An Advanced EEG Motion Artifacts Eradication Algorithm

Abstract 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.