TY - GEN
T1 - Cortical decoding of individual finger and wrist kinematics for an upper-limb neuroprosthesis
AU - Aggarwal, Vikram
AU - Tenore, Francesco
AU - Acharya, Soumyadipta
AU - Schieber, Marc H.
AU - Thakor, Nitish V.
PY - 2009
Y1 - 2009
N2 - Previous research has shown that neuronal activity can be used to continuously decode the kinematics of gross movements involving arm and hand trajectory. However, decoding the kinematics of fine motor movements, such as the manipulation of individual fingers, has not been demonstrated. In this study, single unit activities were recorded from task-related neurons in M1 of two trained rhesus monkey as they performed individuated movements of the fingers and wrist. The primates' hand was placed in a manipulandum, and strain gauges at the tips of each finger were used to track the digit's position. Both linear and non-linear filters were designed to simultaneously predict kinematics of each digit and the wrist, and their performance compared using mean squared error and correlation coefficients. All models had high decoding accuracy, but the feedforward ANN (R=0.76-0.86, MSE=0.04-0.05) and Kalman filter (R=0.68-0.86, MSE=0.04-0.07) performed better than a simple linear regression filter (0.58-0.81, 0.05-0.07). These results suggest that individual finger and wrist kinematics can be decoded with high accuracy, and be used to control a multi-fingered prosthetic hand in real-time.
AB - Previous research has shown that neuronal activity can be used to continuously decode the kinematics of gross movements involving arm and hand trajectory. However, decoding the kinematics of fine motor movements, such as the manipulation of individual fingers, has not been demonstrated. In this study, single unit activities were recorded from task-related neurons in M1 of two trained rhesus monkey as they performed individuated movements of the fingers and wrist. The primates' hand was placed in a manipulandum, and strain gauges at the tips of each finger were used to track the digit's position. Both linear and non-linear filters were designed to simultaneously predict kinematics of each digit and the wrist, and their performance compared using mean squared error and correlation coefficients. All models had high decoding accuracy, but the feedforward ANN (R=0.76-0.86, MSE=0.04-0.05) and Kalman filter (R=0.68-0.86, MSE=0.04-0.07) performed better than a simple linear regression filter (0.58-0.81, 0.05-0.07). These results suggest that individual finger and wrist kinematics can be decoded with high accuracy, and be used to control a multi-fingered prosthetic hand in real-time.
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U2 - 10.1109/IEMBS.2009.5334129
DO - 10.1109/IEMBS.2009.5334129
M3 - Conference contribution
C2 - 19964645
AN - SCOPUS:77951000314
SN - 9781424432967
T3 - Proceedings of the 31st Annual International Conference of the IEEE Engineering in Medicine and Biology Society: Engineering the Future of Biomedicine, EMBC 2009
SP - 4535
EP - 4538
BT - Proceedings of the 31st Annual International Conference of the IEEE Engineering in Medicine and Biology Society
PB - IEEE Computer Society
T2 - 31st Annual International Conference of the IEEE Engineering in Medicine and Biology Society: Engineering the Future of Biomedicine, EMBC 2009
Y2 - 2 September 2009 through 6 September 2009
ER -