Abstract
Intracortical brain-computer interfaces (iBCIs) have the potential to restore hand grasping and object interaction to individuals with tetraplegia. Optimal grasping and object interaction require simultaneous production of both force and grasp outputs. However, since overlapping neural populations are modulated by both parameters, grasp type could affect how well forces are decoded from motor cortex in a closed-loop force iBCI. Therefore, this work quantified the neural representation and offline decoding performance of discrete hand grasps and force levels in two human participants with tetraplegia. Participants attempted to produce three discrete forces (light, medium, hard) using up to five hand grasp configurations. A two-way Welch ANOVA was implemented on multiunit neural features to assess their modulation to force and grasp. Demixed principal component analysis (dPCA) was used to assess for population-level tuning to force and grasp and to predict these parameters from neural activity. Three major findings emerged from this work: (1) force information was neurally represented and could be decoded across multiple hand grasps (and, in one participant, across attempted elbow extension as well); (2) grasp type affected force representation within multiunit neural features and offline force classification accuracy; and (3) grasp was classified more accurately and had greater population-level representation than force. These findings suggest that force and grasp have both independent and interacting representations within cortex, and that incorporating force control into real-time iBCI systems is feasible across multiple hand grasps if the decoder also accounts for grasp type.
Highlights
Intracortical brain-computer interfaces have emerged as a promising technology to restore upper limb function to individuals with paralysis
Research Article: New Research 2 of 23 more accurately and had greater population-level representation than force. These findings suggest that force and grasp have both independent and interacting representations within cortex, and that incorporating force control into real-time Intracortical brain-computer interfaces (iBCIs) systems is feasible across multiple hand grasps if the decoder accounts for grasp type
Column 1 shows neural activity that was averaged across grasp types, resulting in trial-averaged feature traces whose differences in modulation were due to force alone
Summary
Intracortical brain-computer interfaces (iBCIs) have emerged as a promising technology to restore upper limb function to individuals with paralysis. IBCIs decode kinematic parameters from motor cortex to control the position and velocity of end effectors. These iBCIs evolved from the seminal work of Georgopoulos and colleagues, who proposed that motor cortex encodes high-level kinematics, including continuous movement directions and three-dimensional hand positions, in a global coordinate frame (Georgopoulos et al, 1982, 1986). The MGH Translational Research Center has a clinical research support agreement with Neuralink, Paradromics, and Synchron, for which L.M.H. provides consultative input. On the Scientific Advisory Boards of CTRL-Labs, Inc., MIND-X Inc., Inscopix Inc., and Heal, Inc. J.M.H. is a consultant for Neuralink, Proteus Biomedical, and Boston Scientific and serves on the Medical Advisory Board of Enspire DBS.
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