A New Hybrid Method for EOG Artifact Rejection from EEG Signal Using CCA and RLS

The purpose of this article is to evaluate the efficiency of Canonical Correlation Analysis- Recursive Least Square (CCA-RLS)hybridmethod in ElectroOcluGram (EOG) artifact removal from ElectroEncephaloGram (EEG) signal and compare it with Independent Component Analysis (ICA), Canonical Correlation Analysis (CCA), Recursive Least Square (RLS)methods and ICA-RLS hybrid method. After decomposition of the noisy signal by CCA, the noisy components aredetected based ontheir kurtosis, and are filtered by RLS. As the result,the enhanced signal is reconstructed by mixing the original noise-free components and filtered components. In order to compare the methods quantitatively, two evaluation criteria, namely Mean Square Error (MSE) and Signal to Noise Ratio (SNR) are used.The MSE and SNR average values were calculated for five subject in four different channels. EEG data are taken from BCI2008. According to the results,the combination of CCA-RLS method has better performance compareto the other methods used in this paper.