DWT-BSS: Blind Source Separation applied to EEG signals by extracting wavelet transform’s approximation coefficients

The Electroencephalogram (EEG) signal is widely contaminated by a physiological artifact, such as muscle activity, heart rhythm, and eye movement. The researcher has proposed a number of methods to clean the EEG signal. A group of these methods is called Blind Source Separation (BSS). In this paper, we suggest an approach that combines the BSS methods and the Discrete Wavelet Transform (DWT) algorithm, in order to evaluate the BSS methods after applying them to the approximation coefficients extracted using the DWT. The aim of this work is to identify which BSS algorithms, using which family of wavelet and at which decomposition level, would provide excellent performance. We used the Spearman Correlation Coefficient (SCC) to rate our methods. The technique that performs the best, as evaluated by the SCC between the generated component and the approximation coefficient obtained from the Horizontal EOG results, is AMICA, which obtains a value of 0.81 for levels 2 while using the wavelet symlet at scales 7 and 11. With a value of 0.70 and the use of the wavelet Daubechies at scale 9 and Coiflets at scales 2 and 5 for level 1, AMICA also has the best SCC value calculated between the separated component and the approximation coefficient recovered from the Vertical EOG. While employing the wavelet symlets at scales 5, 7, 8, and 11. for level 2, and level 3 when using the wavelet symlets at scales 1 and 2.