Removal of EMG artifacts from single channel EEG signal using singular spectrum analysis

Abstract

The electroencephalogram (EEG) signals often contaminated by muscle or electromyogram (EMG) artifacts. The presence of these artifacts obscure the desired information in the EEG signal. In this paper, we proposed an efficient subspace based technique named singular spectrum analysis (SSA), to remove the EMG artifacts from the single channel EEG signals. For this we proposed a new grouping technique to extract efficiently the desired component from the contaminated EEG signal by setting a threshold. Firstly, single channel signal is mapped into multichannel signal or data, called embedding. Next, the orthogonal eigenvectors are estimated from the covariance matrix of the multichannel data by singular value decomposition (SVD). Since the local variations of eigenvectors corresponding to the EEG signals are low compare with the EMG signals, we set an arbitrary threshold (0.275) to find these eigenvectors, which are used to create the subspace corresponding to the EEG signals. After identifying the subspace, the EEG signals are extracted by simply projecting the multichannel data onto this subspace followed by reverse process of embedding step. Finally, the proposed method is applied on synthetic noisy sinusoidal signals and EEG signals contaminated by the EMG artifacts. The results shows that the proposed method can efficiently removes the EMG artifacts without altering the desired components.