Motor Imagery Intention Recognition Based on Common Spatial Pattern for Manipulator Grasping

Li，Wenjie1; Xu，Jialu2; Yan，Xiaoyu2; Lin，Chengyu2; Fu，Chenglong2

10.1007/978-3-031-13822-5_12

2022

15th International Conference on Intelligent Robotics and Applications (ICIRA ) - Smart Robotics for Society

0302-9743

1611-3349

978-3-031-13821-8

LECT NOTES ARTIF INT

13456 LNAI

125-135

AUG 01-03, 2022

null,Harbin,PEOPLES R CHINA

GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND

SPRINGER INTERNATIONAL PUBLISHING AG

With further development of brain-computer interface (BCI) technology and the BCI medicine field, people with movement disorders can be treated by using artificial limbs or some external devices such as an exoskeleton to achieve sports function rehabilitation. At present, brain-computer interfaces (BCI) based on motor imagination mainly have problems such as accuracy that need to be improved. The key to solving such problems is to extract high-quality EEG signal features and reasonably select classification algorithms. In this paper, the efficient decoding of dichotomy EEG intentions is verified by combining features such as Common Spatial Pattern (CSP) with different classifiers in the PhysioNet public data set. The results show that: CSP is used as the feature, and the support vector machine (SVM) has the best classification effect. The highest classification accuracy of five subjects reaches 87% and the average accuracy reaches 80%. At the same time, the CSP-SVM algorithm was used in the qbrobotics manipulator to conduct a grasping experiment to verify the real-time performance and effectiveness of the algorithm. This provides a new solution for the brain-computer interface to control robotic auxiliary equipment, which is expected to improve the daily life of the disabled.

Brain-computer interfaces Classification algorithm Common spatial pattern Feature extraction Motor imagery

Corresponding

English

EI ; CPCI-S

National Key R&D Program of China["2018YFB1305400","2018YFC2001601"] ; National Natural Science Foundation of China[U1913205] ; Special fund for the Cultivation of Guangdong College Students' Scientific and Technological Innovation[pdjh2022a0453]

Computer Science ; Robotics

Computer Science, Artificial Intelligence ; Robotics

WOS:000870561700012

20223412602638

Auxiliary equipment ; Biomedical signal processing ; Brain computer interface ; Classification (of information) ; Exoskeleton (Robotics) ; Feature extraction ; Image classification ; Manipulators

Information Theory and Signal Processing:716.1 ; Computer Peripheral Equipment:722.2 ; Computer Software, Data Handling and Applications:723 ; Data Processing and Image Processing:723.2 ; Robotics:731.5 ; Engineering Profession:901 ; Information Sources and Analysis:903.1

2-s2.0-85136097663

Scopus

1.Shenzhen Foreign Languages School,Shenzhen,China
2.Department of Mechanical and Energy Engineering,Southern University of Science and Technology,Shenzhen,China

Li，Wenjie,Xu，Jialu,Yan，Xiaoyu,et al. Motor Imagery Intention Recognition Based on Common Spatial Pattern for Manipulator Grasping[C]. GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND:SPRINGER INTERNATIONAL PUBLISHING AG,2022:125-135.