ORIGINAL ARTICLE
Year : 2022 | Volume
: 9 | Issue : 3 | Page : 96--106
Design of magnetoencephalography-based brain–machine interface control methodology through time-varying cortical neural connectivity and extreme learning machine
Caglar Uyulan
Department of Mechanical Engineering, Faculty of Engineering and Architecture, İzmir Katip Celebi University, İzmir, Turkey
Correspondence Address:
Caglar Uyulan
Department of Mechanical Engineering, Faculty of Engineering and Architecture, İzmir Katip Celebi University, İzmir
Turkey
Introduction: Human-machine interfaces (HMIs) can improve the quality of life for physically disabled users. This study proposes a noninvasive BMI design methodology to control a robot arm using MEG signals acquired during the user's imagined wrist movements in four directions. Methods: The BMI uses the partial directed coherence measure and a time-varying multivariate adaptive autoregressive model to extract task-dependent features for mental task discrimination. An extreme learning machine is used to generate a model with the extracted features, which is used to control the robot arm for rehabilitation or assistance tasks for motor-impaired individuals. Results: The classification results show that the proposed BMI methodology is a feasible solution with good performance and fast learning speed. Discussion: The proposed BMI methodology is a promising solution for rehabilitation or assistance systems for motor-impaired individuals. The BMI provides satisfactory classification performance at a fast learning speed.
How to cite this article:
Uyulan C. Design of magnetoencephalography-based brain–machine interface control methodology through time-varying cortical neural connectivity and extreme learning machine.J Neurobehav Sci 2022;9:96-106
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How to cite this URL:
Uyulan C. Design of magnetoencephalography-based brain–machine interface control methodology through time-varying cortical neural connectivity and extreme learning machine. J Neurobehav Sci [serial online] 2022 [cited 2023 Mar 31 ];9:96-106
Available from: http://www.jnbsjournal.com/article.asp?issn=2149-1909;year=2022;volume=9;issue=3;spage=96;epage=106;aulast=Uyulan;type=0 |
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