Open AccessSpeed Control of a Wheelchair Prototype Driven by a DC Motor Through Real EEG Brain Signals
Author(s) -
Bashar Abbas Fadheel,
Ali Jafer Mahdi,
Hussain F. Jaafar,
Muhammad Shahzad Nazir,
Muntaha Sattar Obaid,
Sarah Hussein Musa
Publication year - 2020
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/671/1/012036
Subject(s) - wheelchair , headset , electroencephalography , brain–computer interface , bluetooth , computer science , interface (matter) , dc motor , microcontroller , computer hardware , simulation , engineering , psychology , neuroscience , electrical engineering , telecommunications , wireless , bubble , maximum bubble pressure method , parallel computing , world wide web
For some disabled people, Electroencephalogram (EEG) signals are used to interpret brain thinking to drive machines by creating interface between the human brain and such machines. EEG signals are naturally varied due to human thinking process, and can be manipulated to drive a wheelchair based DC motors in real-time without any muscular efforts. In this paper, EEG signals are used to control DC motors using a Brain Computer Interface (BCI) that includes an EEG sensor headset to capture brain signals. The extracted EEG signals are considered as reference signals and transmitted to a microcontroller via Bluetooth. An intelligent wheelchair (IW) with an EEG sensors is connected to an Arduino, that drives two DC motors, to control movement references to the specific EEG signals. For the proposed IW based EEG, life cycle cost (LCC), over 5 year lifetime, is about 2674$ compared with a manufactured passive wheelchair, which its LCC is 3957$. The experimental tests suggest that the proposed design of IW is efficient and low cost as well as allowing disabled people to more easily control their wheelchairs and to lead independent lives.