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Deep Learning of Multifractal Attributes from Motor Imagery Induced EEG

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Neural Information Processing (ICONIP 2014)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 8834))

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Abstract

Electroencephalogram (EEG) is an effective metric to monitor or measure human brain activities. Another advantage for EEG utilization is noninvasive, and is not harmful to subjects. However, this leads to two drawbacks: low signal-to-noise ratio and non-stationarity. In order to extract useful features contained in the EEG, multifractal attributes were explored in this paper. A few attributes were utilized to analyze the EEG recorded during motor imagery tasks. Then, we built a deep learning model based on denoising autoencoder to recognize different motor imagery tasks. From the results, we can find that 1) Motor imagery induced EEG is of multifractal attributes, 2) multifractal spectrum D(h) and the statistics cp based on cumulants can reflect difference between different motor imagery tasks, so they can be adopted as features for classification. 3) A deep network with initialization by denoising autoencoder is suitable to learn multifractal attributes extracted from EEG. The classification accuracies demonstrated that the proposed method is feasible.

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Author information

Authors and Affiliations

  1. Laboratory for Advanced Brain Signal Processing, Brain Science Institute, RIKEN, Saitama, 351-0198, Japan

    Junhua Li & Andrzej Cichocki

Authors
  1. Junhua Li
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  2. Andrzej Cichocki
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Editor information

Editors and Affiliations

  1. Department of Artificial Intelligence, Faculty of Computer Science and Information Technology Building, University of Malaya, 50603, Kuala Lumpur, Malaysia

    Chu Kiong Loo

  2. Department of Electronics and Communication Engineering,College of Engineering, Jalan IKRAM-UNITEN, Universiti Tenaga Nasional, 43009, Kajang, Selangor, Malaysia

    Keem Siah Yap

  3. School of Engineering and Information Technology, Murdoch University, South St, 6150, Murdoch, Western Australia, Australia

    Kok Wai Wong

  4. Department of Electrical and Electronics Engineering, Yonsei University, 50 Yonsei-ro, Seodaemun-gu, 120-749, Seoul, South Korea

    Andrew Teoh

  5. Department of Electrical and Electronic Engineering, Xi’an Jiaotong-Liverpool University, Ren’ai Road 111, SIP 215123, Suzhou, Jiangsu Province, China

    Kaizhu Huang

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© 2014 Springer International Publishing Switzerland

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Li, J., Cichocki, A. (2014). Deep Learning of Multifractal Attributes from Motor Imagery Induced EEG. In: Loo, C.K., Yap, K.S., Wong, K.W., Teoh, A., Huang, K. (eds) Neural Information Processing. ICONIP 2014. Lecture Notes in Computer Science, vol 8834. Springer, Cham. https://doi.org/10.1007/978-3-319-12637-1_63

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  • DOI: https://doi.org/10.1007/978-3-319-12637-1_63

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-12636-4

  • Online ISBN: 978-3-319-12637-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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