4.6 Article

Prediction of Imagined Single-Joint Movements in a Person With High-Level Tetraplegia

期刊

IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING
卷 59, 期 10, 页码 2755-2765

出版社

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TBME.2012.2209882

关键词

Brain-computer interface (BCI); BrainGate; local field potential (LFP); motor imagery; paralysis; spike

资金

  1. Department of Veterans Affairs Rehabilitation Research and Development Service (RRD)
  2. National Institute of Health (NIH) through the Eunice Kennedy Shriver National Institute on Child Health and Human Development (NICHD) [N01-HD-53403]
  3. RR&D, Office of Research and Development, Department of Veterans Affairs [B6453R, A6779I]
  4. NIH through the NICHD [RC1HD063931]
  5. NIH through the National Institute on Neurological Disorders and Stroke [R01NS-25074]
  6. NIH through the National Institute of Biomedical Imaging and Bioengineering [R01EB007401-01]
  7. NIH through the National Institute on Deafness and Other Communication Disorders [R01DC009899]
  8. NIH through the National Center for Research Resources [C06-16549-01A1]
  9. Doris Duke Charitable Foundation
  10. MGH-Deane Institute of Integrated Research on Atrial Fibrillation and Stroke
  11. Katie Samson Foundation

向作者/读者索取更多资源

Cortical neuroprostheses for movement restoration require developing models for relating neural activity to desired movement. Previous studies have focused on correlating single-unit activities (SUA) in primary motor cortex to volitional arm movements in able-bodied primates. The extent of the cortical information relevant to arm movements remaining in severely paralyzed individuals is largely unknown. We record intracortical signals using a microelectrode array chronically implanted in the precentral gyrus of a person with tetraplegia, and estimate positions of imagined single-joint arm movements. Using visually guided motor imagery, the participant imagined performing eight distinct single-joint arm movements, while SUA, multispike trains (MSP), multiunit activity, and local field potential time (LFPrms), and frequency signals (LFPstft) were recorded. Using linear system identification, imagined joint trajectories were estimated with 20-60% variance explained, with wrist flexion/extension predicted the best and pronation/supination the poorest. Statistically, decoding of MSP and LFPstft yielded estimates that equaled those of SUA. Including multiple signal types in a decoder increased prediction accuracy in all cases. We conclude that signals recorded from a single restricted region of the precentral gyrus in this person with tetraplegia contained useful information regarding the intended movements of upper extremity joints.

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