Biomedical Engineering Letters

, Volume 2, Issue 3, pp 186–197

Analysis and testing of PSO variants through application in EEG/ERP adaptive filtering approach

  • Mitul Kumar Ahirwal
  • Anil Kumar
  • Girish Kumar Singh
Original Article

DOI: 10.1007/s13534-012-0071-x

Cite this article as:
Ahirwal, M.K., Kumar, A. & Singh, G.K. Biomed. Eng. Lett. (2012) 2: 186. doi:10.1007/s13534-012-0071-x



An improved method for adaptive noise canceller (ANC) is proposed for electroencephalography (EEG)/event related potential (ERP) filtering in case of EEG self interference. ANC is implemented through five versions of Particles Swarm Optimization (PSO) technique.


A comparative study of the performance of PSO and its different versions such as constant weighted inertia PSO (CWI PSO), linear decay inertia PSO (LDI-PSO), constriction factors inertia PSO (CFI-PSO), nonlinear inertia PSO (NLI-PSO), and dynamic inertia PSO (DI-PSO) has been done. Fidelity parameters like signal to noise ratio (SNR) in dB, correlation between resultant and template ERP, and mean square error (MSE) are observed with varying range of particles and inertia weights.


In this the results of two data sets, simulated ERP and real visual evoked potential (VEP) are compared. Fidelity parameters as well as quality (shape) of extracted ERP are determined with Kurtosis and skewness measures.


From the simulation results and comparative studies, it is found that that NLI and LDI version of PSO are most suitable for ANC for ERP filtering.



Copyright information

© Korean Society of Medical and Biological Engineering and Springer 2012

Authors and Affiliations

  • Mitul Kumar Ahirwal
    • 1
  • Anil Kumar
    • 1
  • Girish Kumar Singh
    • 2
  1. 1.PDPM Indian Institute of Information Technology, Design and ManufacturingJabalpurIndia
  2. 2.Department of Electrical EngineeringIndian Institute of TechnologyRoorkeeIndia

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