Kinesthetic Motor Imagery Based Brain-Computer Interface for Power Wheelchair Manoeuvring

Authors

  • Jackie T. School of Mechatronic Engineering, University Malaysia Perlis, Perlis, Malaysia
  • Paulraj M.P. School of Mechatronic Engineering, University Malaysia Perlis, Perlis, Malaysia
  • A.H. Adom School of Mechatronic Engineering, University Malaysia Perlis, Perlis, Malaysia
  • M.S. Abdul Majid School of Mechatronic Engineering, University Malaysia Perlis, Perlis, Malaysia

Keywords:

Artificial Neural Network, Brain-Computer Interface, Kinesthetic Motor Imagery, Powered Wheelchair,

Abstract

Patients who are suffering from diseases like motor neurone diseases (MND), or trauma such as spinal cord injury (SCI), and amputation is not able to move. This paper presents work on combining the power wheelchair designed to aid the movement of the disabled patient, and a Brain-Computer Interface can be used to replace conventional joystick so that it can be controlled without using hands. The brain signal emanated during Motor Imagery tasks can be converted into control signal for power wheelchair manoeuvring. In this research, five subjects are requested to perform six Kinesthetic Motor Imagery tasks, and Electroencephalography (EEG) signals are recorded. The elliptic filter was used to remove power line noise. Three features, namely Fractal dimension (FD), Mel-frequency Cepstral Coefficients (MFCCs) and a combined feature of FD with MFCCs were extracted and evaluated by using Multilayer Perceptron Neural Network (MLPNN). The Levenberg-Marquardt training algorithm is used to train the networks, and the classification result of the MLPNN using a combined feature of FD with MFCCs achieved an average accuracy of 91.7%. The developed model is tested and evaluated with the simulated virtual environment created by MATLAB graphical user interface (GUI). The result suggests that the combined feature of FD with MFCCs and MLPNN can be used to classify Motor Imagery signal for directional control of powered wheelchair.

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Published

2018-05-30

How to Cite

T., J., M.P., P., Adom, A., & Abdul Majid, M. (2018). Kinesthetic Motor Imagery Based Brain-Computer Interface for Power Wheelchair Manoeuvring. Journal of Telecommunication, Electronic and Computer Engineering (JTEC), 10(1-15), 23–27. Retrieved from https://jtec.utem.edu.my/jtec/article/view/4040

Issue

Vol. 10 No. 1-15: Exploring Man-Machines Compliance in Robotics and Image Processing

Section

Articles

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