EEG Artifact Removal Using Canonical Correlation Analysis and EMD-DFA based Hybrid Denoising Approach
Satyender, Sanjeev Kumar Dhull, Krishan Kant Singh
Abstract
EEG signal recordings are often contaminated by Ocular and Muscular artifacts. Such artifacts obscure the signal's important information and complicate the diagnosis. Thus, before utilising the recorded EEG signal for any kind of diagnosis, the signal must be pre-processed to eliminate the artifacts that have contaminated the signal. In this paper a hybrid approach for denoising the EEG signal is proposed. The approach is based on Canonical Correlation Analysis (CCA) as a Blind Source Separation (BSS) technique for elimination of muscular artifacts. In the next step, DFA thresholded EMD process is utilized for the rejection of ocular artifacts. The performance of the proposed hybrid approach is compared with existing techniques on the basis of different performance metrics.