EEG signal co-channel interference suppression based on image dimensionality reduction and permutation entropy

It is well known that electroencephalogram (EEG) signals collected from scalps are highly contaminated by various types of artifacts and background noise. The perturbations induced by artifacts and random noise are particularly difficult to correct because of their high amplitude, wide spectral distribution, and variable topographical distribution. Therefore, de-noising of EEG is a very challenging pre-processing step prior to qualitative or quantitative EEG signal analysis. To address this issue, some de-noising approaches have been proposed for noise suppression. However, most of these methods are only available for multi-electrode EEG signal processing, besides, the co-channel interference are always left unprocessed. Aiming at the obstacles encountered by the conventional approaches in single electrode EEG signal co-channel interference suppression, a method based on time-frequency image dimensionality reduction is proposed in this paper. The innovative idea of the proposed method is that it is applicable for single electrode EEG signal enhancement and the background noise can be suppressed in entire time-frequency space. The proposed method is experimentally validated by a group of real EEG data. The experimental results indicate that the proposed method is effective in EEG single electrode co-channel interference suppression.