Feature selection and classifier parameters estimation for EEG signals peak detection using particle swarm optimization

• Main Authors: Adam, Asrul, Shapiai, Mohd. Ibrahim, Mohd. Tumari, Mohd. Zaidi, Mohamad, Mohd. Saberi, Mubin, Marizan
• Format: Article
• Language: English
• Published: Hindawi Publishing Corporation 2014
• Subjects: TK Electrical engineering. Electronics Nuclear engineering
• Online Access: http://eprints.utm.my/id/eprint/52873/1/Mohd.IbrahimShapiai2014_Featureselectionandclassifier.pdf; http://eprints.utm.my/id/eprint/52873/; http://dx.doi.org/10.1155/2014/973063

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.