Journal of Engineering and Applied Sciences

Year: 2018
Volume: 13
Issue: 11
Page No. 4165 - 4172

Detection and Separation of EEG Artifacts Using Wavelet Transform

Authors : R. Suresh Kumar and P. Manimegalai

References

Arvaneh, M., C. Guan, K.K. Ang and C. Quek, 2011. Optimizing the channel selection and classification accuracy in EEG-based BCI. IEEE. Trans. Biomed. Eng., 58: 1865-1873.
CrossRef  |  PubMed  |  Direct Link  |  

Babu, P.A. and K.V.S.V.R. Prasad, 2011. Removal of ocular artifacts from EEG signals using adaptive threshold PCA and wavelet transforms. Proceedings of the 2011 International Conference on Communication Systems and Network Technologies (CSNT’11), June 3-5, 2011, IEEE, Katra, Jammu, India, ISBN:978-1-4577-0543-4, pp: 572-575.

Balli, T. and R. Palaniappan, 2010. Classification of biological signals using linear and nonlinear features. Physiol. Meas., 31: 903-920.
PubMed  |  

Barlow, J.S., 1985. Methods of analysis of nonstationary EEGs, with emphasis on segmentation techniques: A comparative review. J. Clin. Neurophysiol. Off. Publ. Am. Electroencephalographic Soc., 2: 267-304.
PubMed  |  Direct Link  |  

Bashashati, A., M. Fatourechi, R.K. Ward and G.E. Birch, 2007. A survey of signal processing algorithms in brain-computer interfaces based on electrical brain signals. J. Neural Eng., 4: R32-R57.
CrossRef  |  PubMed  |  

Chui, C.K., 1992. An Introduction to Wavelets. Posts & Telecom Press, China, Pages: 266.

Crouse, M.S., R.D. Nowak and R.G. Baraniuk, 1998. Wavelet-based statistical signal processing using hidden markov models. IEEE Trans. Signal Process., 46: 886-902.
CrossRef  |  

Davies, M.E. and C.J. James, 2007. Source separation using single channel ICA. Signal Process., 87: 1819-1832.
CrossRef  |  Direct Link  |  

Deepa, V.B., P. Thangaraj and S. Chitra, 2010. Investigating the performance improvement by sampling techniques in EEG data. Intl. J. Comput. Sci. Eng., 2: 2025-2028.
Direct Link  |  

Inuso, G., F.L. Foresta, N. Mammone and F.C. Morabito, 2007. Wavelet-ICA methodology for efficient artifact removal from Electroencephalographic recordings. Proceedings of the 2007 International Joint Conference on Neural Networks (IJCNN’07), August 12-17, 2007, IEEE, Orlando, Florida, USA., ISBN:978-1-4244-1379-9, pp: 1524-1529.

Kang, D. and L. Zhizeng, 2012. A method of denoising multi-channel EEG signals fast based on PCA and DEBSS algorithm. Proceedings of the 2012 International Conference on Computer Science and Electronics Engineering (ICCSEE’12) Vol. 3, March 23-25, 2012, IEEE, Hangzhou, China, ISBN:978-1-4673-0689-8, pp: 322-326.

Karhunen, J. and J. Joutsensalo, 1995. Generalizations of principal component analysis, optimization problems and neural networks. Neural Netw., 8: 549-562.
CrossRef  |  Direct Link  |  

Lotte, F., M. Congedo, A. Lecuyer, F. Lamarche and B. Arnaldi, 2007. A review of classification algorithms for EEG-based brain-computer interfaces. J. Neural Eng., 4: R1-R13.
CrossRef  |  PubMed  |  

Mallat, S.G., 1989. A theory for multiresolution signal decomposition: The wavelet representation. IEEE Trans. Pattern Anal. Mach. Intell., 11: 674-693.
CrossRef  |  Direct Link  |  

Park, H.J., D.U. Jeong and K.S. Park, 2002. Automated detection and elimination of periodic ECG artifacts in EEG using the energy interval histogram method. IEEE. Trans. Biomed. Eng., 49: 1526-1533.
CrossRef  |  PubMed  |  Direct Link  |  

Rioul, O. and M. Vetterli, 1991. Wavelets and signal processing. IEEE. Signal Process. Mag., 8: 14-38.
Direct Link  |  

Tseng, S.Y., R.C. Chen, F.C. Chong and T.S. Kuo, 1995. Evaluation of parametric methods in EEG signal analysis. Med. Eng. Phys., 17: 71-78.
CrossRef  |  Direct Link  |  

Vigario, R., V. Jousmaki, M. Hamalainen, R. Hari and E. Oja, 1998. Independent Component Analysis for Identification of Artifacts in Magnetoencephalographic Recordings. In: Advances in Neural Information Processing Systems, Jordan, M.I., M.J. Kearns and S.A. Solla (Eds.). MIT Press, Cambridge, Massachusetts, USA., pp: 229-235.

Walters-Williams, J. and Y. Li, 2011. A new approach to denoising EEG signals-merger of translation invariant wavelet and ICA. Int. J. Biometrics Bioinf., 5: 130-148.
Direct Link  |  

Yu, L., 2009. EEG de-noising based on wavelet transformation. Proceedings of the 3rd International Conference on Bioinformatics and Biomedical Engineering (ICBBE), June 11-13, 2009, IEEE, Beijing, China, ISBN:978-1-4244-2901-1, pp: 1-4.

Design and power by Medwell Web Development Team. © Medwell Publishing 2024 All Rights Reserved