Comparative analysis of wavelet based approaches for reliable removal of ocular artifacts from single channel EEG

Abstract

For biomedical and scientific fields, Electroencephalography (EEG) has turned out to be an important tool to understand, study, and utilize brain functionalities. To fully utilize EEG signals in real-life closed-loop applications, artifacts such as ocular must be removed. Wavelet transform is one of the powerful methods to remove ocular artifacts from single channel EEG devices. In this study, both stationary and discrete wavelet transforms (SWT and DWT, respectively) have been compared with various wavelet basis functions, such as sym3, haar, coif3, and bior4.4 using either universal threshold (UT) or statistical threshold (ST). Different combinations of wavelet transform techniques, mother wavelets, and thresholds are compared to identify an optimum combination for ocular artifact removal. Performance metrics like Correlation Coefficient (CC), Normalized Mean Square Error (NMSE), Time Frequency Analysis, and execution time have been calculated for measuring the effectiveness of each combination. According to CC, DWT+UT combination turned out to be a good option for the ocular artifact removal. However, according to NMSE and time frequency analysis, SWT+ST has generated better performance in keeping neural segments of EEG unaffected. According to the measurement of execution times, DWT+ST is faster compared to other combinations. The study shows that wavelet transform is suitable in artifact removal from single channel EEG data to implement in ambulatory real-time EEG systems.