TY - GEN
T1 - Towards a brain-computer interface for dexterous control of a multi-fingered prosthetic hand
AU - Acharya, Soumyadipta
AU - Aggarwal, Vikram
AU - Tenore, Francesco
AU - Shin, Hyun Chool
AU - Etienne-Cummings, Ralph
AU - Schieber, Marc H.
AU - Thakor, Nitish V.
PY - 2007
Y1 - 2007
N2 - Recent advances in Brain-Computer Interfaces (BCI) have enabled direct neural control of robotic and prosthetic devices. However, it remains unknown whether cortical signals can be decoded in real-time to replicate dexterous movements of individual fingers and the wrist. In this study, single unit activity from 115 task-related neurons in the primary motor cortex (M1) of a trained rhesus monkey were recorded, as it performed individuated movements of the fingers and wrist of the right hand. Virtual multi-unit ensembles, or voxels, were created by randomly selecting contiguous subpopulations of these neurons. Non-linear hierarchical filters using Artificial Neural Networks (ANNs) were designed to asynchronously decode the activity from multiple virtual ensembles, in real-time. The decoded output was then used to actuate individual fingers of a robotic hand. An average real-time decoding accuracy of greater than 95 % was achieved with all neurons from randomly placed voxels containing 48 neurons, and up to 80% with as few as 25 neurons. These results suggest that dexterous control of individual digits and wrist of a prosthetic hand can be achieved by real-time decoding of neuronal ensembles from the M1 hand area in primates.
AB - Recent advances in Brain-Computer Interfaces (BCI) have enabled direct neural control of robotic and prosthetic devices. However, it remains unknown whether cortical signals can be decoded in real-time to replicate dexterous movements of individual fingers and the wrist. In this study, single unit activity from 115 task-related neurons in the primary motor cortex (M1) of a trained rhesus monkey were recorded, as it performed individuated movements of the fingers and wrist of the right hand. Virtual multi-unit ensembles, or voxels, were created by randomly selecting contiguous subpopulations of these neurons. Non-linear hierarchical filters using Artificial Neural Networks (ANNs) were designed to asynchronously decode the activity from multiple virtual ensembles, in real-time. The decoded output was then used to actuate individual fingers of a robotic hand. An average real-time decoding accuracy of greater than 95 % was achieved with all neurons from randomly placed voxels containing 48 neurons, and up to 80% with as few as 25 neurons. These results suggest that dexterous control of individual digits and wrist of a prosthetic hand can be achieved by real-time decoding of neuronal ensembles from the M1 hand area in primates.
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U2 - 10.1109/CNE.2007.369646
DO - 10.1109/CNE.2007.369646
M3 - Conference contribution
AN - SCOPUS:34548782697
SN - 1424407923
SN - 9781424407927
T3 - Proceedings of the 3rd International IEEE EMBS Conference on Neural Engineering
SP - 200
EP - 203
BT - Proceedings of the 3rd International IEEE EMBS Conference on Neural Engineering
T2 - 3rd International IEEE EMBS Conference on Neural Engineering
Y2 - 2 May 2007 through 5 May 2007
ER -