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International Conference on Brain Informatics and Health

BIH 2015: Brain Informatics and Health pp 222–231Cite as

Time-Varying Parametric Modeling of ECoG for Syllable Decoding

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9250))

Abstract

As a step toward developing neuroprostheses, the purpose of this study is to explore syllable decoding in a subject with implanted electrocorticographic (ECoG) recordings. For this study, we use ECoG signals recorded while a subject volunteered to perform a task in which the patient has been visually cued to speak isolated consonant-vowel syllables varying in their articulatory features. We propose a recursive estimation method to calculate the parametric model coefficients in each time instant and band power features from individual ECoG sites are extracted to decode the articulated syllables. Our findings may contribute to the development of brain machine interface (BMI) systems for syllable-level speech rehabilitation in handicapped individuals.

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Author information

Authors and Affiliations

  1. Department of Electrical and Computer Engineering, University of Patras, Patras, Greece

    Vasileios G. Kanas, Iosif Mporas & Kyriakos N. Sgarbas

  2. Computer Informatics Engineering Department, TEI of Western Greece, Patras, Greece

    Iosif Mporas

  3. Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, 21205, USA

    Griffin W. Milsap

  4. Department of Neurology, Johns Hopkins University, Baltimore, MD, 21205, USA

    Nathan E. Crone

  5. Department of Medical Physics, School of Medicine, University of Patras, Patras, Greece

    Anastasios Bezerianos

  6. Singapore Institute for Neurotechnology, National University of Singapore, Singapore, Singapore

    Anastasios Bezerianos

Authors
  1. Vasileios G. Kanas
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  2. Iosif Mporas
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  3. Griffin W. Milsap
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  4. Kyriakos N. Sgarbas
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  5. Nathan E. Crone
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  6. Anastasios Bezerianos
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Corresponding author

Correspondence to Vasileios G. Kanas .

Editor information

Editors and Affiliations

  1. Imperial College London , Data Science Institute, London, United Kingdom

    Yike Guo

  2. Institute of Cognitive Neuroscience, University College London, London, United Kingdom

    Karl Friston

  3. Imperial College London, London, United Kingdom

    Faisal Aldo

  4. Campus Biotech, École Polytechnique Fédérale de Lausanne, Geneva, Switzerland

    Sean Hill

  5. Allen Institute for Brain Science, Seattle, USA

    Hanchuan Peng

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© 2015 Springer International Publishing Switzerland

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Kanas, V.G., Mporas, I., Milsap, G.W., Sgarbas, K.N., Crone, N.E., Bezerianos, A. (2015). Time-Varying Parametric Modeling of ECoG for Syllable Decoding. In: Guo, Y., Friston, K., Aldo, F., Hill, S., Peng, H. (eds) Brain Informatics and Health. BIH 2015. Lecture Notes in Computer Science(), vol 9250. Springer, Cham. https://doi.org/10.1007/978-3-319-23344-4_22

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  • DOI: https://doi.org/10.1007/978-3-319-23344-4_22

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-23343-7

  • Online ISBN: 978-3-319-23344-4

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