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Deep Learning for EEG Motor Imagery-Based Cognitive Healthcare

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Connected Health in Smart Cities

Abstract

Electroencephalography (EEG) motor imagery signals have recently gained significant attention due to its ability to encode a person’s intent to perform an action. Researchers have used motor imagery signals to help disabled persons control devices, such as wheelchairs and even autonomous vehicles. Hence, the accurate decoding of these signals is important to brain–computer interface (BCI) systems. Such motor imagery-based BCI systems can become an integral part of cognitive modules that are increasingly being used in smart city frameworks. However, the classification and recognition of EEG have consistently been a challenge due to its dynamic time series data and low signal-to-noise ratio. Deep learning methods, such as the convolution neural network (CNN), have achieved remarkable success in computer vision tasks. Considering the limited applications of deep learning for motor imagery EEG classification, this work focuses on developing CNN-based deep learning methods for such purpose. We propose a multiple-CNN feature fusion architecture to extract and fuse features by using subject-specific frequency bands. CNN has been designed with variable filter sizes and split convolutions for the extraction of spatial and temporal information from raw EEG data. A feature fusion technique based on autoencoders is applied. Cross-encoding technique has been proposed and is successfully used to train autoencoders for a novel cross-subject information transfer and augmenting EEG data. This proposed method outperforms the state-of-the-art four-class motor imagery classification methods for subject-specific and cross-subject data. Autoencoder cross-encoding helps to learn subject invariant and generic features for EEG data and achieves more than 10% increase on cross-subject classification results. The fusion approaches show the potential of applying multiple CNN feature fusion techniques for the advancement of EEG-related research.

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Acknowledgments

The authors extend their appreciation to the International Scientific Partnership Program ISPP at King Saud University for funding this research work through ISPP-121.

Author information

Authors and Affiliations

  1. Department of Computer Engineering, College of Computer and Information Sciences (CCIS), King Saud University, Riyadh, Saudi Arabia

    Syed Umar Amin, Mansour Alsulaiman & Ghulam Muhammad

  2. Department of Software Engineering, College of Computer and Information Sciences (CCIS), King Saud University, Riyadh, Saudi Arabia

    M. Shamim Hossain

  3. Department of Electrical and Computer Engineering, University of Idaho, Moscow, ID, USA

    Mohsen Guizani

Authors
  1. Syed Umar Amin
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  2. Mansour Alsulaiman
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  3. Ghulam Muhammad
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  4. M. Shamim Hossain
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  5. Mohsen Guizani
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Corresponding author

Correspondence to M. Shamim Hossain .

Editor information

Editors and Affiliations

  1. School of Electrical Engineering and Computer Science, University of Ottawa, Ottawa, ON, Canada

    Abdulmotaleb El Saddik

  2. Computer and Information Sciences, King Saud University, Riyadh, Saudi Arabia

    M. Shamim Hossain

  3. School of Electrical Engineering and Computer Science, University of Ottawa, Ottawa, ON, Canada

    Burak Kantarci

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Amin, S.U., Alsulaiman, M., Muhammad, G., Hossain, M.S., Guizani, M. (2020). Deep Learning for EEG Motor Imagery-Based Cognitive Healthcare. In: El Saddik, A., Hossain, M., Kantarci, B. (eds) Connected Health in Smart Cities. Springer, Cham. https://doi.org/10.1007/978-3-030-27844-1_12

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  • DOI: https://doi.org/10.1007/978-3-030-27844-1_12

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  • Print ISBN: 978-3-030-27843-4

  • Online ISBN: 978-3-030-27844-1

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