title: Feature selection using angle modulated simulated Kalman filter for peak classification of EEG signals.
abstract: 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.
authors.affiliation: Applied Control and Robotics (ACR) Laboratory, Department of Electrical Engineering, Faculty of Engineering, University of Malaya, 50603 Kuala Lumpur, Malaysia.
authors.forename: Asrul
authors.initials: A
authors.lastname: Adam
authors.affiliation: Faculty of Electrical and Electronic Engineering, Universiti Malaysia Pahang, 26600 Pekan, Pahang Malaysia.
authors.forename: Zuwairie
authors.initials: Z
authors.lastname: Ibrahim
authors.affiliation: Applied Control and Robotics (ACR) Laboratory, Department of Electrical Engineering, Faculty of Engineering, University of Malaya, 50603 Kuala Lumpur, Malaysia.
authors.forename: Norrima
authors.initials: N
authors.lastname: Mokhtar
authors.affiliation: Malaysia-Japan International Institute of Technology, Universiti Teknologi Malaysia Kuala Lumpur, Jalan Semarak, 54100 Kuala Lumpur, Malaysia.
authors.forename: Mohd Ibrahim
authors.initials: MI
authors.lastname: Shapiai
authors.affiliation: Applied Control and Robotics (ACR) Laboratory, Department of Electrical Engineering, Faculty of Engineering, University of Malaya, 50603 Kuala Lumpur, Malaysia.
authors.forename: Marizan
authors.initials: M
authors.lastname: Mubin
authors.affiliation: Artificial Intelligence Research Unit (AiRU), Faculty of Engineering, Universiti Malaysia Sabah, Jalan UMS, 88400 Kota Kinabalu, Sabah Malaysia.
authors.forename: Ismail
authors.initials: I
authors.lastname: Saad
keywords: Electroencephalogram (EEG); Kalman filtering; Neural network with random weights (NNRW); Pattern recognition; Peak detection algorithm; Simulated Kalman filter (SKF)