Open Access Journal

ISSN : 2394-6849 (Online)

International Journal of Engineering Research in Electronics and Communication Engineering(IJERECE)

Monthly Journal for Electronics and Communication Engineering

Open Access Journal

International Journal of Engineering Research in Electronics and Communication Engineering(IJERECE)

Monthly Journal for Electronics and Communication Engineering

ISSN : 2394-6849 (Online)

Brain-Wave Controlled Automated Wheelchair

Author : Sachin C Dhannur 1 Sherwal Tom 2 Thallapalli Srikanth 3 Ullas Kumar K 4 Radhika K R 5

Date of Publication :26th September 2018

Abstract: This project attempts to implement an Arduino robot to simulatea brainwave-controlled wheelchair for paralyzed patients with an improved controlling method. The robot should be able to move freely in anywhere under the control of the user and it is not required to predefine any map or path. An accurate and natural controlling method is provided, and the user can stop the robot any time immediately to avoid risks or danger. This project is using a low-cost brainwave-reading headset, which has only a single lead electrode (Neurosky mind wave headset) to collect the EEG signal. BCI will be developed by sending the EEG signal to the Arduino Mega and control the movement of the robot. This project used the eye blinking as the robot controlling method as the eye blinking will cause a significant pulse in the EEG signal. By using the neural network to classify the blinking signal and the noise, the user can send the command to control the robot by blinking twice in a short period of time. The robot will be evaluated by driving in different places to test whether it can follow the expected path, avoid the obstacles, and stop on a specific position.

Reference :

    1. Puviarasi R, Ramalingam M, Chinnavan E. Self assistive technology for disabled people – voice controlled wheel chair and home automation system. IAES International Journal of Robotics and Automation (IJRA). 2014; 3(1).
    2. Tan D, Nijholt A. Brain-Computer Interfaces and HumanComputerInteraction. Brain-Computer Interfaces HumanComputer Interaction Series. 2010: 3-19.
    3. Siswoyo A, Arief Z, Sulistijono IA. Application of Artificial Neural Networks in Modeling Direction Wheelchairs Using Neurosky Mindset Mobile (EEG) Device. EMITTER International Journal of Engineering Technology. 2017; 5(1).

Recent Article


DOI : 10.36647/ijerece/0000