The Neural Representation of Force across Grasp Types in Motor Cortex of Humans with Tetraplegia
Anisha Rastogi, Francis R. Willett, Jessica Abreu, Douglas C. Crowder, Brian Murphy, William D. Memberg, Carlos E. Vargas-Irwin, Jonathan P. Miller, Jennifer A. Sweet, Benjamin L. Walter, Paymon G. Rezaii, Sergey D. Stavisky, Leigh R. Hochberg, Krishna V. Shenoy, Jaimie M. Henderson, Robert F. Kirsch, A. Bolu Ajiboye
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