Artifacts Removal of EEG Signals By the Application of ICA and Double Density DWT Algorithm

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Vandana Roy 1,* Shailja Shukla 2

1. Department of Electronics & Communication, Jabalpur Engineering College, Jabalpur, Madhya Pradesh, India

2. Department of Computer Science Engineering, Jabalpur Engineering College, Jabalpur, Madhya Pradesh, India

* Corresponding author.


Received: 15 May 2014 / Revised: 18 Jun. 2014 / Accepted: 24 Jul. 2014 / Published: 26 Aug. 2014

Index Terms

Artifacts, EEG Signals, Double Density, Wavelet Denoising


Independent Component Analysis is used for the automation and detection of brain artifacts. The Independent Component Analysis (ICA) here is used for the segmentation of artifact peaks in the signal. Then the Discrete Wavelet Transform is applied for multi-level transfer of signal data until the reception of significant result. We have extended our search and applied the Double Density Algorithm for the multi-level transfer. The results obtained were analyzed from the data set of EEG signals taken with a outsource reference. Since the method is parameter free implementations in clinical settings are imaginable.

Cite This Paper

Vandana Roy, Shailja Shukla,"Artifacts Removal of EEG Signals By the Application of ICA and Double Density DWT Algorithm", IJEM, vol.4, no.2, pp.42-55, 2014. DOI: 10.5815/ijem.2014.02.04


[1] eegsemi nar/pdfs/EEGPrimer Ch6.pdf.

[2]Iván Manuel Benito Núñez, “EEG Artifact Detection”. Department of Cybernetics Czech Technical University, Prague.

[3]Jorge Baztarrica Ochoa, “EEG Signal Classification for Brain Computer Interface Applications”. wvt/bz.pdf

[4]Tzyy-Ping Jung & Scott Makeig, “Removing Artifacts from EEG” artifact.html

[5]Arnaud Delorme, Terrence Sejnowski, and Scott Makeig, “Enhanced detection of artifacts in EEG data using higher-order statistics and independent component analysis” Elsevier, NeuroImage 34 (2007) 1443–1449.

[6]P. Senthil Kumar, R. Arumuganathan, K. Sivakumar, and C. Vimal, “Removal of Ocular Artifacts in the EEG through Wavelet Transform without using an EOG Reference Channel” Int. J. Open Problems Compt. Math., Vol. 1, No. 3, December 2008.

[7]Joseph T. Gwin, Klaus Gramann, Scott Makeig, and Daniel P. Ferris, “Removal of Movement Artifact From High-Density EEG Recorded During Walking and Running” J Neurophysiol 103: 3526–3534, 2010.

[8]M. Ungureanu, C. Bigan, R. Strungaru, V. Lazarescu, “Independent Component Analysis Applied in Biomedical Signal Processing”. Measurement Science Review, Volume 4, Section 2, 2004.

[9]W. Selesnick, "The double-density dual-tree DWT," IEEE Trans. Signal Processing, vol. 52, no.5, 2004, pp. 1304-1314.

[10]Ivan W. Selesnick, “The Double Density Dwt” Polytechnic University. Brooklyn, NY.

[11]“Rodrigo Quian Quiroga, EEG, ERP and single cell recordings database”. ~rodri/data.htm

[12]Ivan W. Selesnick, “The Double-Density Dual-Tree DWT” IEEE TRANSACTIONS ON SIGNAL PROCESSING, VOL. 52, NO. 5, MAY 2004.

[13]Flexer A., Bawer H., Pripfl J., Dorffner G.:Using ICA for removal of ocular artifacts in EEG recorded from blind subjects :, Neural Networks 18.7 , 2005, 998-1005.

[14]Klados and Manousos A.: REG-ICA: A hybrid methodology combining Blind Source Separation and regression techniques for the rejection of ocular artifacts: Biomedical Signal Processing and Control 6.3, 2011, 291-300.

[15]Lindsen, Job P., and Joydeep B.: Correction of blink artifacts using independent component analysis and empirical mode decomposition: Psychophysiology 47.5, 2010, 955-960.

[16]Escudero J., Hornero R., Abasolo D., Fernandez A.: Quantitative evaluation of artifact removal in real magnetoencephalogram signals with blind source separation: Annals of biomedical engineering 39.8, 2011, 2274-2286.

[17]Flexer A., Bauer H, Pripfl J, Dorffner G.:Using ICA for removal of ocular artifacts in EEG recorded from blind subjects: Neural Networks 2005;18,998–1005.

[18]Hyv¨arinen A., Pajunen P.: Nonlinear independent component analysis: existence and uniqueness results: Neural Networks 1999, 12, 209–219.

[19]Chao, Jih-Cheng, and Scott C. Douglas. : A robust complex FastICA algorithm using the huber M-estimator cost function; Independent Component Analysis and Signal Separation: Springer Berlin Heidelberg, 2007, 152-160.

[20]Arora S., Ge R., Moitra A., Sachdeva S.: Provable ICA with unknown Gaussian noise, and implications for Gaussian mixtures and autoencoders : arXiv preprint arXiv:1206.5349, 2012.