Title: Methods of denoising of electroencephalogram signal: a review

Authors: Monika Sheoran; Sanjeev Kumar; Seema Chawla

Addresses: Deenbandhu Chhotu Ram University of Science & Technology, Murthal, Sonepat 131039, India ' Biomedical Instrumentation Unit, Central Scientific Instruments Organisation, Sector 30-C, Chandigarh 160030, India ' Bio-medical Engineering Department, Deenbandhu Chhotu Ram University of Science & Technology, Murthal, Sonepat 131039, India

Abstract: Electroencephalogram (EEG) is obtained as a result of electrical activity of neurons in the brain. These signals have very small amplitudes and hence are quite prone to contamination by different artefacts. The major types of artefacts that affect the EEG are baseline wandering, power line noise, eye movements, Electromyogram (EMG) disturbance, and Electrocardiogram (ECG) disturbance. The presence of artefacts makes the analysis of EEG difficult for clinical evaluation and information. To deal with these artefacts, numerous methods and techniques have been evolved by different researchers. These methods include regression, blind source separation, wavelet and empirical mode decomposition etc. This paper provides a review of these methods for denoising of EEG signal.

Keywords: signal denoising; electroencephalograms; EEG signals; blind source separation; BSS; PCA; principal component analysis; ICA; independent component analysis; regression; wavelets; empirical mode decomposition; EMD.

DOI: 10.1504/IJBET.2015.071012

International Journal of Biomedical Engineering and Technology, 2015 Vol.18 No.4, pp.385 - 395

Received: 01 Sep 2014
Accepted: 02 Mar 2015
Published online: 05 Aug 2015 *

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