OPTIMIZATION OF BRAIN CONTROL SYSTEM USING TWO ELECTROENCEPHALOGRAPH CHANNELS WITH SUPPORT VECTOR MACHINE METHOD FOR ELECTRIC WHEELCHAIR

OPTIMIZATION OF BRAIN CONTROL SYSTEM USING TWO ELECTROENCEPHALOGRAPH CHANNELS WITH SUPPORT VECTOR MACHINE METHOD FOR ELECTRIC WHEELCHAIR

Oleh :  Ivan Halim Parmonangan, Jennifer Santoso, Widodo Budiharto

Most of the disabled person need intelligent wheelchair such as using his/her mind for moving with minimum effort. This paper proposes a technology which enables human brain to control the electronic wheelchair movement. We create application software in tablet PC to process electroencephalography (EEG) data using Emotiv’s Software Development Kit (SDK). The main aim is to increase the accuracy rate of the brain control system by taking raw EEG from Emotiv EPOC, filtering and applying machine learning algorithm using Support Vector Machine. For analysis stage, samples of electroencephalography (EEG) are taken from several respondent for most dominant channel analysis, which will be used as a suitable learning input. The controller system based on Arduino microcontroller and combined with our software to control the wheel movement. The result of this research is a brain-controlled electric wheelchair with enhanced and optimized EEG classification.

keywords: EEG, Neuroheadset, wheelchair, Emotiv, brain control system