• Sep 20, 2019 News!IJMMM Had Implemented Online Submission System, Please Submit New Submissions through This System Only!   [Click]
  • Feb 26, 2018 News!'Writing Tips' shared by Prof. Ian McAndrew!   [Click]
  • Dec 18, 2019 News!IJMMM Vol.7, No.6 has been published with online version.   [Click]
General Information
    • ISSN: 1793-8198 (Print)
    • Abbreviated Title: Int. J. Mater. Mech. Manuf.
    • Frequency: Bimonthly
    • DOI: 10.18178/IJMMM
    • Editor-in-Chief: Prof. Ian McAndrew
    • Co-editor-in-Chief: Prof. K. M. Gupta
    • Executive Editor: Cherry L. Chen
    • Abstracting/Indexing: EI (INSPEC, IET), Chemical Abstracts Services (CAS),  ProQuest, Crossref, Ulrich's Periodicals Directory,  EBSCO.
    • E-mail ijmmm@ejournal.net

Prof. Ian McAndrew
Capitol Technology University, USA
It is my honor to be the editor-in-chief of IJMMM. I will do my best to work with the editorial team and help make this journal better.

IJMMM 2017 Vol.5(3):169-172 ISSN: 1793-8198
DOI: 10.18178/ijmmm.2017.5.3.311

EEG Waves for Robotics and Prosthesis Grasping and Motorization

Ebrahim A. Mattar
Abstract—Dexterous multi-finger robotics hands/or prosthesis hands are complicated devices to model, control, and to motorize. Modeling involves building coordinated kinematics relations, while the dynamic model involves grasping forces and optimized distributions of forces and torques at grasping locations. Over the last thirty years or more of research, a coordinated control of fingers for such devices was done analytically, however such control issues were facing few number of difficulties. Therefore, the purpose of this paper is to look at novel approach for defining grasping patters from EEG readings, then to learn-mirror such patters into robotics hand- prosthesis. We shall create an association between fingers motions, forces, and particularly detected EEG brainwaves from human. Such an association is very useful for robotics humanoids, or for prosthesis. The association between human EEG to robotics is modeled here, and it will be used for grasping by system robotic by learning (via training) a robotics multi-finger dexterous hands. In addition, such an association is also useful for controlling a prosthesis for rehabilitations purposes.

Index Terms—EEG, rehabilitation, BMI, robotic-prosthetic, patterns recognition, learning systems.

E. A. Mattar is with the College of Engineering, University of Bahrain, Sukhair, P.O. Box 32038, Kingdom of Bahrain (e-mail: ebmattar@uob.deu.bh).


Cite: Ebrahim A. Mattar, "EEG Waves for Robotics and Prosthesis Grasping and Motorization," International Journal of Materials, Mechanics and Manufacturing vol. 5, no. 3, pp. 169-172, 2017.

Copyright © 2008-2020. International Journal of Materials, Mechanics and Manufacturing. All rights reserved.
E-mail: ijmmm@ejournal.net