The classification of EEG signal processing using different machine learning techniques for BCI application
Brain-Computer Interface (BCI) or Human-Machine Interface is now becoming vital in biomedical engineering and technology field which applying EEG technologies to provide assistive device technology (AT) to humans. Hence, this paper presents the results of analyzing EEG signals from various human cog...
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| Language: | en en |
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Springer, Singapore
2019
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| Online Access: | http://umpir.ump.edu.my/id/eprint/24498/1/29.%20The%20classification%20of%20EEG%20signal%20processing%20using%20different.pdf http://umpir.ump.edu.my/id/eprint/24498/2/29.1%20The%20classification%20of%20EEG%20signal%20processing%20using%20different.pdf http://umpir.ump.edu.my/id/eprint/24498/ https://doi.org/10.1007/978-981-13-7780-8_17 |
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| author | Rashid, Mamunur Norizam, Sulaiman Mahfuzah, Mustafa Sabira, Khatun Bari, Bifta Sama |
| author_facet | Rashid, Mamunur Norizam, Sulaiman Mahfuzah, Mustafa Sabira, Khatun Bari, Bifta Sama |
| author_sort | Rashid, Mamunur |
| building | UMPSA Library |
| collection | Institutional Repository |
| content_provider | Universiti Malaysia Pahang Al-Sultan Abdullah |
| content_source | UMPSA Institutional Repository |
| continent | Asia |
| country | Malaysia |
| description | Brain-Computer Interface (BCI) or Human-Machine Interface is now becoming vital in biomedical engineering and technology field which applying EEG technologies to provide assistive device technology (AT) to humans. Hence, this paper presents the results of analyzing EEG signals from various human cognitive states to extract the suitable EEG features that can be em-ployed to control BCI devices which can be used by disabled or paralyzed people. The EEG features in term of power spectral density, spectral centroids, standard deviation and entropy are selected and investigated from two different mental exercises; i) quick solving math and ii) relax (do nothing). Then the se-lected features are classified using Linear Discriminant Analysis (LDA), Sup-port Vector Machine (SVM) and K-Nearest Neighbors (k-NN) classifier. Among all these features, the best accuracy has been achieved by the power spectral density. The accuracies of this feature are 95%, 100%, 100% with LDA, SVM and K-NN respectively. Finally, the translation algorithm will be con-structed using selected and classified EEG features to control the BCI devices. |
| format | Conference or Workshop Item |
| id | my.ump.umpir.24498 |
| institution | Universiti Malaysia Pahang |
| language | en en |
| publishDate | 2019 |
| publisher | Springer, Singapore |
| record_format | eprints |
| spelling | my.ump.umpir.244982020-01-20T02:25:57Z http://umpir.ump.edu.my/id/eprint/24498/ The classification of EEG signal processing using different machine learning techniques for BCI application Rashid, Mamunur Norizam, Sulaiman Mahfuzah, Mustafa Sabira, Khatun Bari, Bifta Sama TK Electrical engineering. Electronics Nuclear engineering Brain-Computer Interface (BCI) or Human-Machine Interface is now becoming vital in biomedical engineering and technology field which applying EEG technologies to provide assistive device technology (AT) to humans. Hence, this paper presents the results of analyzing EEG signals from various human cognitive states to extract the suitable EEG features that can be em-ployed to control BCI devices which can be used by disabled or paralyzed people. The EEG features in term of power spectral density, spectral centroids, standard deviation and entropy are selected and investigated from two different mental exercises; i) quick solving math and ii) relax (do nothing). Then the se-lected features are classified using Linear Discriminant Analysis (LDA), Sup-port Vector Machine (SVM) and K-Nearest Neighbors (k-NN) classifier. Among all these features, the best accuracy has been achieved by the power spectral density. The accuracies of this feature are 95%, 100%, 100% with LDA, SVM and K-NN respectively. Finally, the translation algorithm will be con-structed using selected and classified EEG features to control the BCI devices. Springer, Singapore 2019-04 Conference or Workshop Item PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/24498/1/29.%20The%20classification%20of%20EEG%20signal%20processing%20using%20different.pdf pdf en http://umpir.ump.edu.my/id/eprint/24498/2/29.1%20The%20classification%20of%20EEG%20signal%20processing%20using%20different.pdf Rashid, Mamunur and Norizam, Sulaiman and Mahfuzah, Mustafa and Sabira, Khatun and Bari, Bifta Sama (2019) The classification of EEG signal processing using different machine learning techniques for BCI application. In: RiTA 2018: Robot Intelligence Technology and Applications , 16-18 December 2018 , Putrajaya, Selangor, Malaysia. pp. 207-221.. ISBN 978-981-13-7779-2 (Published) https://doi.org/10.1007/978-981-13-7780-8_17 |
| spellingShingle | TK Electrical engineering. Electronics Nuclear engineering Rashid, Mamunur Norizam, Sulaiman Mahfuzah, Mustafa Sabira, Khatun Bari, Bifta Sama The classification of EEG signal processing using different machine learning techniques for BCI application |
| title | The classification of EEG signal processing using different machine learning techniques for BCI application |
| title_full | The classification of EEG signal processing using different machine learning techniques for BCI application |
| title_fullStr | The classification of EEG signal processing using different machine learning techniques for BCI application |
| title_full_unstemmed | The classification of EEG signal processing using different machine learning techniques for BCI application |
| title_short | The classification of EEG signal processing using different machine learning techniques for BCI application |
| title_sort | classification of eeg signal processing using different machine learning techniques for bci application |
| topic | TK Electrical engineering. Electronics Nuclear engineering |
| url | http://umpir.ump.edu.my/id/eprint/24498/1/29.%20The%20classification%20of%20EEG%20signal%20processing%20using%20different.pdf http://umpir.ump.edu.my/id/eprint/24498/2/29.1%20The%20classification%20of%20EEG%20signal%20processing%20using%20different.pdf http://umpir.ump.edu.my/id/eprint/24498/ https://doi.org/10.1007/978-981-13-7780-8_17 |
| url_provider | http://umpir.ump.edu.my/ |