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.
Parmonangan I. H, Santoso J, Budiharto D. W and Gunawan D. A. A. S. (2016). Fast Brain Control Systems for Electric Wheelchair using Support Vector Machine (SCOPUS). SPIE - The International Society for Optical Engineering/International Conference on Robotics and Vision (ICRV 2016), 1-7. Tokyo, Japan: SPIE
optimization, electroencephalograph, channels, sensitivity