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    Abstract
2017 (Vol. 14, Issue: 10)
Research Article

Features Selection and Pattern Classification of Electroencephalography Motor Imagery Tasks of Right Hand

1Ahmed A. Ibrahim, 2Mohammed I. Awad, 3Abdulwahab A. Alnaqi, 4Ann A. Abdel Kader and 2Farid A. Tolbah
1Department of Mechatronics Engineering, the Egyptian Academy of Engineering and Advanced Technology
2Mechanical Engineering, Faculty of Engineering, Ain Shams University,Egypt
3Department of Automotive and Marine Engineering Technology, College of Technological Studies, The Public Authority for Applied Education and Training, Kuwait
4Department of Neurophysiology, Faculty of Medicine, Cairo University, Egypt
 

DOI: 10.19026/rjaset.14.5129
Submitted Accepted Published
February 3, 2017 March 16, 2017 October 15, 2017

  How to Cite this Article:

1Ahmed A. Ibrahim, 2Mohammed I. Awad, 3Abdulwahab A. Alnaqi, 4Ann A. Abdel Kader and 2Farid A. Tolbah, 2017. Features Selection and Pattern Classification of Electroencephalography Motor Imagery Tasks of Right Hand.  Research Journal of Applied Sciences, Engineering and Technology, 14(10): 372-379.

DOI: 10.19026/rjaset.14.5129

URL: http://www.maxwellsci.com/jp/mspabstract.php?jid=RJASET&doi=rjaset.14.5129

Abstract:


This study presentsa Brain Computer Interface (BCI) approach to detect the motor intents of the disabled people with right hand amputation. Electroencephalography (EEG) Motor Imagery (MI)-based Brain Computer Interface (BCI) systems have been recently used to improve the quality of life of disabled people. However, to naturally trigger particular applications (i.e., upper limb prostheses), independent BCIs appeal further paradigms to involve realistic motor imagery tasks. This study proposes an approach to classifying imagined hand gesture tasks, including the water glass gesture and the index pointer gesture of the right hand using OPENBCI as a consumer-grade EEG acquisition device. For three subjects, the data recorded by OPENBCI were sampled with a sampling rate of 250 Hz. The Minimum Redundancy Maximum Relevance (MRMR) technique was implemented as a feature selection method along with the Support Vector Machine (SVM) algorithm for classification. By obtaining a maximum classification accuracy of 91.7%, the results showed the feasibility of such Brain Computer Interface systems to detect different motor imagery tasks for the right hand. Consequently, upper limb prostheses could be manipulated using the intended motor imagery tasks.

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    Competing interests

The authors have no competing interests.
    Open Access Policy

This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

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© The Author(s) 2017

ISSN (Online):  2040-7467
ISSN (Print):   2040-7459
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