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Reducing Execution Time for Real-Time Motor Imagery Based BCI Systems

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Proceedings of the International Conference on Advanced Intelligent Systems and Informatics 2016 (AISI 2016)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 533))

Abstract

Brain Computer Interface (BCI) systems based on electroencephalography (EEG) has introduced a new communication method for people with severe motor disabilities. One of the main challenges of Motor Imagery (MI) is to develop a real-time BCI system. Using complex classification techniques to enhance the accuracy of the system may cause a remarkable delay of real-time systems. This paper aims to achieve high accuracy with low computational cost. Two public datasets (BCIC III IVa and BCIC IV IIa) were used in this study; to check the robustness of the proposed approach. Dimension reduction of input signal has been done by channel selection and extracting features using Root Mean Square (RMS). The extracted features have been examined with four different classifiers. Experimental results showed that using Least Squares classifier gives best results, compared to other classifiers, with minimum computational time.

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Authors and Affiliations

  1. Computer Science Department, Modern Academy for CS and MT, Maadi, Cairo, Egypt

    Sahar Selim

  2. Faculty of Computer and Information Science, SC Department, Ain Shams University, Cairo, Egypt

    Manal Tantawi & Howida Shedeed

  3. Faculty of Computers and Information, CS Department, Cairo University, Cairo, Egypt

    Amr Badr

Authors
  1. Sahar Selim
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  2. Manal Tantawi
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  3. Howida Shedeed
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  4. Amr Badr
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Corresponding author

Correspondence to Sahar Selim .

Editor information

Editors and Affiliations

  1. Faculty of Computers & Information, Cairo University, Giza, Egypt

    Aboul Ella Hassanien

  2. Dubai International Academic City, The British University, Dubai, United Arab Emirates

    Khaled Shaalan

  3. CS Dept. Faculty of Computers and Inform, Suez Canal University CS Dept. Faculty of Computers and Inform, Ismailia, Egypt

    Tarek Gaber

  4. Ahmed Orabi Square , Menouf, Egypt

    Ahmad Taher Azar

  5. Faculty of Computer & Information Scienc, Ain Shams University Faculty of Computer & Information Scienc, Cairo, Egypt

    M. F. Tolba

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Selim, S., Tantawi, M., Shedeed, H., Badr, A. (2017). Reducing Execution Time for Real-Time Motor Imagery Based BCI Systems. In: Hassanien, A., Shaalan, K., Gaber, T., Azar, A., Tolba, M. (eds) Proceedings of the International Conference on Advanced Intelligent Systems and Informatics 2016. AISI 2016. Advances in Intelligent Systems and Computing, vol 533. Springer, Cham. https://doi.org/10.1007/978-3-319-48308-5_53

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  • DOI: https://doi.org/10.1007/978-3-319-48308-5_53

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-48307-8

  • Online ISBN: 978-3-319-48308-5

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