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Tensor Based Simultaneous Feature Extraction and Sample Weighting for EEG Classification

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Book cover Neural Information Processing. Models and Applications (ICONIP 2010)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 6444))

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Abstract

In this paper we propose a Multi-linear Principal Component Analysis (MPCA) which is a new feature extraction and sample weighting method for classification of EEG signals using tensor decomposition. The method has been successfully applied to Motor-Imagery Brain Computer Interface (MI-BCI) paradigm. The performance of the proposed approach has been compared with standard Common Spatial Pattern (CSP) as well with a combination of PCA and CSP methods. We have achieved an average accuracy improvement of two classes classification in a range from 4 to 14 percents.

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References

  1. Cichocki, A., Zdunek, R., Phan, A.H., Amari, S.: Nonnegative Matrix Factorization and Tensor Factorizations. John Wiley and Sons, Chichester (2009)

    Book  MATH  Google Scholar 

  2. Lu, H., Plataniotis, K.N., Venetsanopoulos, A.N.: MPCA: Multilinear principal component analysis of tensor objects. IEEE Trans. Neural Network 19(1), 18–39 (2008)

    Article  Google Scholar 

  3. Lathauwer, L.D., Moor, B.D., Vandewalle, J.: A multilinear singular value decomposition. SIAM Journal of Matrix Analysis and Application 21(4), 1253–1278 (2001)

    Article  MathSciNet  MATH  Google Scholar 

  4. Phan, A.H., Cichocki, A.: Tensor Decompositions for Feature Extraction and Classification of High Dimensional Datasets. IEICE NOLTA E93-N(10) (October 2010)

    Google Scholar 

  5. Ramoser, H., Mueller-Gerking, J., Pfurtscheller, G.: Optimal spatial filtering of single trial EEG during imagined hand movement. IEEE Trans. Rehabilitation Engineering 8(4), 441–446 (2000)

    Article  Google Scholar 

  6. Dornhege, G., Blankertz, B., Curio, G., Müller, K.-R.: Boosting bit rates in noninvasive EEG single-trial classifications by feature combination and multiclass paradigms. IEEE Trans. Biomedical Engineering 51(6), 993–1002 (2004)

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. RIKEN Brain Science Institute, Japan

    Yoshikazu Washizawa, Hiroshi Higashi, Tomasz Rutkowski, Toshihisa Tanaka & Andrzej Cichocki

  2. Department of Electrical and Electronic Engineering, Tokyo University of Agriculture and Technology, Japan

    Hiroshi Higashi & Toshihisa Tanaka

Authors
  1. Yoshikazu Washizawa
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  2. Hiroshi Higashi
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  3. Tomasz Rutkowski
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  4. Toshihisa Tanaka
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  5. Andrzej Cichocki
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Editor information

Editors and Affiliations

  1. School of Information Technology, Murdoch University, 6150, Murdoch, WA, Australia

    Kok Wai Wong

  2. The Australian National University, 0200, Canberra, ACT, Australia

    B. Sumudu U. Mendis

  3. School of Electrical, Computer and Telecommunications Engineering, University of Wollongong, Northfields Avenue, 2522, P.O. Box, Wollongong, NSW, Australia

    Abdesselam Bouzerdoum

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© 2010 Springer-Verlag Berlin Heidelberg

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Washizawa, Y., Higashi, H., Rutkowski, T., Tanaka, T., Cichocki, A. (2010). Tensor Based Simultaneous Feature Extraction and Sample Weighting for EEG Classification. In: Wong, K.W., Mendis, B.S.U., Bouzerdoum, A. (eds) Neural Information Processing. Models and Applications. ICONIP 2010. Lecture Notes in Computer Science, vol 6444. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17534-3_4

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  • DOI: https://doi.org/10.1007/978-3-642-17534-3_4

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-17533-6

  • Online ISBN: 978-3-642-17534-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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