Abstract
The motion generated at the capturing time of electro-encephalography (EEG) signal leads to the artifacts, which may reduce the quality of obtained information. Existing artifact removal methods use canonical correlation analysis (CCA) for removing artifacts along with ensemble empirical mode decomposition (EEMD) and wavelet transform (WT). A new approach is proposed to further analyse and improve the filtering performance and reduce the filter computation time under highly noisy environment. This new approach of CCA is based on Gaussian elimination method which is used for calculating the correlation coefficients using backslash operation and is designed for EEG signal motion artifact removal. Gaussian elimination is used for solving linear equation to calculate Eigen values which reduces the computation cost of the CCA method. This novel proposed method is tested against currently available artifact removal techniques using EEMD-CCA and wavelet transform. The performance is tested on synthetic and real EEG signal data. The proposed artifact removal technique is evaluated using efficiency matrices such as del signal to noise ratio (DSNR), lambda (λ), root mean square error (RMSE), elapsed time, and ROC parameters. The results indicate suitablity of the proposed algorithm for use as a supplement to algorithms currently in use.
Highlights
EEG signal is widely used for exploring the human brain activity and is preferred over other physiological signals because they can be used to detect directly brain electrical activity changes over spans of millisecond time, whereas functional magnetic resonance imaging has time resolutions in seconds or minutes
This three-stage cascaded approach removes the artifacts effectively with increased computational cost. This computational complexity is reduced in this research paper by developing an existing correlation-based algorithm with Gaussian elimination (GE) and inserted at the cascade of ensemble empirical mode decomposition (EEMD)-stationary wavelet transform (SWT), leading to EEMD-Gaussian Elimination Canonical Correlation Analysis (GECCA)-SWT which is the combination of EEMD and an improved approach GECCA (Gaussian elimination canonical correlation analysis) with SWT
The measurement and processing of EEG signal result in the probability of signal contamination prominently through motion artifacts which can obstruct the important features and information quality existing in the original EEG signal
Summary
EEG signal is widely used for exploring the human brain activity and is preferred over other physiological signals because they can be used to detect directly brain electrical activity changes over spans of millisecond time, whereas functional magnetic resonance imaging (fMRI) has time resolutions in seconds or minutes. The combination of EEMD, CCA, and SWT approaches has been applied for effective suppression of the motion artifact from EEG signal. This three-stage cascaded approach removes the artifacts effectively with increased computational cost. This computational complexity is reduced in this research paper by developing an existing correlation-based algorithm with Gaussian elimination (GE) and inserted at the cascade of EEMD-SWT, leading to EEMD-GECCA-SWT which is the combination of EEMD and an improved approach GECCA (Gaussian elimination canonical correlation analysis) with SWT.
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