Important methods concerning artifact removal from EEG signals has been briefly described pertaining to its significance and its drawbacks. Some methods described herein range from conventional methods such as linear filtering, Linear combination and regression (LCR) to more contemporary methods such as blind source separation (BSS) with applications such as Principal component analysis (PCA) and Independent component Analysis (ICA) including the more recent wavelet based transformation methods (such as Discrete Wavelet Transform and Wave Packet decomposition). It is observed that these methods complement each other in perspective of their drawbacks, therefore a novel combination in some of these methods particularly the ICA and Wavelet based Transform results in a much better balance between statistical considerations, practicality and computational efficiency.

1. R. Princy. , P. Thamarai, and B. Karthik, "Denoising EEG Signal Using Wavelet Transform", International Journal of Advanced Research in Computer Engineering & Technology, Vol. 4, Issue. 3, 2015.
2. A. G. Reddy and S. Narava, "Artifact removal from EEG Signals. "International Journal of Computer Applications. Vol. 77, No. 13, 2013.
3. D. Jyoti, S. Ahmad, and K. Gulia, "Comparison between Adaptive filter Algorithms (LMS, NLMS and RLS). " International Journal of Science, Engineering and Technology Research Vol. 2, No. 5, pp-1100, 2013.
4. L. I. Smith, A tutorial on principal components analysis. Cornell University, USA, Vol. 51, 2015, pp. 52.
5. T. Berg; O. Røyset and E. Steinnes, "Principal component analysis of data for trace elements and main components in precipitation falling on Norway. " Environmental monitoring and assessment, Vol. 31, No. 3 1994.
6. G. L Wallstrom; R. E Kass; A. Miller; J. F Cohn and N. A Fox, "Automatic correction of ocular artifacts in the EEG: a comparison of regression-based and component-based methods. " International journal of psychophysiology, Vol. 53, No. 2 , pp. 105-119, 2004.
7. M. E Wall; A. Rechtsteiner and Luis M. Rocha, "Singular value decomposition and principal component analysis", A practical approach to microarray data analysis, Springer US,Vol. 91, No. 109, 2003.
8. R. Vigário; V . Jousmiiki; M. Hiimiiliiinen; R. Hari; E. Oja, "Independent component analysis for identification of artifacts in magnetoencephalographic recordings" Advances in neural information processing systems, pp. 229-235, 1998.
9. M. Ungureanu; C. Bigan; R. Strungaru and V. Lazarescu, "Independent component analysis applied in biomedical signal processing", Measurement Science Review, Vol. 4, No. 2, pp. 18, 2004.
10. A. J. Bell and T. J. Sejnowski. "An information-maximization approach to blind separation and blind deconvolution", Neural computation,Vol. 7, No. 6, pp. 1129-1159, 1995.
11. T. W. Lee; M. Girolami and T. J. Sejnowski. "Independent component analysis using an extended infomax algorithm for mixed subgaussian and supergaussian sources", Neural computation Vol. 11, No. 2, pp. 417-441,1999.
12. J. F. Cardoso; "High-order contrasts for independent component analysis", Neural computation Vol. 11, No. 1, pp. 157-192, 1999.
13. J. W. Williams and Yan Li. "Performance comparison of known ICA algorithms to a wavelet-ICA merger", Signal Process, Vol. 5, pp. 80-92, 2011.
14. C. J Barrera; L. J. Ginori, and E. V Rodríguez "A wavelet-packets based algorithm for EEG signal compression", Informatics for Health and Social Care, Vol. 29, No. 1, pp. 15-27, 2004.

Linear Filtering Lcr Bss Ica Pca Wavelet Based Transformation.