Motion artifact removal from single channel electroencephalogram signals using singular spectrum analysis

In ambulatory electroencephalogram (EEG) health care systems, recorded EEG signals often contaminated by motion artifacts. In this paper, we proposed a singular spectrum analysis (SSA) technique with new grouping criteria to remove the motion artifact from a single channel EEG signal. In order to remove the motion artifact from a single channel EEG signal, we considered the eigenvectors (basis vectors) corresponding to motion artifact are grouped or identified based on their local mobility, which is a signal complexity measure. However, as the local mobility of eigenvectors associated to the motion artifact are small, a threshold of 0.1 is set to identify them. The motion artifact signal is estimated using the identified eigenvectors and subtracted from the contaminated EEG signal to obtain the corrected EEG signal. The proposed technique is tested on 21 single channel real EEG signals contaminated by motion artifact and compared the results with the existing combined ensemble empirical mode decomposition and canonical correlation analysis (EEMD-CCA) technique. The simulation results show that the proposed modified SSA enjoys an improvement in the signal to noise ratio and the percentage reduction in artifact. Moreover, as the ambulatory EEG systems are battery operated, use of high computational signal processing techniques will reduce the battery lifetime. Hence, low computational signal processing techniques are greatly demanded in such applications. Thus, we have also evaluated the computational complexity of the proposed technique and compared with EEMD-CCA. We found that the proposed modified SSA technique significantly reduces the computational complexity and thereby lower power consumption compared to the EEMD-CCA.