Title: Descendent adaptive noise cancellers to improve SNR of contaminated EEG with gradient-based and evolutionary approach

Authors: Mitul Kumar Ahirwal; Anil Kumar; Girish Kumar Singh

Addresses: PDPM Indian Institute of Information Technology, Design and Manufacturing, Jabalpur 482011, Madhya Pradesh, India ' PDPM Indian Institute of Information Technology, Design and Manufacturing, Jabalpur 482011, Madhya Pradesh, India ' Department of Electrical Engineering, Indian Institute of Technology Roorkee, Uttrakhand 247667, India; Department of Electrical Engineering, University of Malaya, Kuala Lumpur, Malaysia

Abstract: In this paper, an Adaptive Noise Canceller (ANC) technique for different artefacts cancellations from the Electroencephalogram (EEG) signals is presented. The proposed technique is based on gradient based adaptive algorithms such as Least Mean Square (LMS), Normalised Least Mean Square (N-LMS) and Recursive Least Square (RLS) algorithms and an evolutionary algorithm like particle swarm optimisation (PSO) technique. Descendent structure is made through three adaptive noise cancellers for the removal of line noise, ECG and EOG artefacts. When compared, the adaptive noise canceller technique based on PSO performs better than all gradient based approaches. Several examples are included to illustrate the effectiveness of the proposed method in terms of the quality, for better and correct interpretation of EEG.

Keywords: adaptive filters; contaminated EEG signals; electroencephalograms; artefacts; least mean squares; normalised LMS; recursive least squares; particle swarm optimisation; NLMS; RLS; PSO; adaptive noise canceller; SNR; signal to noise ratio.

DOI: 10.1504/IJBET.2013.057713

International Journal of Biomedical Engineering and Technology, 2013 Vol.13 No.1, pp.49 - 68

Received: 16 Jul 2013
Accepted: 07 Oct 2013
Published online: 27 Sep 2014 *

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