CluSem: Accurate Clustering-based Ensemble Method to Predict Motor Imagery Tasks from Multi-channel EEG Data
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Author(s)
Miah, Md Ochiuddin
Muhammod, Rafsanjani
Mamun, Khondaker Abdullah Al
Farid, Dewan Md
Kumar, Shiu
Sharma, Alok
Dehzangi, Abdollah
Griffith University Author(s)
Year published
2021
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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 ...
View more >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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View more >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
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)