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Bandara DSV, Arata J, Kiguchi K. Towards Control of a Transhumeral Prosthesis with EEG Signals. Bioengineering (Basel). 2018;5(2)doi: 10.3390/bioengineering5020026.
Bandara, D. S. V., Arata, J., & Kiguchi, K. (2018). Towards Control of a Transhumeral Prosthesis with EEG Signals. Bioengineering (Basel, Switzerland), 5(2), . https://doi.org/10.3390/bioengineering5020026
Bandara, D S V, et al. "Towards Control of a Transhumeral Prosthesis with EEG Signals." Bioengineering (Basel, Switzerland) vol. 5,2 (2018). doi: https://doi.org/10.3390/bioengineering5020026
Bandara DSV, Arata J, Kiguchi K. Towards Control of a Transhumeral Prosthesis with EEG Signals. Bioengineering (Basel). 2018 Mar 22;5(2). doi: 10.3390/bioengineering5020026. PMID: 29565293; PMCID: PMC6027267.
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Bioengineering (Basel). 2018 Mar 22;5(2). doi: 10.3390/bioengineering5020026.

Towards Control of a Transhumeral Prosthesis with EEG Signals.

Bioengineering (Basel, Switzerland)

D S V Bandara, Jumpei Arata, Kazuo Kiguchi

Affiliations

  1. System Engineering Laboratory, Department of Mechanical Engineering, Kyushu University, Fukuoka 819-0395, Japan. [email protected].
  2. System Engineering Laboratory, Department of Mechanical Engineering, Kyushu University, Fukuoka 819-0395, Japan. [email protected].
  3. System Engineering Laboratory, Department of Mechanical Engineering, Kyushu University, Fukuoka 819-0395, Japan. [email protected].

PMID: 29565293 PMCID: PMC6027267 DOI: 10.3390/bioengineering5020026

Abstract

Robotic prostheses are expected to allow amputees greater freedom and mobility. However, available options to control transhumeral prostheses are reduced with increasing amputation level. In addition, for electromyography-based control of prostheses, the residual muscles alone cannot generate sufficiently different signals for accurate distal arm function. Thus, controlling a multi-degree of freedom (DoF) transhumeral prosthesis is challenging with currently available techniques. In this paper, an electroencephalogram (EEG)-based hierarchical two-stage approach is proposed to achieve multi-DoF control of a transhumeral prosthesis. In the proposed method, the motion intention for arm reaching or hand lifting is identified using classifiers trained with motion-related EEG features. For this purpose, neural network and

Keywords: brain computer interface; electroencephalography; motion intention; transhumeral prosthesis; wearable robot

Conflict of interest statement

The authors declare no conflict of interest.

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