Journal of Medical and Biological Engineering

, Volume 39, Issue 4, pp 508–522 | Cite as

Robot Navigation Using a Brain Computer Interface Based on Motor Imagery

  • Majid AljalalEmail author
  • Ridha Djemal
  • Sutrisno Ibrahim
Original Article


An interface between a human brain and a computer (or any external device) can be implemented for interchanging orders using a brain–computer interface (BCI) system. Motor imagery (MI), which represents human intention to execute actions or movements, can be captured and analyzed using brain signals such as electroencephalograms (EEGs). The present study focuses on a synchronous control system with a BCI based on MI for robot navigation. We employ a new feature extraction technique using common spatial pattern (CSP) filtering combined with band power to form feature vectors. Linear discriminant analysis (LDA) is employed to classify two types of MI tasks (right hand and left hand). In addition, we have developed posture-dependent control architecture that translates the obtained MI into four robot motion commands: going forward, turning left, turning right, and stopping. The EEGs of eight healthy volunteer male subjects were recorded and employed to navigate a simulated robot to a goal in a virtual environment. On a predefined task, the developed BCI robot control system achieved its task in170 s with a collision number of 0.65, distance of 23.92 m, and successful command rate of 80%. Although the performance of the complete system varied from one subject to another, the robot always reached its final position successfully. The developed BCI robot control system yields promising results compared to manual controls.


Brain computer interface (BCI) Common spatial pattern (CSP) Electroencephalogram (EEG) Motor imagery Robot navigation 



The authors acknowledge the College of Engineering Research Center and Deanship of Scientific Research at King Saud University in Riyadh, Saudi Arabia, for the financial support to carry out the research work reported in this paper.


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Copyright information

© Taiwanese Society of Biomedical Engineering 2018

Authors and Affiliations

  1. 1.Department of Electrical Engineering, College of EngineeringKing Saud University, Saudi ArabiaRiyadhSaudi Arabia

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