Towards Reduced EEG Based Brain-Computer Interfacing for Mobile Robot Navigation

  • Mufti Mahmud
  • Amir Hussain
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8266)


Rapid development in highly parallel neurophysiological recording techniques along with sophisticated signal processing tools allow direct communication with neuronal processes at different levels. One important level from the point of view of Rehabilitation Engineering & Assistive Technology is to use the Electroencephalogram (EEG) signals to interface with assistive devices. This type of brain-computer interface (BCI) aims to reestablish the broken loop of the persons with motor dysfunction(s). However, with the growing availability of of instruments and processes for implementation, the BCI is also getting more complex. In this work, the authors present a model for reduced complexity BCI based on EEG signals through a few simple processes for automated navigation and control of robotic device. It is demonstrated that not only with few number of electrodes, but also using simple features like evoked responses caused by Saccadic eye movement can be used in building robust BCI for rehabilitation which may revolutionize the development of assitive devices for the disabled.


Brain-computer interface Electroencephalogram mobile robot robot navigation neuronal signal analysis 


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  1. 1.
    Mason, S., Birch, G.: A general framework for brain-computer interface design. IEEE Trans. Neural Syst. Rehabil. Eng. 11(1), 70–85 (2003)CrossRefGoogle Scholar
  2. 2.
    Millán, J., Renkens, F., Mouriño, J., Gerstner, W.: Non-invasive brain-actuated control of a mobile robot. In: Proceedings of the 18th International Joint Conference on Artificial Intelligence, Acapulco, Mexico, pp. 1121–1126 (2003)Google Scholar
  3. 3.
    Vaughan, T.M., Heetderks, W.J., Trejo, L.J., Rymer, W.Z., Weinrich, M., Moore, M.M., Kübler, A., Dobkin, B.H., Birbaumer, N., Donchin, E., Wolpaw, E.W., Wolpaw, J.R.: Rain-computer interface technology: a review of the second international meeting. IEEE Trans. Neural Syst. Rehabil. Eng. 2(11), 94–109 (2003)CrossRefGoogle Scholar
  4. 4.
    Fatourechi, M.: Design of a self-paced brain computer interface system using features extracted from three neurological phenomena. Ph.D. Dissertation, The University of British Colombia, Canada (2008)Google Scholar
  5. 5.
    Ferreira, A., Celeste, W., Cheein, F., Bastos-Filho, T., Sarcinelli-Filho, M., Carelli, R.: Human-machine interfaces based on EMG and EEG applied to robotic systems. Journal of NeuroEngineering and Rehabilitation 5(1), 10 (2008)CrossRefGoogle Scholar
  6. 6.
    Moon, I., Lee, M., Chu, J.: Wearable emg-based hci for electric-powered wheelchair users with motor disabilities. In: Proceedings of 2005 IEEE International Conference on Robotics and Automation (ICRA 2005), Barcelona, Spain, pp. 2649–2654 (2005)Google Scholar
  7. 7.
    Mourino, J.: EEG-based analysis for the design of adaptive brain interfaces, Ph.D. Dissertation, Universitat Politecnica de Catalunya, Barcelona, Spain (2003)Google Scholar
  8. 8.
    Bi, L., Fan, X.A., Liu, Y.: EEG-based brain-controlled mobile robots: a survey. IEEE Trans. Human-Machine Systems 43(2), 161–176 (2013)CrossRefGoogle Scholar
  9. 9.
    Mahmud, M., Hawellek, D., Valjamae, A.: A brain-machine interface based on EEG: extracted alpha waved applied to mobile robot. In: Proceedings of the 2009 ECSIS Symposium on Advanced Technologies for Enhanced Quality of Life (AT-EQUAL 2009), Iasi, Romania, pp. 28–31 (2009)Google Scholar
  10. 10.
    Mahmud, M., Hawellek, D., Bertoldo, A.: EEG based brain-machine interface for navigation of robotic device. In: Proceedings of the 3rd IEEE/RAS-EMBS International Conference on Biomedical Robotics and Biomechatronics (BioRob 2010), Tokyo, Japan, pp. 168–172 (2010)Google Scholar
  11. 11.
    Mahmud, M., Bertoldo, A., Vassanelli, S.: EEG Based Brain Machine Interfacing: Navigation of Mobile Robotic Device. In: Bedkwoski, J. (ed.) Mobile Robots - Control Architectures, Bio-interfacing, Navigation, Multi Robot Motion Planning and Operator Training, pp. 129–144. Intech, Rijeka (2011) ISBN: 978-953-307-842-7Google Scholar
  12. 12.
    Ohno, K., Funase, A., Cichocki, A., Takumi, I.: Analysis of eeg signals in memory guided saccade tasks. In: IFMBE Proceedings of World Congress on Medical Physics and Biomedical Engineering, COEX Seoul, Korea, pp. 2664–2667 (2006)Google Scholar
  13. 13.
    Bernardet, U., Blanchard, M., Verschure, P.: IQR: a distributed system for real-time real-world neuronal simulation. Neurocomputing 44(46), 1043–1048 (2002)CrossRefGoogle Scholar
  14. 14.
    Bernardet, U., Verschure, P.: IQR: A tool for the construction of multi-level simulations of brain and behaviour. Neurocomputing 8(2), 113–134 (2010)Google Scholar
  15. 15.
    Haas, S., Frei, M., Osorio, I., Pasik-Duncan, B., Radel, J.: EEG ocular artifact removal through armax model system identification using extended least squares. Communications in Information and Systems 3, 19–40 (2003)MathSciNetzbMATHGoogle Scholar
  16. 16.
    Rohalova, M., Sykacek, P., Koska, M., Dorffner, G.: Detection of the eeg artifacts by the means of the (extended) kalman filter. Measurement Science Review 1, 59–62 (2001)Google Scholar
  17. 17.
    Mahmud, M., Bertoldo, A., Girardi, S., Maschietto, M., Vassanelli, S.: SigMate: a Matlab–based neuronal signal processing tool. In: 32nd Intl. Conf. of IEEE EMBS, pp. 1352–1355. IEEE Press, New York (2010)Google Scholar
  18. 18.
    Mahmud, M., et al.: SigMate: A Comprehensive Software Package for Extracellular Neuronal Signal Processing and Analysis. In: 5th Intl. Conf. on Neural Eng., pp. 88–91. IEEE Press, New York (2011)Google Scholar
  19. 19.
    Mahmud, M., Bertoldo, A., Girardi, S., Maschietto, M., Vassanelli, S.: SigMate: A MATLAB–based automated tool for extracellular neuronal signal processing and analysis. J. Nerusci. Meth. 207, 97–112 (2012)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Mufti Mahmud
    • 1
    • 2
    • 3
  • Amir Hussain
    • 3
  1. 1.Theoretical Neurobiology & Neuroengineering Lab, Department of Biomedical SciencesUniversity of AntwerpWilrijkBelgium
  2. 2.Institute of Information TechnologyJahangirnagar UniversitySavarBangladesh
  3. 3.Cognitive Signal Image and Control Processing Research (COSIPRA) Laboratory, School of Natural SciencesUniversity of StirlingStirlingUK

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