Decoding grasp and speech signals from the cortical grasp circuit in a tetraplegic human

Sarah K. Wandelt, Spencer Kellis, David A. Bjånes, Kelsie Pejsa, Brian Lee, Charles Liu, Richard A. Andersen

Research output: Contribution to journalArticlepeer-review

15 Scopus citations

Abstract

The cortical grasp network encodes planning and execution of grasps and processes spoken and written aspects of language. High-level cortical areas within this network are attractive implant sites for brain-machine interfaces (BMIs). While a tetraplegic patient performed grasp motor imagery and vocalized speech, neural activity was recorded from the supramarginal gyrus (SMG), ventral premotor cortex (PMv), and somatosensory cortex (S1). In SMG and PMv, five imagined grasps were well represented by firing rates of neuronal populations during visual cue presentation. During motor imagery, these grasps were significantly decodable from all brain areas. During speech production, SMG encoded both spoken grasp types and the names of five colors. Whereas PMv neurons significantly modulated their activity during grasping, SMG's neural population broadly encoded features of both motor imagery and speech. Together, these results indicate that brain signals from high-level areas of the human cortex could be used for grasping and speech BMI applications.

Original languageEnglish (US)
Pages (from-to)1777-1787.e3
JournalNeuron
Volume110
Issue number11
DOIs
StatePublished - Jun 1 2022

Keywords

  • brain-machine interfaces
  • grasp decoding
  • single-unit recording
  • somatosensory cortex
  • speech decoding
  • supramarginal gyrus
  • ventral premotor cortex

ASJC Scopus subject areas

  • Neuroscience(all)

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