Tonin, LucaBauer, Felix ChristianMillan, Jose del R.2020-05-032020-05-032020-05-032020-02-0110.1109/TRO.2019.2943072https://infoscience.epfl.ch/handle/20.500.14299/168557WOS:000526526500006Despite the growing interest in brain-machine interface (BMI)-driven neuroprostheses, the translation of the BMI output into a suitable control signal for the robotic device is often neglected. In this article, we propose a novel control approach based on dynamical systems that was explicitly designed to take into account the nature of the BMI output that actively supports the user in delivering real-valued commands to the device and, at the same time, reduces the false positive rate. We hypothesize that such a control framework would allow users to continuously drive a mobile robot and it would enhance the navigation performance. 13 healthy users evaluated the system during three experimental sessions. Users exploit a 2-class motor imagery BMI to drive the robot to five targets in two experimental conditions: with a discrete control strategy, traditionally exploited in the BMI field, and with the novel continuous control framework developed herein. Experimental results show that the new approach: 1) allows users to continuously drive the mobile robot via BMI; 2) leads to significant improvements in the navigation performance; and 3) promotes a better coupling between user and robot. These results highlight the importance of designing a suitable control framework to improve the performance and the reliability of BMI-driven neurorobotic devices.RoboticsRoboticsdecodingelectroencephalographytask analysismobile robotsnavigationperformance evaluationbrain-machine interface (bmi)control frameworkmotor imagery (mi)neuroroboticscomputer interfacesautonomous roboticsactuated wheelchairmotor imagerydesynchronizationtext::journal::journal article::research article