Cortical decoding of individual finger and wrist kinematics for an upper-limb neuroprosthesis.

Vikram Aggarwal, Francesco Tenore, Soumyadipta Acharya, Marc H. Schieber, Nitish V. Thakor

Research output: Contribution to journalArticle

Abstract

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.

Original languageEnglish (US)
Pages (from-to)4535-4538
Number of pages4
JournalConference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference
StatePublished - 2009
Externally publishedYes

Fingerprint

Wrist
Biomechanical Phenomena
Upper Extremity
Fingers
Decoding
Kinematics
Hand
Strain gages
Prosthetics
Macaca mulatta
Linear regression
Kalman filters
Primates
Neurons
Linear Models
Arm
Trajectories
Research

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition
  • Signal Processing
  • Biomedical Engineering
  • Health Informatics

Cite this

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title = "Cortical decoding of individual finger and wrist kinematics for an upper-limb neuroprosthesis.",
abstract = "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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AU - Tenore, Francesco

AU - Acharya, Soumyadipta

AU - Schieber, Marc H.

AU - Thakor, Nitish V.

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