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Classification of EEG-Based Brain–Computer Interfaces

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Advanced Intelligent Computational Technologies and Decision Support Systems

Part of the book series: Studies in Computational Intelligence ((SCI,volume 486))

Abstract

This chapter demonstrates the development of a brain computer interface (BCI) decision support system for controlling the movement of a wheelchair for neurologically disabled patients using their Electroencephalography (EEG). The subject was able to imagine his/her hand movements during EEG experiment which made EEG oscillations in the signal that could be classified by BCI. The BCI will translate the patient’s thoughts into simple wheelchair commands such as “go” and “stop”. EEG signals are recorded using 59 scalp electrodes. The acquired signals are artifacts contaminated. These artifacts were removed using blind source separation (BSS) by independent component analysis (ICA) to get artifact-free EEG signal from which certain features are extracted by applying discrete wavelet transformation (DWT). The extracted features were reduced in dimensionality using principal component analysis (PCA). The reduced features were fed to neural networks classifier yielding classification accuracy greater than 95 %.

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Acknowledgments

The research of Valentina Emilia Balas was supported by the Bilateral Cooperation Research Project between Bulgaria-Romania (2010-2012) entitled “Electronic Health Records for the Next Generation Medical Decision Support in Romanian and Bulgarian National Healthcare Systems”, NextGenElectroMedSupport.

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Authors and Affiliations

  1. Faculty of Engineering, Misr University for Science and Technology (MUST), 6th of October City, Cairo, Egypt

    Ahmad Taher Azar

  2. Aurel Vlaicu University of Arad, Arad, Romania

    Valentina E. Balas

  3. Vasile Goldis Western University of Arad, Arad, Romania

    Teodora Olariu

  4. Clinical Emergency County Hospital Arad, Arad, Romania

    Teodora Olariu

Authors
  1. Ahmad Taher Azar
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  2. Valentina E. Balas
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  3. Teodora Olariu
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Correspondence to Ahmad Taher Azar .

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Editors and Affiliations

  1. Petru Maior University, Targu Mures, Romania

    Barna Iantovics

  2. Technical University of Sofia, Sofia, Bulgaria

    Roumen Kountchev

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Azar, A.T., Balas, V.E., Olariu, T. (2014). Classification of EEG-Based Brain–Computer Interfaces. In: Iantovics, B., Kountchev, R. (eds) Advanced Intelligent Computational Technologies and Decision Support Systems. Studies in Computational Intelligence, vol 486. Springer, Cham. https://doi.org/10.1007/978-3-319-00467-9_9

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  • DOI: https://doi.org/10.1007/978-3-319-00467-9_9

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-00466-2

  • Online ISBN: 978-3-319-00467-9

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