Gaussian Elimination-Based Novel Canonical Correlation Analysis Method for EEG Motion Artifact Removal

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.

Differentiation in Theta and Beta Electrocortical Activity between Visual and Physical Perturbations to Walking and Standing Balance

“…To remove large mechanical artifacts, we used artifact subspace reconstruction ( Mullen et al, 2013 ) with a threshold of 20 SDs, which has been used in a previous mobile EEG study ( Artoni et al, 2017 ). We also performed selective low-pass filtering using ensemble empirical mode decomposition ( Wu and Huang, 2009 ; Al-Subari et al, 2015 ) and canonical correlation analysis ( Hotelling, 1936 ), similar to a technique used by Roy et al (2017) . This specifically targeted large high-frequency activity with low autocorrelation, such as muscle activity and line noise ( Safieddine et al, 2012 ).…”

Cortical Oscillations during Gait: Wouldn’t Walking Be So Automatic?

“…ICA and dipole fitting accurately localized 99% of the independent components in non-neural locations. Some authors also propose removal of specific muscular activity such as neck muscle activity that can affect the EEG signal during walking [22,27], by using either ICA or another blind source separation approach called canonical correlation analysis (CCA) [28]. A more general problem remains with analysis in the gamma band since gamma rhythms are generated by small volumes and are thus difficult to record with scalp EEG [29].…”

Transient visual perturbations boost short-term balance learning in virtual reality by modulating electrocortical activity

“…First, we used artifact subspace reconstruction (Mullen et al 2015) with a standard deviation of 20 to remove large artifacts. Next, we developed a high-frequency denoising method based on two emerging signal processing methods: ensemble empirical mode decomposition (EEMD) (Wu and Huang 2009) and canonical correlation analysis (CCA) (Hotelling 1936), which have been combined before (Roy et al 2017). Our EEMD-CCA method performed selective low-pass filtering, specifically targeting large high-frequency activity with low autocorrelation such as muscle activity and line noise.…”