Classification of four class motor imagery for brain computer interface
In this paper, four class motor imagery classification has been studied for brain computer interface. Feature investigations were conducted on the Enobio device, firstly with all 8 channels (F3, F4, T7, C3, C4, Cz, T8 and Pz) and subsequently with 3 selected channels (C4 left hand, C3 right hand, C3...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Published: |
Springer Verlag
2017
|
| Online Access: | https://www.scopus.com/inward/record.uri?eid=2-s2.0-84992688800&doi=10.1007%2f978-981-10-1721-6_32&partnerID=40&md5=4d0c87ad2644f45c6724e33dc96b2f44 http://eprints.utp.edu.my/20299/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| id |
my.utp.eprints.20299 |
|---|---|
| record_format |
eprints |
| spelling |
my.utp.eprints.202992018-04-23T01:02:22Z Classification of four class motor imagery for brain computer interface Abdalsalam, E. Yusoff, M.Z. Kamel, N. Malik, A.S. Mahmoud, D. In this paper, four class motor imagery classification has been studied for brain computer interface. Feature investigations were conducted on the Enobio device, firstly with all 8 channels (F3, F4, T7, C3, C4, Cz, T8 and Pz) and subsequently with 3 selected channels (C4 left hand, C3 right hand, C3 and C4 both hand and Cz both feet) in alpha and beta rhythm in order to establish the active networks. Five volunteers were participated, the volunteers were instructed to perform motor imagery tasks, such as to imagine the opening and closing of the left and right hand, both hands, and both feet movement. Electroencephalogram (EEG) data were collected and offline signals processing were performed. Discrete wavelet transform (DWT) was used for feature extraction, while difference classifications methods such as multilayer perceptron (MLP), RBFNetwork, and K-Nearest Neighbors (KNN) were implemented. Best classification of MLP over KNN and RBFNetwork was noticed, whereas the highest accuracy was achieved at sym8 wavelet using DWT based feature extraction. On average over the subjects the selected channel accuracies were in the range of 86.61 . Whereas for all the channels, accuracies were in range of 78.37 . The study has shown that the classification accuracy can significantly improve by using specific channels for the EEG classification rather than using all EEG channels a time. © Springer Science+Business Media Singapore 2017. Springer Verlag 2017 Article PeerReviewed https://www.scopus.com/inward/record.uri?eid=2-s2.0-84992688800&doi=10.1007%2f978-981-10-1721-6_32&partnerID=40&md5=4d0c87ad2644f45c6724e33dc96b2f44 Abdalsalam, E. and Yusoff, M.Z. and Kamel, N. and Malik, A.S. and Mahmoud, D. (2017) Classification of four class motor imagery for brain computer interface. Lecture Notes in Electrical Engineering, 398 . pp. 297-305. http://eprints.utp.edu.my/20299/ |
| institution |
Universiti Teknologi Petronas |
| building |
UTP Resource Centre |
| collection |
Institutional Repository |
| continent |
Asia |
| country |
Malaysia |
| content_provider |
Universiti Teknologi Petronas |
| content_source |
UTP Institutional Repository |
| url_provider |
http://eprints.utp.edu.my/ |
| description |
In this paper, four class motor imagery classification has been studied for brain computer interface. Feature investigations were conducted on the Enobio device, firstly with all 8 channels (F3, F4, T7, C3, C4, Cz, T8 and Pz) and subsequently with 3 selected channels (C4 left hand, C3 right hand, C3 and C4 both hand and Cz both feet) in alpha and beta rhythm in order to establish the active networks. Five volunteers were participated, the volunteers were instructed to perform motor imagery tasks, such as to imagine the opening and closing of the left and right hand, both hands, and both feet movement. Electroencephalogram (EEG) data were collected and offline signals processing were performed. Discrete wavelet transform (DWT) was used for feature extraction, while difference classifications methods such as multilayer perceptron (MLP), RBFNetwork, and K-Nearest Neighbors (KNN) were implemented. Best classification of MLP over KNN and RBFNetwork was noticed, whereas the highest accuracy was achieved at sym8 wavelet using DWT based feature extraction. On average over the subjects the selected channel accuracies were in the range of 86.61 . Whereas for all the channels, accuracies were in range of 78.37 . The study has shown that the classification accuracy can significantly improve by using specific channels for the EEG classification rather than using all EEG channels a time. © Springer Science+Business Media Singapore 2017. |
| format |
Article |
| author |
Abdalsalam, E. Yusoff, M.Z. Kamel, N. Malik, A.S. Mahmoud, D. |
| spellingShingle |
Abdalsalam, E. Yusoff, M.Z. Kamel, N. Malik, A.S. Mahmoud, D. Classification of four class motor imagery for brain computer interface |
| author_facet |
Abdalsalam, E. Yusoff, M.Z. Kamel, N. Malik, A.S. Mahmoud, D. |
| author_sort |
Abdalsalam, E. |
| title |
Classification of four class motor imagery for brain computer interface |
| title_short |
Classification of four class motor imagery for brain computer interface |
| title_full |
Classification of four class motor imagery for brain computer interface |
| title_fullStr |
Classification of four class motor imagery for brain computer interface |
| title_full_unstemmed |
Classification of four class motor imagery for brain computer interface |
| title_sort |
classification of four class motor imagery for brain computer interface |
| publisher |
Springer Verlag |
| publishDate |
2017 |
| url |
https://www.scopus.com/inward/record.uri?eid=2-s2.0-84992688800&doi=10.1007%2f978-981-10-1721-6_32&partnerID=40&md5=4d0c87ad2644f45c6724e33dc96b2f44 http://eprints.utp.edu.my/20299/ |
| _version_ |
1738656191728320512 |
| score |
13.211869 |