B. S. Raghavendra and D. Narayana Dutt
Wavelet Enhanced CCA for Minimization of Ocular and Muscle Artifacts in EEG
419 - 424
2011
5
9
International Journal of Biomedical and Biological Engineering
https://publications.waset.org/pdf/10431
https://publications.waset.org/vol/57
World Academy of Science, Engineering and Technology
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 (BSSCCA) 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 BSSCCA. 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 BSSCCA 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.
Open Science Index 57, 2011