Vision-aided brain-machine interface training system for robotic arm control and clinical application on two patients with cervical spinal cord injury.
Journal:
Biomedical engineering online
Published Date:
Feb 11, 2019
Abstract
BACKGROUND: While spontaneous robotic arm control using motor imagery has been reported, most previous successful cases have used invasive approaches with advantages in spatial resolution. However, still many researchers continue to investigate methods for robotic arm control with noninvasive neural signal. Most of noninvasive control of robotic arm utilizes P300, steady state visually evoked potential, N2pc, and mental tasks differentiation. Even though these approaches demonstrated successful accuracy, they are limited in time efficiency and user intuition, and mostly require visual stimulation. Ultimately, velocity vector construction using electroencephalography activated by motion-related motor imagery can be considered as a substitution. In this study, a vision-aided brain-machine interface training system for robotic arm control is proposed and developed.