title: Feature selection and classifier parameters estimation for EEG signals peak detection using particle swarm optimization.
abstract: Electroencephalogram (EEG) signal peak detection is widely used in clinical applications. The peak point can be detected using several approaches, including time, frequency, time-frequency, and nonlinear domains depending on various peak features from several models. However, there is no study that provides the importance of every peak feature in contributing to a good and generalized model. In this study, feature selection and classifier parameters estimation based on particle swarm optimization (PSO) are proposed as a framework for peak detection on EEG signals in time domain analysis. Two versions of PSO are used in the study: (1) standard PSO and (2) random asynchronous particle swarm optimization (RA-PSO). The proposed framework tries to find the best combination of all the available features that offers good peak detection and a high classification rate from the results in the conducted experiments. The evaluation results indicate that the accuracy of the peak detection can be improved up to 99.90% and 98.59% for training and testing, respectively, as compared to the framework without feature selection adaptation. Additionally, the proposed framework based on RA-PSO offers a better and reliable classification rate as compared to standard PSO as it produces low variance model.
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 Engineering, Universiti Teknologi Malaysia, 81310 Johor Bahru, Malaysia.
authors.forename: Mohd Ibrahim
authors.identifier: 0000-0003-0594-8231
authors.initials: MI
authors.lastname: Shapiai
authors.affiliation: Faculty of Electrical and Electronic Engineering, Universiti Malaysia Pahang, 26600 Pekan, Pahang, Malaysia.
authors.forename: Mohd Zaidi Mohd
authors.initials: MZ
authors.lastname: Tumari
authors.affiliation: Faculty of Computing, Universiti Teknologi Malaysia, 81310 Johor Bahru, Malaysia.
authors.forename: Mohd Saberi
authors.initials: MS
authors.lastname: Mohamad
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.identifier: 0000-0001-7565-7822
authors.initials: M
authors.lastname: Mubin
mesh_terms: D000465:Algorithms; D004569:Electroencephalography / Q000379:methods*; D005133:Eye Movements / Q000502:physiology*; D006801:Humans; D010775:Photic Stimulation / Q000379:methods*