title: Brain-machine interface control of a manipulator using small-world neural network and shared control strategy.
abstract: The improvement of the resolution of brain signal and the ability to control external device has been the most important goal in BMI research field. This paper describes a non-invasive brain-actuated manipulator experiment, which defined a paradigm for the motion control of a serial manipulator based on motor imagery and shared control. The techniques of component selection, spatial filtering and classification of motor imagery were involved. Small-world neural network (SWNN) was used to classify five brain states. To verify the effectiveness of the proposed classifier, we replace the SWNN classifier by a radial basis function (RBF) networks neural network, a standard multi-layered feed-forward backpropagation network (SMN) and a multi-SVM classifier, with the same features for the classification. The results also indicate that the proposed classifier achieves a 3.83% improvement over the best results of other classifiers. We proposed a shared control method consisting of two control patterns to expand the control of BMI from the software angle. The job of path building for reaching the 'end' point was designated as an assessment task. We recorded all paths contributed by subjects and picked up relevant parameters as evaluation coefficients. With the assistance of two control patterns and series of machine learning algorithms, the proposed BMI originally achieved the motion control of a manipulator in the whole workspace. According to experimental results, we confirmed the feasibility of the proposed BMI method for 3D motion control of a manipulator using EEG during motor imagery.
authors: State Key Laboratory for Manufacturing Systems Engineering, School of Mechanical Engineering, Xi'an Jiaotong University, China.
authors: Ting
authors: T
authors: Li
authors: State Key Laboratory for Manufacturing Systems Engineering, School of Mechanical Engineering, Xi'an Jiaotong University, China.
authors: Jun
authors: J
authors: Hong
authors: State Key Laboratory for Manufacturing Systems Engineering, School of Mechanical Engineering, Xi'an Jiaotong University, China. Electronic address: tingli.xjtu.edu@gmail.com.
authors: Jinhua
authors: J
authors: Zhang
authors: State Key Laboratory for Manufacturing Systems Engineering, School of Mechanical Engineering, Xi'an Jiaotong University, China.
authors: Feng
authors: F
authors: Guo
mesh_terms: D000328:Adult; D001185:Artificial Intelligence; D001921:Brain / Q000502:physiology*; D001931:Brain Mapping; D062207:Brain-Computer Interfaces; D004569:Electroencephalography; D025461:Feedback, Physiological; D005260:Female; D006801:Humans; D008297:Male; D009862:Online Systems; D009949:Orientation / Q000502:physiology; D011597:Psychomotor Performance / Q000502:physiology*; D055815:Young Adult
keywords: Brain–machine interface; Electroencephalograph recognition; Manipulator control; Shared control protocol