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  • CluSem: Accurate Clustering-based Ensemble Method to Predict Motor Imagery Tasks from Multi-channel EEG Data

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    Embargoed until: 2023-04-01
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    Accepted Manuscript (AM)
    Author(s)
    Miah, Md Ochiuddin
    Muhammod, Rafsanjani
    Mamun, Khondaker Abdullah Al
    Farid, Dewan Md
    Kumar, Shiu
    Sharma, Alok
    Dehzangi, Abdollah
    Griffith University Author(s)
    Sharma, Alok
    Year published
    2021
    Metadata
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    Abstract
    BACKGROUND: The classification of motor imagery electroencephalogram (MI-EEG) is a pivotal task in the biosignal classification process in the brain-computer interface (BCI) applications. Currently, this bio-engineering-based technology is being employed by researchers in various fields to develop cutting-edge applications. The classification of real-time MI-EEG signals is the most challenging task in these applications. The prediction performance of the existing classification methods is still limited due to the high dimensionality and dynamic behaviors of the real-time EEG data. PROPOSED METHOD: To enhance the classification ...
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    BACKGROUND: The classification of motor imagery electroencephalogram (MI-EEG) is a pivotal task in the biosignal classification process in the brain-computer interface (BCI) applications. Currently, this bio-engineering-based technology is being employed by researchers in various fields to develop cutting-edge applications. The classification of real-time MI-EEG signals is the most challenging task in these applications. The prediction performance of the existing classification methods is still limited due to the high dimensionality and dynamic behaviors of the real-time EEG data. PROPOSED METHOD: To enhance the classification performance of real-time BCI applications, this paper presents a new clustering-based ensemble technique called CluSem to mitigate this problem. We also develop a new brain game called CluGame using this method to evaluate the classification performance of real-time motor imagery movements. In this game, real-time EEG signal classification and prediction tabulation through animated balls are controlled via threads. By playing this game, users can control the movements of the balls via the brain signals of motor imagery movements without using any traditional input devices. RESULTS: Our results demonstrate that CluSem is able to improve the classification accuracy between 5% and 15% compared to the existing methods on our collected as well as the publicly available EEG datasets. The source codes used to implement CluSem and CluGame are publicly available athttps://github.com/MdOchiuddinMiah/MI-BCI_ML.
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    Journal Title
    Journal of Neuroscience Methods
    DOI
    https://doi.org/10.1016/j.jneumeth.2021.109373
    Copyright Statement
    © 2021 Elsevier. Licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International Licence (http://creativecommons.org/licenses/by-nc-nd/4.0/) which permits unrestricted, non-commercial use, distribution and reproduction in any medium, providing that the work is properly cited.
    Note
    This publication has been entered in Griffith Research Online as an advanced online version.
    Subject
    Neurosciences
    Information systems
    Brain Computer Interface (BCI)
    Brain Engineering
    Clustering
    Ensemble Learning
    Human Machine Interface (HMI)
    Publication URI
    http://hdl.handle.net/10072/408997
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