Feature selection using angle modulated simulated Kalman filter for peak classification of EEG signals.
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| Title: | Feature selection using angle modulated simulated Kalman filter for peak classification of EEG signals. |
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| Authors: | Adam A; Applied Control and Robotics (ACR) Laboratory, Department of Electrical Engineering, Faculty of Engineering, University of Malaya, 50603 Kuala Lumpur, Malaysia., Ibrahim Z; Faculty of Electrical and Electronic Engineering, Universiti Malaysia Pahang, 26600 Pekan, Pahang Malaysia., Mokhtar N; Applied Control and Robotics (ACR) Laboratory, Department of Electrical Engineering, Faculty of Engineering, University of Malaya, 50603 Kuala Lumpur, Malaysia., Shapiai MI; Malaysia-Japan International Institute of Technology, Universiti Teknologi Malaysia Kuala Lumpur, Jalan Semarak, 54100 Kuala Lumpur, Malaysia., Mubin M; Applied Control and Robotics (ACR) Laboratory, Department of Electrical Engineering, Faculty of Engineering, University of Malaya, 50603 Kuala Lumpur, Malaysia., Saad I; Artificial Intelligence Research Unit (AiRU), Faculty of Engineering, Universiti Malaysia Sabah, Jalan UMS, 88400 Kota Kinabalu, Sabah Malaysia. |
| Source: | SpringerPlus [Springerplus] 2016 Sep 15; Vol. 5 (1), pp. 1580. Date of Electronic Publication: 2016 Sep 15 (Print Publication: 2016). |
| Publication Type: | Journal Article |
| Journal Info: | Publisher: SpringerPlus Country of Publication: Switzerland NLM ID: 101597967 Publication Model: eCollection Cited Medium: Print ISSN: 2193-1801 (Print) Linking ISSN: 21931801 NLM ISO Abbreviation: Springerplus Subsets: PubMed not MEDLINE |
| Database: | MEDLINE Ultimate |
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