Discrimination of four class simple limb motor imagery movements for brain�computer interface
The discrimination of four simple limb motor imagery movements for brain-computer interface (BCI) applications is still challenging. This is because most of the movement imaginations have close spatial representations on the motor cortex area. Nevertheless, due to its potential applications in signi...
| Main Authors: | Abdalsalam M, E., Yusoff, M.Z., Mahmoud, D., Malik, A.S., Bahloul, M.R. |
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| Format: | Article |
| Institution: | Universiti Teknologi Petronas |
| Record Id / ISBN-0: | utp-eprints.21470 / |
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Elsevier Ltd
2018
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https://www.scopus.com/inward/record.uri?eid=2-s2.0-85046446150&doi=10.1016%2fj.bspc.2018.04.010&partnerID=40&md5=3a5adf1694a9333e1b9bf2d967e9af75 http://eprints.utp.edu.my/21470/ |
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utp-eprints.214702018-11-16T08:24:43Z Discrimination of four class simple limb motor imagery movements for brain�computer interface Abdalsalam M, E. Yusoff, M.Z. Mahmoud, D. Malik, A.S. Bahloul, M.R. The discrimination of four simple limb motor imagery movements for brain-computer interface (BCI) applications is still challenging. This is because most of the movement imaginations have close spatial representations on the motor cortex area. Nevertheless, due to its potential applications in significant areas including BCI, solutions need to be formulated to overcome the task discrimination issues faced when a motor imagery movement approach is utilized. Feature extraction is one of the most important steps in any BCI system; as such, enhancement to the existing methods has been incorporated in this work. For this, we propose four-class movement imaginations of the right hand, left hand, right foot, and left foot, and develop feature extraction methods utilizing discrete wavelet transform (DWT) and empirical mode decomposition (EMD); in both methods, artificial neural network (ANN) was used as a classifier. Based on the processed electroencephalography (EEG) data recorded from eleven subjects, it can be seen that EMD features outperform DWT features; the average accuracy achieved by the EMD features is 90.02, and 84.77 using the DWT features. EMD even performs better than DWT in discriminating the most challenging tasks involving the right foot and left foot imageries, whose EEG data were derived from the same Cz node of the motor cortex. © 2018 Elsevier Ltd Elsevier Ltd 2018 Article PeerReviewed https://www.scopus.com/inward/record.uri?eid=2-s2.0-85046446150&doi=10.1016%2fj.bspc.2018.04.010&partnerID=40&md5=3a5adf1694a9333e1b9bf2d967e9af75 Abdalsalam M, E. and Yusoff, M.Z. and Mahmoud, D. and Malik, A.S. and Bahloul, M.R. (2018) Discrimination of four class simple limb motor imagery movements for brain�computer interface. Biomedical Signal Processing and Control, 44 . pp. 181-190. http://eprints.utp.edu.my/21470/ |
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Universiti Teknologi Petronas |
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UTP Institutional Repository |
| description |
The discrimination of four simple limb motor imagery movements for brain-computer interface (BCI) applications is still challenging. This is because most of the movement imaginations have close spatial representations on the motor cortex area. Nevertheless, due to its potential applications in significant areas including BCI, solutions need to be formulated to overcome the task discrimination issues faced when a motor imagery movement approach is utilized. Feature extraction is one of the most important steps in any BCI system; as such, enhancement to the existing methods has been incorporated in this work. For this, we propose four-class movement imaginations of the right hand, left hand, right foot, and left foot, and develop feature extraction methods utilizing discrete wavelet transform (DWT) and empirical mode decomposition (EMD); in both methods, artificial neural network (ANN) was used as a classifier. Based on the processed electroencephalography (EEG) data recorded from eleven subjects, it can be seen that EMD features outperform DWT features; the average accuracy achieved by the EMD features is 90.02, and 84.77 using the DWT features. EMD even performs better than DWT in discriminating the most challenging tasks involving the right foot and left foot imageries, whose EEG data were derived from the same Cz node of the motor cortex. © 2018 Elsevier Ltd |
| format |
Article |
| author |
Abdalsalam M, E. Yusoff, M.Z. Mahmoud, D. Malik, A.S. Bahloul, M.R. |
| spellingShingle |
Abdalsalam M, E. Yusoff, M.Z. Mahmoud, D. Malik, A.S. Bahloul, M.R. Discrimination of four class simple limb motor imagery movements for brain�computer interface |
| author_sort |
Abdalsalam M, E. |
| title |
Discrimination of four class simple limb motor imagery movements for brain�computer interface |
| title_short |
Discrimination of four class simple limb motor imagery movements for brain�computer interface |
| title_full |
Discrimination of four class simple limb motor imagery movements for brain�computer interface |
| title_fullStr |
Discrimination of four class simple limb motor imagery movements for brain�computer interface |
| title_full_unstemmed |
Discrimination of four class simple limb motor imagery movements for brain�computer interface |
| title_sort |
discrimination of four class simple limb motor imagery movements for brain�computer interface |
| publisher |
Elsevier Ltd |
| publishDate |
2018 |
| url |
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85046446150&doi=10.1016%2fj.bspc.2018.04.010&partnerID=40&md5=3a5adf1694a9333e1b9bf2d967e9af75 http://eprints.utp.edu.my/21470/ |
| _version_ |
1741196464862789632 |
| score |
11.62408 |