Paper
Real Time Decoding for Brain-Machine Interface Applications
- Authors:
- Jonathan Becedas; Rodrigo Quian Quiroga
- Abstract
- There is substantial evidence that it is possible to predict movement intentions from single cell recordings in monkeys and since more recently, in humans. Such predictions, using decoding algorithms, have a large potential for clinical applications in order to drive robotic devices to be used by paralyzed patients or amputees. In spite of these advances, it is still not clear how accurate and practical the movements obtained from real neuronal devices could be. In this work, an original decoding method to perform movements to different locations was proposed and studied in realistic simulations. The method provides a high level control command to a Brain-Machine Interface device, which is a precise estimation of the target location as a function of the number of recorded neurons. Finally the method was applied to a 7 Degrees of Freedom (DOF) anthropomorphic robotic arm for reaching and grasping an object.
- Keywords
- Brain-Machine Interface; Neuroprosthetic Devices; Neural Decoding; Parietal Cortex
- StartPage
- 20
- EndPage
- 32
- Doi