Automatic electrooculogram separation method for single channel electroencephalogram signals

The traditional ElectroOculoGram (EOG) correction methods usually use the correlation information of multi-channel ElectroEncephaloGram (EEG), and are difficult to apply to portable Brain-Computer Interface (BCI) in single channel. An automatic EOG separation method is proposed based on the long term difference amplitude envelope and the wavelet transformation in the paper. Firstly, the accurate EOG beginning and ending points are detected on the long term difference amplitude envelope of the original EEG through a dual thresholds method. Secondly, the sym5 wavelet is applied to decompose the original EEG signal, and the Birgé_Massart strategy is introduced to adaptively determine the thresholds of wavelet coefficients. Finally, the EOG is accurately reconstructed and separated from the EEG in this channel. Compared with the popular regression analysis of averaging artifact and the Independent Component Analysis (ICA) based methods, the proposed method is proved to achieve a better correlation measure between the separated EOG and the original EOG, a higher signal-to-noise ratio of the corrected EEG, and a good real-time operating speed for most BCI application requirements.