Journal
IEEE TRANSACTIONS ON ROBOTICS
Volume 36, Issue 1, Pages 78-91Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TRO.2019.2943072
Keywords
Decoding; Electroencephalography; Task analysis; Mobile robots; Navigation; Performance evaluation; Brain-machine interface (BMI); control framework; motor imagery (MI); neurorobotics
Categories
Funding
- Hasler Foundation, Bern, Switzerland [17061]
- Swiss NationalCentre ofCompetence in Research (NCCR) Robotics
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Despite 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.
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