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A Synergetic Brain-Machine Interfacing Paradigm for Multi-DOF Robot Control

Bhattacharyya, S and Shimoda, S and Hayashibe, M (2016) 'A Synergetic Brain-Machine Interfacing Paradigm for Multi-DOF Robot Control.' IEEE Transactions on Systems Man and Cybernetics: Systems, 46 (7). pp. 957-968. ISSN 2168-2216

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This paper proposes a novel brain-machine interfacing (BMI) paradigm for control of a multijoint redundant robot system. Here, the user would determine the direction of end-point movement of a 3-degrees of freedom (DOF) robot arm using motor imagery electroencephalography signal with co-adaptive decoder (adaptivity between the user and the decoder) while a synergetic motor learning algorithm manages a peripheral redundancy in multi-DOF joints toward energy optimality through tacit learning. As in human motor control, torque control paradigm is employed for a robot to be adaptive to the given physical environment. The dynamic condition of the robot arm is taken into consideration by the learning algorithm. Thus, the user needs to only think about the end-point movement of the robot arm, which allows simultaneous multijoints control by BMI. The support vector machine-based decoder designed in this paper is adaptive to the changing mental state of the user. Online experiments reveals that the users successfully reach their targets with an average decoder accuracy of over 75% in different end-point load conditions.

Item Type: Article
Uncontrolled Keywords: Brain-machine interfacing (BMI); co-adaptive decoder; joint redundancy; multijoint robot; synergetic learning control; tacit learning
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
R Medicine > RC Internal medicine > RC0321 Neuroscience. Biological psychiatry. Neuropsychiatry
Divisions: Faculty of Science and Health
Faculty of Science and Health > Computer Science and Electronic Engineering, School of
SWORD Depositor: Elements
Depositing User: Elements
Date Deposited: 06 Sep 2018 15:36
Last Modified: 06 Jan 2022 13:41

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