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Proceedings Paper

Fast brain control systems for electric wheelchair using support vector machine
Author(s): Ivan Halim Parmonangan; Jennifer Santoso; Widodo Budiharto; Alexander Agung Santoso Gunawan
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Paper Abstract

This paper proposes a technology which enables healthy human brain to control electronic wheelchair movement. The method involves acquiring electroencephalograph (EEG) data from specific channels using Emotiv Software Development Kit (SDK) into Windows based application in a tablet PC to be preprocessed and classified. The aim of this research is to increase the accuracy rate of the brain control system by applying Support Vector Machine (SVM) as machine learning algorithm. EEG samples are taken from several respondents with disabilities but still have healthy brain to pick most suitable EEG channel which will be used as a proper learning input in order to simplify the computational complexity. The controller system based on Arduino microcontroller and combined with .NET based software to control the wheel movement. The result of this research is a brain-controlled electric wheelchair with enhanced and optimized EEG classification.

Paper Details

Date Published: 11 July 2016
PDF: 6 pages
Proc. SPIE 10011, First International Workshop on Pattern Recognition, 100111N (11 July 2016); doi: 10.1117/12.2243126
Show Author Affiliations
Ivan Halim Parmonangan, Bina Nusantara Univ. (Indonesia)
Jennifer Santoso, Bina Nusantara Univ. (Indonesia)
Widodo Budiharto, Bina Nusantara Univ. (Indonesia)
Alexander Agung Santoso Gunawan, Bina Nusantara Univ. (Indonesia)


Published in SPIE Proceedings Vol. 10011:
First International Workshop on Pattern Recognition
Xudong Jiang; Guojian Chen; Genci Capi; Chiharu Ishll, Editor(s)

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