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EEG Signals of Motor Imagery Classification Using Adaptive Neuro-Fuzzy Inference System

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Advances in Nature and Biologically Inspired Computing

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 419))

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Abstract

Brain Computer Interface (BCI) techniques are used to help disabled people to translate brain signals to control commands imitating specific human thinking based on Electroencephalography (EEG) signal processing. This paper tries to accurately classify motor imagery imagination tasks: e.g. left and right hand movement using three different methods which are: (1) Adaptive Neuro Fuzzy Inference System (ANFIS), (2) Linear Discriminant Analysis (LDA) and (3) k-nearest neighbor (KNN) classifiers. With ANFIS, different clustering methods are utilized which are Subtractive, Fuzzy C-Mean (FCM) and K-means. These clustering methods are examined and compared in terms of their accuracy. Three features are studied in this paper including AR coefficients, Band Power Frequency, and Common Spatial pattern (CSP). The classification accuracies with two optimal channels C3 and C4 are investigated.

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Author information

Authors and Affiliations

  1. Faculty of Science, Alazhar University, Cairo, Egypt

    Shereen A. El-aal & Neveen I. Ghali

  2. Computer Engineering Department, Cairo University, Cairo, Egypt

    Rabie A. Ramadan

  3. Hail University, Hail, Kingdom of Saudi Arabia

    Rabie A. Ramadan

Authors
  1. Shereen A. El-aal
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  2. Rabie A. Ramadan
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  3. Neveen I. Ghali
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Corresponding author

Correspondence to Shereen A. El-aal .

Editor information

Editors and Affiliations

  1. School of Mathematics, Statistics and CS, University of KwaZulu-Natal, Pietermaritzburg, South Africa

    Nelishia Pillay

  2. Sch of IT, Dept of CS, University of Pretoria, Pretoria, South Africa

    Andries P. Engelbrecht

  3. Sci N/w for Innova and Research Exc, Machine Intelligence Research Labs, Auburn, Washington, USA

    Ajith Abraham

  4. Department of Computing Sciences,, Nelson Mandela Metropolitan University, Port Elizabeth, South Africa

    Mathys C. du Plessis

  5. Faculty of Electrical Engineering and, Technical University of Ostrava, Ostrava-Poruba, Czech Republic

    Václav Snášel

  6. Fakulti Teknologi Maklumat dan Komunikas, Universiti Teknikal Malaysia Melaka, Durian Tunggal, Malaysia

    Azah Kamilah Muda

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El-aal, S.A., Ramadan, R.A., Ghali, N.I. (2016). EEG Signals of Motor Imagery Classification Using Adaptive Neuro-Fuzzy Inference System. In: Pillay, N., Engelbrecht, A., Abraham, A., du Plessis, M., Snášel, V., Muda, A. (eds) Advances in Nature and Biologically Inspired Computing. Advances in Intelligent Systems and Computing, vol 419. Springer, Cham. https://doi.org/10.1007/978-3-319-27400-3_10

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  • DOI: https://doi.org/10.1007/978-3-319-27400-3_10

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-27399-0

  • Online ISBN: 978-3-319-27400-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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