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Wavelet Enhanced CCA for Minimization of Ocular and Muscle Artifacts in EEG

Authors: B. S. Raghavendra, D. Narayana Dutt


Electroencephalogram (EEG) recordings are often contaminated with ocular and muscle artifacts. In this paper, the canonical correlation analysis (CCA) is used as blind source separation (BSS) technique (BSS-CCA) to decompose the artifact contaminated EEG into component signals. We combine the BSSCCA technique with wavelet filtering approach for minimizing both ocular and muscle artifacts simultaneously, and refer the proposed method as wavelet enhanced BSS-CCA. In this approach, after careful visual inspection, the muscle artifact components are discarded and ocular artifact components are subjected to wavelet filtering to retain high frequency cerebral information, and then clean EEG is reconstructed. The performance of the proposed wavelet enhanced BSS-CCA method is tested on real EEG recordings contaminated with ocular and muscle artifacts, for which power spectral density is used as a quantitative measure. Our results suggest that the proposed hybrid approach minimizes ocular and muscle artifacts effectively, minimally affecting underlying cerebral activity in EEG recordings.

Keywords: Blind source separation, Canonical correlationanalysis, Electroencephalogram, Muscle artifact, Ocular artifact, Power spectrum, Wavelet threshold.

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[1] Croft RJ, Barry RJ. Removal of ocular artifact from the EEG: a review. Clin Neurophysiology 2000; 30: 5-19.
[2] Narasimhan SV, Dutt DN. Application of LMS adaptive filtering for muscle artifact (noise) cancellation from EEG signals. Computers Elect Engg 1996; 22(1): 13-20.
[3] Croft RJ, Barry RJ. EOG correction: a new perspective. Electroenc and Clin Neurophys 1998; 107: 387-394.
[4] Schlogl A, Keinrath C, Zimmermann D, Scherer R, Leeb R, Pfurtscheller G. A fully automated correction method of EOG artifacts in EEG recordings. Clin Neurophysiol 2007; 118: 98-104.
[5] Wallstorm GL, Kass RE, Miller A, Cohn JF, Fox NA, Automatic correction of ocular artifacts in the EEG: a comparison of regression based and component based methods. Int J of Psychophys 2004; 53: 105-119.
[6] Bell AJ, Sejnowski TJ. An information maximization approach to blind separation and blind deconvolution. Neural Comput 1995; 7: 1129-1159.
[7] Jung T-P, Humphries C, Lee T-W, Makeig S, McKeown MJ, Iragui V, Sejnowski TJ. Extended ICA removes artifacts from electroencephalographic recordings. Advances in Neural Information Processing 1998; 10: 894-900.
[8] Jung T-P, Makeig S, Humphries C, Lee T-W, McKeown MJ, Iragui V, Sejnowski TJ. Removing electroencephalographic artifacts by blind source separation. Psychophysiol 2000; 37: 163-178.
[9] Sadasivan PK, Dutt DN. SVD based technique for noise reduction in electroencephalographic signals. Signal Proc 1996; 55: 179-189.
[10] Sadasivan PK, Dutt DN. Development of Newton-type adaptive algorithms for minimization of EOG artifacts from noisy EEG signals. Signal Proc 1997; 62: 173-186.
[11] Ille N, Berg P, Scherg M. Artifact correction of the ongoing EEG using spatial filters based on artifact and brain signal topographies. J Clin Neurophys 2002; 19: 113-124.
[12] James CJ, Gibson J. Temporally constrained ICA: an approach to artifact rejection in electromagnetic brain signal analysis. IEEE Trans Biomed Eng 2003; 50(9): 1108-1116.
[13] Clercq WD, Vergult A, Vanrumste B, Paesschen WV, Huffel SV. Canonical correlation analysis applied to remove muscle artifacts from the electroencephalogram. IEEE Trans Biomed Eng 2006; 53: 2583- 2587.
[14] Friman O, Cedefamn J, Lundberg P, Borga M, Knutsson H. Detection of neural activity in functional MRI using canonical correlation analysis. Magnetic Resonance in Medicine 2001; 45: 323-330.
[15] Friman O, Borga M, Lundberg P, Knutsson H. Exploratory fMRI analysis by autocorrelation maximization. NeuroImage 2002; 16(2): 454- 464.
[16] Donoho DL. Denoising by soft-thresholding. IEEE Trans Inform Theory 1995; 41(3): 613-627.
[17] Hyvorinen A, Oja E. Independent component analysis: algorithm and applications. Neural Netw 2000; 13: 411-430.
[18] Castellanos NP, Makarov VA. Recovering EEG brain signals: Artifact suppression with wavelet enhanced independent component analysis. Journal of Neurosc Methods 2006; 158(2): 300-312.