Abstract
Eye movements during electroencephalogram (EEG) recordings are the major sources of artifacts. These artifacts tend to mask the EEG signals. So, to obtain good quality EEG signals, these artifacts must be removed without deteriorating the underlying EEG activity. In this paper, a new algorithm is proposed that combines canonical correlation analysis (CCA) and noise adjusted principal component transform (NAPCT) to efficiently remove the electrooculogram (EOG) and blink artifacts in a considerably fast manner. CCA-NAPCT is implemented after the preliminary outlier thresholding of EEG data. CCA is used to estimate the noise covariance matrix while NAPCT is implemented for noise removal. The results of this algorithm on EOG affected BCI competition III dataset IVb and blink contaminated EEG data of four subjects showed the efficacy of the proposed algorithm in effective removal of noise. The algorithm provides an average signal to noise ratio and root mean square error values of 3.616 & 42.456 with artifactual EEG data respectively. Moreover, the average correlation coefficients (0.8839) and mutual information (1.1546) values also verify the efficacy of algorithm more firmly as supported by comparison with the state-of-the-art technique. The proposed algorithm successfully removed the artifactual components with no manual intervention.
Published Version
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