-weeks in-laboratory study with a mean accuracy of 89.0% (range 78–93.3%). Improvements were observed in both the speed and accuracy of various upper extremity tasks, including lifting small objects and transferring objects to specific targets. At-home decoding accuracy during open-loop trials reached an accuracy of 91.3% (range 80–98.95%) and an accuracy of 88.3% (range 77.6–95.5%) during closed-loop trials. Importantly, the temporal stability of both the functional outcomes and decoder metrics were not explored in this study. A fully implanted brain–computer interface can be safely used to reliably decode movement-intent from motor cortex, allowing for accurate volitional control of hand grasp. Abstract : Brain–computer interfaces, which translate internal electrical signals related to desired hand movement may be used to restore independence to patients with paralysis. In this work, Cajigas et al. demonstrate that a fully implanted interface can reliably decode motor commands using electrocorticography and can be deployed for home use. Such systems may provide means for self-driven portable rehabilitation and assistance. Graphical Abstract: Video Abstract: