Published in International Journal of Advanced Research in Robotics and Development
ISSN: 2348-2338 Impact Factor:1.678 Volume:1 Issue:1 Year: 08 December,2013 Pages:12-16
This paper reports an Electroencephalogram based- brain actuated telepresence system to provide a user with the presence in remote environments through the mobile robot. We present the design of brain computer interface control of wheelchair and a mobile robot with autonomous navigation. The shared control strategy is built by the BCI decoding of task- related orders which can be autonomously executed by the robot. The quality of life of people suffering from severe motor disabilities can benefit from use of this technology capable of communication. Brain computer interfaces are the systems that can translate brain activity into signals that control the external devices. Thus they can represent the only technology for severally paralyzed patients to maintain their communication within the surrounding environment
Brain Computer Interfaces, Telerobitic
 J. R.Wolpaw, D. J. McFarland, G.W. Neat, and C. A. Forneris, “An EEGbased brain–computer interface for cursor control,” Electroencephalography Clin. Neurophysiol., vol. 78, no. 3, pp. 252–259, Mar. 1991.  N. Birbaumer, N. Ghanayim, T. Hinterberger, I. Iversen, B. Kotchoubey, A. Kübler, J. Perelmouter, E. Taub, and H. Flor, “A spelling device for the paralyzed,” Nature, vol. 398, no. 6725, pp. 297–298, Mar. 1999.  A. A. Karim, T. Hinterberger, J. Richter, J. Mellinger, N. Neumann, H. Flor, A. Kübler, and N. Birbaumer, “Neural Internet: Web surfing with brain potentials for the completely paralyzed,” Neurorehab. Neural Repair, vol. 20, no. 4, pp. 508–515, Dec. 2006.  J. R. Millán, F. Renkens, J. Mouriño, and W. Gerstner, “Noninvasive brain-actuated control of a mobile robot by human EEG,” IEEE Trans. Biomed. Eng., vol. 51, no. 6, pp. 1026–1033, Jun. 2004.  I. Iturrate, J. M. Antelis, A. Kübler, and J. Minguez, “A noninvasive brainactuated wheelchair based on a P300 neurophysiological protocol and automated navigation,” IEEE Trans. Robot., vol. 25, no. 3, pp. 614–627, Jun. 2009.  G. Vanacker, J. R. Millán, E. Lew, P. W. Ferrez, F. G. Moles, J. Philips, H. Van Brussel, andM. Nuttin, “Context-based filtering for assisted brainactuated wheelchair driving,” Comput. Intell. Neurosci., vol. 2007, p. 3, Jan. 2007.  T. Luth, D. Ojdanic, O. Friman, O. Prenzel, and A. Graser, “Low level control in a semi-autonomous rehabilitation robotic system via a brain– computer interface,” in Proc. IEEE 10th ICORR, 2007, pp. 721–728.  B. Rebsamen, E. Burdet, C. Guan, H. Zhang, C. L. Teo, Q. Zeng, C. Laugier, and M. H. Ang, Jr., “Controlling a wheelchair indoors using thought,” IEEE Intell. Syst., vol. 22, no. 2, pp. 18–24, Mar./ Apr. 2007.  A. Ferreira,W. C. Celeste, F. A. Cheein, T. F. Bastos- Filho, M. Sarcinelli- Filho, and R. Carelli, “Human–machine interfaces based on EMG and EEG applied to robotic systems,” J. NeuroEng. Rehab., vol. 5, no. 1, pp. 1– 15, Mar. 2008.