Blind source separation and artefact cancellation for single channel bioelectrical signal

Bioelectrical signal analysis is gaining significant interests from both academics and industries due to its capability for improved diagnosis and therapy of chronic diseases. In practice, different bio-signals, such as EEG, ECG, EOG and EMG, are usually contaminating each other, and the measured signal is the linear combination of them. It is critical to separate them since analysis of one type or several of them separately is of more interest. In the case of multichannel recording, several blind source separation methods are available to extract its original components. However, for single channel scenarios, the problem has yet to be well studied. Therefore in this paper, we explore blind source separation and artefact cancellation for a single channel signal by combining signal decomposition method singular spectrum analysis (SSA) with different blind source separation methods, such as principal component analysis (PCA), maximum noise fraction (MNF), independent component analysis (ICA) and canonical correlation analysis (CCA). We also systematically compare the separation performance by combing different decomposition methods (wavelet transform (WT), ensemble empirical mode decomposition (EEMD) and SSA) with blind source separation methods (PCA, MNF ICA and CCA). The good simulation results have demonstrated the effectiveness and efficiency of the proposed method.