TY - JOUR
T1 - Enhanced μ rhythm extraction using blind source separation and wavelet transform
AU - Ng, Siew Cheok
AU - Raveendran, Paramesran
N1 - Funding Information:
Manuscript received November 24, 2008; revised February 10, 2009 and April 18, 2009. First published May 19, 2009; current version published July 15, 2009. This work was supported by the University of Malaya under Research Grant SF037/2007A. Asterisk indicates corresponding author. ∗S.-C. Ng is with the Department of Biomedical Engineering, University of Malaya, 50603 Kuala Lumpur, Malaysia (e-mail: [email protected]). P. Raveendran is with the Department of Electrical Engineering, University of Malaya, 50603 Kuala Lumpur, Malaysia (e-mail: [email protected]). Color versions of one or more of the figures in this paper are available online at http://ieeexplore.ieee.org. Digital Object Identifier 10.1109/TBME.2009.2021987
PY - 2009/8
Y1 - 2009/8
N2 - The μ rhythm is an electroencephalogram (EEG) signal located at the central region of the brain that is frequently used for studies concerning motor activity. Quite often, the EEG data are contaminated with artifacts and the application of blind source separation (BSS) alone is insufficient to extract the μ rhythm component. We present a new two-stage approach to extract the μrhythm component. The first stage uses second-order blind identification (SOBI) with stationary wavelet transform (SWT) to automatically remove the artifacts. In the second stage, SOBI is applied again to find the μ rhythm component. Our method is first compared with independent component analysis with discrete wavelet transform (ICA-DWT) as well as SOBI-DWT, ICA-SWT, and regression method for artifact removal using simulated EEG data. The results showed that the regression method is more effective in removing electrooculogram (EOG) artifacts, while SOBI-SWT is more effective in removing electromyogram (EMG) artifacts as compared to the other artifact removal methods. Then, all the methods are compared with the direct application of SOBI in extracting μ rhythm components on simulated and actual EEG data from ten subjects. The results showed that the proposed method of SOBI-SWT artifact removal enhances the extraction of the μrhythm component.
AB - The μ rhythm is an electroencephalogram (EEG) signal located at the central region of the brain that is frequently used for studies concerning motor activity. Quite often, the EEG data are contaminated with artifacts and the application of blind source separation (BSS) alone is insufficient to extract the μ rhythm component. We present a new two-stage approach to extract the μrhythm component. The first stage uses second-order blind identification (SOBI) with stationary wavelet transform (SWT) to automatically remove the artifacts. In the second stage, SOBI is applied again to find the μ rhythm component. Our method is first compared with independent component analysis with discrete wavelet transform (ICA-DWT) as well as SOBI-DWT, ICA-SWT, and regression method for artifact removal using simulated EEG data. The results showed that the regression method is more effective in removing electrooculogram (EOG) artifacts, while SOBI-SWT is more effective in removing electromyogram (EMG) artifacts as compared to the other artifact removal methods. Then, all the methods are compared with the direct application of SOBI in extracting μ rhythm components on simulated and actual EEG data from ten subjects. The results showed that the proposed method of SOBI-SWT artifact removal enhances the extraction of the μrhythm component.
KW - μ rhythm
KW - Artifact removal
KW - Blind source separation (BSS)
KW - Second-order blind identification with stationary wavelet transform (SOBI-SWT)
UR - https://www.scopus.com/pages/publications/70349568700
U2 - 10.1109/TBME.2009.2021987
DO - 10.1109/TBME.2009.2021987
M3 - Article
C2 - 19457744
AN - SCOPUS:70349568700
SN - 0018-9294
VL - 56
SP - 2024
EP - 2034
JO - IEEE Transactions on Biomedical Engineering
JF - IEEE Transactions on Biomedical Engineering
IS - 8
ER -