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Efficient Channel Selection Approach for Motor Imaginary Classification based on Convolutional Neural Network

Mzurikwao, Deogratias, Ang, Chee Siang, Samuel, Oluwarotimi Williams, Asogbon, Mojisola Grace, Li, Xiangxin, Li, Guanglin (2018) Efficient Channel Selection Approach for Motor Imaginary Classification based on Convolutional Neural Network. In: 2018 IEEE International Conference on Cyborg and Bionic Systems (CBS). 2018 IEEE International Conference on Cyborg and Bionic Systems (CBS). . pp. 418-421. IEEE E-ISBN 978-1-5386-7355-3. (doi:10.1109/CBS.2018.8612157​) (KAR id:73581)

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https://doi.org/10.1109/CBS.2018.8612157​

Abstract

Brain Computer Interface (BCI) may be the only way to communicate and control for disabled people. Someone's intention can be decoded from their brainwaves during motor imagery action. This can be used to help them control their environment without making any physical movement. To decode someone's intention from brainwaves during motor imagery activities, machine learning models trained on features extracted from the acquired EEG signals have been used. Although the technique has been successful, it has encountered several limitations and difficulties especially during feature extraction. Moreover, many current BCI systems rely on a large number of channels (e.g. 64) to capture spatial information which are necessary during training a machine learning model. In this study, Convolutional Neural Network (CNN) is used to decode five motor imagery intentions from EEG signals obtained from four subjects using 64 channels EEG device. A CNN model trained on raw EEG data managed to achieve a mean classification accuracy of 99.7%. Channel selection based on learned weights extracted from a trained CNN model has been performed with subsequent models trained on only two selected channels with higher weights attained a high accuracy (average of 98%) among three participants out of four.

Item Type: Conference or workshop item (Proceeding)
DOI/Identification number: 10.1109/CBS.2018.8612157​
Uncontrolled keywords: Feature extraction, brain modeling, electroencephalography, machine learning, task analysis, convolution, brain-computer interfaces
Subjects: T Technology > TA Engineering (General). Civil engineering (General)
Divisions: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Engineering and Digital Arts
Depositing User: Jim Ang
Date Deposited: 23 Apr 2019 09:56 UTC
Last Modified: 16 Feb 2021 14:03 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/73581 (The current URI for this page, for reference purposes)
Ang, Chee Siang: https://orcid.org/0000-0002-1109-9689
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