| ORIGINAL ARTICLE |
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| Year : 2022 | Volume
: 9
| Issue : 3 | Page : 96-106 |
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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
 Source of Support: None, Conflict of Interest: None
DOI: 10.4103/jnbs.jnbs_35_22
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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.
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