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...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Springer
2016
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| Subjects: | |
| Online Access: | http://umpir.ump.edu.my/id/eprint/14661/ http://umpir.ump.edu.my/id/eprint/14661/ http://umpir.ump.edu.my/id/eprint/14661/ http://umpir.ump.edu.my/id/eprint/14661/1/Feature%20selection%20using%20angle%20modulated%20simulated%20Kalman%20filter%20for%20peak%20classification%20of%20EEG%20signals.pdf |
Internet
http://umpir.ump.edu.my/id/eprint/14661/http://umpir.ump.edu.my/id/eprint/14661/
http://umpir.ump.edu.my/id/eprint/14661/
http://umpir.ump.edu.my/id/eprint/14661/1/Feature%20selection%20using%20angle%20modulated%20simulated%20Kalman%20filter%20for%20peak%20classification%20of%20EEG%20signals.pdf