Feature Selection using Angle Modulated Simulated Kalman Filter for Peak Classification of EEG Signals
In the existing electroencephalogram (EEG) signals peak classification research, the existing models, such as Dumpala, Acir, Liu, and Dingle peak models, employ different set of features. However, all these models may not be able to offer good performance for various applications and it is found to...
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| Format: | Article |
| Language: | English |
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Springer
2016
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| Online Access: | http://umpir.ump.edu.my/id/eprint/14661/1/Feature%20selection%20using%20angle%20modulated%20simulated%20Kalman%20filter%20for%20peak%20classification%20of%20EEG%20signals.pdf |
| _version_ | 1825823204962205696 |
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| author | Asrul, Adam Zuwairie, Ibrahim Norrima, Mokhtar Mohd Ibrahim, Shapiai Marizan, Mubin Ismail, Saad |
| author_facet | Asrul, Adam Zuwairie, Ibrahim Norrima, Mokhtar Mohd Ibrahim, Shapiai Marizan, Mubin Ismail, Saad |
| author_sort | Asrul, Adam |
| collection | UMP |
| description | In the existing electroencephalogram (EEG) signals peak classification research, the existing models, such as Dumpala, Acir, Liu, and Dingle peak models, employ different set of features. However, all these models may not be able to offer good performance for various applications and it is found to be problem dependent. Therefore, the objective of this study is to combine all the associated features from the existing models before selecting the best combination of features. A new optimization algorithm, namely as angle modulated simulated Kalman filter (AMSKF) will be employed as feature selector. Also, the neural network random weight method is utilized in the proposed AMSKF technique as a classifier. In the conducted experiment, 11,781 samples of peak candidate are employed in this study for the validation purpose. The samples are collected from three different peak event-related EEG signals of 30 healthy subjects; (1) single eye blink, (2) double eye blink, and (3) eye movement signals. The experimental results have shown that the proposed AMSKF feature selector is able to find the best combination of features and performs at par with the existing related studies of epileptic EEG events classification. |
| first_indexed | 2024-03-06T12:07:56Z |
| format | Article |
| id | UMPir14661 |
| institution | Universiti Malaysia Pahang |
| language | English |
| last_indexed | 2024-03-06T12:07:56Z |
| publishDate | 2016 |
| publisher | Springer |
| record_format | dspace |
| spelling | UMPir146612018-02-08T00:28:46Z http://umpir.ump.edu.my/id/eprint/14661/ Feature Selection using Angle Modulated Simulated Kalman Filter for Peak Classification of EEG Signals Asrul, Adam Zuwairie, Ibrahim Norrima, Mokhtar Mohd Ibrahim, Shapiai Marizan, Mubin Ismail, Saad TK Electrical engineering. Electronics Nuclear engineering In the existing electroencephalogram (EEG) signals peak classification research, the existing models, such as Dumpala, Acir, Liu, and Dingle peak models, employ different set of features. However, all these models may not be able to offer good performance for various applications and it is found to be problem dependent. Therefore, the objective of this study is to combine all the associated features from the existing models before selecting the best combination of features. A new optimization algorithm, namely as angle modulated simulated Kalman filter (AMSKF) will be employed as feature selector. Also, the neural network random weight method is utilized in the proposed AMSKF technique as a classifier. In the conducted experiment, 11,781 samples of peak candidate are employed in this study for the validation purpose. The samples are collected from three different peak event-related EEG signals of 30 healthy subjects; (1) single eye blink, (2) double eye blink, and (3) eye movement signals. The experimental results have shown that the proposed AMSKF feature selector is able to find the best combination of features and performs at par with the existing related studies of epileptic EEG events classification. Springer 2016 Article PeerReviewed application/pdf en http://umpir.ump.edu.my/id/eprint/14661/1/Feature%20selection%20using%20angle%20modulated%20simulated%20Kalman%20filter%20for%20peak%20classification%20of%20EEG%20signals.pdf Asrul, Adam and Zuwairie, Ibrahim and Norrima, Mokhtar and Mohd Ibrahim, Shapiai and Marizan, Mubin and Ismail, Saad (2016) Feature Selection using Angle Modulated Simulated Kalman Filter for Peak Classification of EEG Signals. SpringerPlus. pp. 1-24. ISSN 2193-1801. (Published) http://springerplus.springeropen.com/articles DOI: 10.1186/s40064-016-3277-z |
| spellingShingle | TK Electrical engineering. Electronics Nuclear engineering Asrul, Adam Zuwairie, Ibrahim Norrima, Mokhtar Mohd Ibrahim, Shapiai Marizan, Mubin Ismail, Saad Feature Selection using Angle Modulated Simulated Kalman Filter for Peak Classification of EEG Signals |
| title | Feature Selection using Angle Modulated Simulated Kalman Filter for Peak Classification of EEG Signals |
| title_full | Feature Selection using Angle Modulated Simulated Kalman Filter for Peak Classification of EEG Signals |
| title_fullStr | Feature Selection using Angle Modulated Simulated Kalman Filter for Peak Classification of EEG Signals |
| title_full_unstemmed | Feature Selection using Angle Modulated Simulated Kalman Filter for Peak Classification of EEG Signals |
| title_short | Feature Selection using Angle Modulated Simulated Kalman Filter for Peak Classification of EEG Signals |
| title_sort | feature selection using angle modulated simulated kalman filter for peak classification of eeg signals |
| topic | TK Electrical engineering. Electronics Nuclear engineering |
| url | http://umpir.ump.edu.my/id/eprint/14661/1/Feature%20selection%20using%20angle%20modulated%20simulated%20Kalman%20filter%20for%20peak%20classification%20of%20EEG%20signals.pdf |
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