NER 2011 Paper Abstract

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Paper FrD1.12

Aggarwal, Vikram (Johns Hopkins University), Kerr, Matthew (Johns Hopkins University), Davidson, Adam (University of Rochester), Davoodi, Rahman (University of Southern California), Loeb, Gerald (University of Southern California), Schieber, Marc (University of Rochester), Thakor, Nitish (Johns Hopkins University)

Cortical Control of Reach and Grasp Kinematics in a Virtual Environment Using Musculoskeletal Modeling Software

Scheduled for presentation during the Poster Session "Poster II: Brain Computer Interface" (FrD1), Friday, April 29, 2011, 15:00−16:30, Salon II

5th International IEEE EMBS Conference on Neural Engineering, April 27 - May 1, 2011, Cancun, Mexico

This information is tentative and subject to change. Compiled on July 15, 2018

Keywords Brain-Computer Interfaces, Neural Prostheses and Robotics, Neural Signal Processing and Modeling

Abstract

Recently there has been a major initiative to develop a Brain-Machine Interface (BMI) for dexterous control of an upper-limb neuroprosthesis. This paper describes the use of a virtual environment using Musculoskeletal Modeling Software as a model system to test and evaluate cortical algorithms for predicting reach and grasp kinematics. Simultaneous neural and motion tracking data was acquired from a non-human primate trained to perform a center-out reach-and-grasp task. A Kalman Filter was designed to simultaneously predict kinematics of the arm, hand, and fingers with high accuracy (avg r=0.83; avg RMSE=13.7%). In lieu of an advanced mechanical limb, the decoded output was used to manipulate a fully articulated 18-DoF arm in a virtual environment using MSMS. This platform lays the foundation for future closed-loop experiments with non-human primates to demonstrate a BMI for dexterous control of the hand and fingers.

 

 

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