Key result
Improved cascaded GECCA-EEMD approach suppresses EEG motion artifacts and boosts computational efficiency versus existing methods.
Why the study?
Patient movement corrupts EEG signals with motion artifacts, diminishing signal quality, and existing artifact elimination approaches require improved efficacy and precision in highly noisy environments.
A novel algorithm combining a median filter, GECCA, and EEMD effectively suppresses motion artifacts in EEG signals, improving signal quality in highly noisy environments.
May facilitate EEG preprocessing in research; leaves open clinical validation before cardiovascular monitoring use.
The electroencephalography (EEG) signal is corrupted with some non-cerebral activities due to patient movement during signal measurement. These non-cerebral activities are termed as artifacts, which may diminish the superiority of acquired EEG signal statistics. The state of the art artifact elimination approaches applied canonical correlation analysis (CCA) for confiscating EEG motion artifacts accompanied by ensemble empirical mode decomposition (EEMD). An improved cascaded approach based on Gaussian elimination CCA (GECCA) and EEMD is applied to suppress EEG artifacts effectively. However, in a highly noisy environment, a novel addition of median filter before the GECCA algorithm is suggested for improving the accuracy of onslaught the EEG signal. The median filter is opted due to its edge preserving nature and speed. This proposed approach is appraised using efficacy grounds for instance Del signal to noise ratio, Lambda (λ), root mean square error and receiver operating characteristic (ROC) parameters and verified contrary to presently obtainable EEG artifacts exclusion methods. The primary concern is to improve the efficacy and precision of the proposed artifact elimination technique. The elapsed time is also calculated to evaluate the computation efficiency. Results show that the proposed algorithm is appropriate to be used as an addition to existing algorithms in use.
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Shukla et al. (2021) studied EEG motion artifacts. Improved cascaded approach based on Gaussian elimination CCA (GECCA) and EEMD with median filter vs. Presently obtainable EEG artifacts exclusion methods was evaluated on Efficacy grounds (Del signal to noise ratio, Lambda, RMSE, ROC parameters, elapsed time). An improved cascaded approach using a median filter, Gaussian elimination CCA, and EEMD effectively suppressed EEG motion artifacts and improved computational efficiency compared to existing methods.
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