Control Strategies of an Assistive Robot Using a Brain-Machine Interface

In this paper, two control strategies to move a planar robot arm with a non-invasive spontaneous brain-machine interface (BMI) have been compared. The BMI is based on the correlation of EEG maps and allows differentiating between two mental tasks related to motor imagery. Using the BMI, the user is able to control 2D movements of the robot arm in order to reach several goals. The first control strategy is based on a hierarchical control and the second one uses a directional control of the movement. The robot arm used is the PuParm, a force-controlled planar robot designed and developed by the nBio research group at the Miguel Hernandez University of Elche (Spain). Three goals have been placed on the experimental setup. After performing the tests, time taken to reach the goals and errors have been presented and compared, showing the advantages and disadvantages of each strategy. The evidence from this study suggests that the control of a planar robot is possible with both strategies. The hierarchical control is slower but more reliable, while the directional control is much faster and more relaxing for the user, but less precise. These findings indicate that future assistive applications like grasping daily objects in a realistic environment could be performed with this system.