EEG Based BCI Using Visual Imagery Task for Robot Control

Authors

  • Husnaini Azmy Faculty of Electrical Engineering, Universiti Teknologi Malaysia, 81310 UTM Johor Bahru, Johor, Malaysia
  • Norlaili Mat Safri Faculty of Electrical Engineering, Universiti Teknologi Malaysia, 81310 UTM Johor Bahru, Johor, Malaysia

DOI:

https://doi.org/10.11113/jt.v61.1628

Keywords:

Electroencephalography (EEG), Brain–computer interface (BCI), visual imagery, right frontal cortex

Abstract

The aim of this study is to detect the brain activation on scalp by Electroencephalogram (EEG) task–based for brain computer interface (BCI) using wirelessly control robot. EEG was measured in 8 normal subjects for control and task conditions. The objective is to determine one scalp location which will give signals that can be used to control the wireless robot using BCI and EEG, using non invasive and without subject training. In control condition subjects were ask to relax but in task condition, subjects were asked to imagine a star rotating clockwise at position 45 degrees direction pointed by the wireless robot where at this angle the target is located. At position 0 and 90 degree angle subjects were asked to relax since there is no target on that direction. Using EEG spectral power analysis and normalization, the optimum location for this task has been detected at position F8 which is in frontal cortex area and the rhythm happened at alpha frequency band. At this position, the signals from the brain should be able to drive the robot to the required direction by giving correct and accurate signals to robot moving towards target.

References

P. R. Kennedy, R. A. E Bakay, M. M. Moore, K. Adams, J. Goldwaithe, 2000. Direct Control of a Computer from the Human Central Nervous System. IEEE Transactions on Rehabilitation Engineering. 8: 198.

D. J. McFarland and J. R. Wolpaw. 2008. Brain-Computer Interface Operation of Robotic and Prosthetic Devices. Journals & Magazines, NewYork State Department of Health, IEEE Computer Society. 41: 52.

N. Neumann and A. Kuber. 2003. Training Locked-in Patients: A Challenge for the Use of Brain–Computer Interfaces. IEEE Transactions on Neural Systems and Rehabilitation Engineering. 11: 169.

A. O. G. Barbosa, D. R. Achanccaray, and M. A. Meggiolaro. 2010. Activation of a Mobile Robot through a Brain Computer Interface. IEEE International Conference on Robotics and Automation. 4815–4821.

S. Z. Salleh, N. M. Safri and S. H. A. Ali. 2009. Moving One Dimensional Cursor Using Extracted Parameter from Brain Signals. Signal Processing: An International Journal (SPIJ). 3: 110.

B. Obermaier, G. Müller, G. Pfurtscheller. 2001. Virtual Keyboard Controlled by Spontaneous EEG Activity. Proc. of the Int. Conference on Artificial Neural Networks. 11: 422.

I. Iturrate, J. Antelis, J. Minguez. 2009. Synchronous EEG Brain-Actuated Wheelchair with Automated Navigation. Robotics and Auto-mation. IEEE International Conference ICRA. 2318–2325.

E. A. Curran and M. J. Stokes. 2003. Learning to Control Brain Activity: A Review of the Production and Control of EEG Components for Driving Brain-Computer Interface (BCI) systems. Brain and Cognition. 51: 326.

J. R. Wolpaw, N. Birbaumer, W. J. Heetderks, D. J. McFarland, P. H. Peckham, G. S., E. Donchin, L. A. Quatrano, C. J. Robinson, and T. M. Vaughan. 2000. Brain-Computer Interface Technology: A Review of the First International Meeting. IEEE Trans. Rehab. Eng. 8: 164.

L. W. Wu, H. C. Liao, J. S. Hu and P. C. Lo. 2008. Brain-controlled robot agent: an EEG-based e-Robot agent. Industrial Robot: An International Journal. 35: 507.

G. E. Fabiani, D. J. McFarland, J. R. Wolpaw and G. Pfurtscheller. 2004. Conversion of EEG Activity into Cursor Movement by a Brain–Computer Interface (BCI). IEEE Transactions On Neural Systems And Rehabilitation Engineering. 12: 331.

C. R. Hema, M. P. Paulraj, S. Yaacob, A. H. Adom, R. Nagarajan. 2007. Motor Imagery Signal Classification for a Four State Brain Machine Interface. International Journal of Biological and Life Sciences. 3: 1.

M. Kawada and R. M. Leahyt. 2006. Electrical Brain Mapping of Motor Imagination Using the Minimum Norm Solution. International Symposium on Communications and Information Technologies (ISCIT). 595–598.

J. C. Lee and D. S. Tan. 2006. Using a Low-Cost Electroencephalograph for Task Classification in HCI Research. UIST’06. 81–90.

A. Ferreira, W. C. Celeste, F. A. Cheein, T. F. Bastos-Filho, M. Sarcinelli-Filho and R. Carelli. 2008. Human-Machine Interface Based on EMG and EEG Applied to Robotic Systems. Journal of Neuro Engineering and Rehabilitation. 5: 10.

R. S. Manzoor, R. Gani, V. Jeoti, N. Kamel and M. Asif. 2009. Dwpt based FFT and its application to SNR estimation in FDM Systems. Signal Processing: An International Journal. 3: 2.

G. N. Martin. 1998. Human Neuropsychology. Europe: Prentice Hall.

J. Cre´mers, A. Dessoullie`res, and G. Garraux. 2012. Hemispheric Specialization during Mental Imagery of Brisk Walking. Human Brain Mapping. 33: 873.

D. H. Romero, M. G. Lacourse, K. E. Lawrence, S. Schandler, M. J. Cohen. 2000. Event-related Potentials as a Function Of Movement Parameter Variations During Motor Imagery and Isometric Action. Behavioural Brain Research. 117: 83.

F. Crivello, N. Tzourio, E. Mellet, O. Ghaëm, B. M. Mazoyer. 1996. Functional Anatomy of Visuo-Spatial Mental Imagery: Correlation Maps Between Baseline NrCBF and Psychometric Data. NeuroImage. 3: S206.

S. D. Slotnick, W. L. Thompson and S. M. Kosslyn. 2012. Visual memory and visual mental imagery recruit common control and sensory regions of the brain. Cognitive Neuroscience. 3: 14.

A. W. de Borst, A. T. Sack, B. M. Jansma, F. Esposito, F. Martino, G. Valente, A. Roebroeck, F. di Salle, R. Goebel, E. Formisano. 2012. Integration of “what†and “where†in frontal cortex during visual imagery of scenes. NeuroImage. 60: 47.

Downloads

Published

2013-02-15

Issue

Section

Science and Engineering

How to Cite

EEG Based BCI Using Visual Imagery Task for Robot Control. (2013). Jurnal Teknologi, 61(2). https://doi.org/10.11113/jt.v61.1628