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Detection of Fast and Slow Hand Movements from Motor Imagery EEG Signals

  • Saugat Bhattacharyya
  • Munshi Asif Hossain
  • Amit Konar
  • D. N. Tibarewala
  • Janarthanan Ramadoss
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 27)

Abstract

Classification of Electroencephalography (EEG) signal is an open area of re-search in Brain-computer interfacing (BCI). The classifiers detect the different mental states generated by a subject to control an external prosthesis. In this study, we aim to differentiate fast and slow execution of left or right hand move-ment using EEG signals. To detect the different mental states pertaining to motor movements, we aim to identify the event related desynchronization/ synchronization (ERD/ERS) waveform from the incoming EEG signals. For this purpose, we have used Welch based power spectral density estimates to create the feature vector and tested it on multiple support vector machines, Nave Bayesian, Linear Discriminant Analysis and k-Nearest Neighbor classifiers. The classification accuracies produced by each of the classifiers are more than 75% with naïve Bayesian yielding the best result of 97.1%.

Keywords

Motor imagery Brain-computer interfacing Event related desynchronization/ synchronization Electroencephalography Pattern classifiers 

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References

  1. 1.
    Daly, J.J., Wolpaw, J.R.: Brain-computer interfaces in neurological rehabilitation. Lancet. Neurol. 7, 1032–1043 (2008)CrossRefGoogle Scholar
  2. 2.
    Dornhege, G.: Towards Brain-Computer Interfacing. MIT Press (2007)Google Scholar
  3. 3.
    McFarland, D.J., Wolpaw, J.R.: Brain-computer interface operation of robotic and prosthetic devices. Computer 41(10), 52–56 (2008)CrossRefGoogle Scholar
  4. 4.
    Bermudez i Badia, S., Garcia Morgade, A., Samaha, H., Verschure, P.F.M.J.: Using a Hy-brid Brain Computer Interface and Virtual Reality System to Monitor and Promote Cortical Reorganization through Motor Activity and Motor Imagery Training. IEEE Trans. Neural Sys. Rehab. Eng. 21(2), 174–181 (2013)CrossRefGoogle Scholar
  5. 5.
    Bordoloi, S., Sharmah, U., Hazarika, S.M.: Motor imagery based BCI for a maze game. In: 4th Int. Conf. Intelligent Human Computer Interaction (IHCI), Kharagpur, India, pp. 1–6 (2012)Google Scholar
  6. 6.
    Millan, J.R., Rupp, R., Muller-Putz, G.R., Murray-Smith, R., Giugliemma, C., Tangermann, M., Vidaurre, C., Cincotti, F., Kubler, A., Leeb, R., Neuper, C., Muller, K.R., Mattia, D.: Combining brain-computer interfaces and assistive technogies: State-of-the-art and challenges. Front. Neurosci. 4, 1–15 (2010)Google Scholar
  7. 7.
    Bhattacharyya, S., Sengupta, A., Chakraborti, T., Konar, A., Tibarewala, D.N.: Automatic feature selection of motor imagery EEG signals using differential evolution and learning automata. Med. & Bio. Eng. & Comp. 52(2), 131–139 (2014)CrossRefGoogle Scholar
  8. 8.
    Zhou, W., Zhong, L., Zhao, H.: Feature Attraction and Classification of Mental EEG Using Approximate Entropy. In: 27th Ann. Int. Conf. Eng. Med. & Bio. Soc., pp. 5975–5978 (2005)Google Scholar
  9. 9.
    Theodoridis, S., Koutroumbas, K.: Pattern Recognition, 4th edn., pp. 13–22. Academic Press (2009)Google Scholar
  10. 10.
    Qiang, C., Hu, P., Huanqing, F.: Experiment study of the relation between motion complexity and event-related desynchronization/synchronization. In: 1st Int. Conf. Neural Interface & Cont. 2005, pp. 14–16 (2005)Google Scholar
  11. 11.
    Chai, R., Ling, S.H., Hunter, G.P., Nguyen, H.T.: Mental non-motor imagery tasks classifi-cations of brain computer interface for wheelchair commands using genetic algorithm-based neural network. In: The 2012 Int. Joint Conf. Neural Networks, pp. 1–7 (2012)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Saugat Bhattacharyya
    • 1
  • Munshi Asif Hossain
    • 1
  • Amit Konar
    • 1
  • D. N. Tibarewala
    • 2
  • Janarthanan Ramadoss
    • 3
  1. 1.Dept. of Electronics and Telecommunication EngineeringJadavpur UniversityKolkataIndia
  2. 2.School of Bioscience and Engineering, Department of Computer ScienceJadavpur UniversityKolkataIndia
  3. 3.TJS Engineering CollegeChennaiIndia

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