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Feature Selection and Classifier Parameters Estimation for EEG Signals Peak Detection Using Particle Swarm Optimization

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...

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Bibliographic Details
Main Authors: Mohd Zaidi, Mohd Tumari, Asrul, Adam, Mohd Ibrahim, Shapiai, Mohd Saberi, Mohamad, Marizan, Mubin
Format: Article
Language:English
Published: Hindawi Publishing Corporation 2014
Subjects:
TK Electrical engineering. Electronics Nuclear engineering
Online Access:http://umpir.ump.edu.my/id/eprint/6465/1/Feature_Selection_and_Classifier_Parameters_Estimation_for_EEG_Signals_Peak_Detection_Using_Particle_Swarm_Optimization.pdf
http://umpir.ump.edu.my/id/eprint/6465/
http://dx.doi.org/10.1155/2014/973063
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http://umpir.ump.edu.my/id/eprint/6465/1/Feature_Selection_and_Classifier_Parameters_Estimation_for_EEG_Signals_Peak_Detection_Using_Particle_Swarm_Optimization.pdf
http://umpir.ump.edu.my/id/eprint/6465/
http://dx.doi.org/10.1155/2014/973063

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