Adaptive wavelet technique for EEG de-noising

Recording the electrical current of the cortex is called electroencephalography (EEG). EEG signals can be affected by high and low frequency noises which are caused due to muscular activity (EMG), Power line interference, eye blinks and etc. In this paper, we introduce an adaptive wavelet method for elimination of high frequency noises from EEG. The desired noise is extracted from the raw signal by wavelet approach. Afterwards, the extracted noise is applied as the input for adaptive filter. The effectiveness of the proposed method is compared to the forth order Butterworth high pass filtering with cut-off frequency at 30 Hz and EMD approach. In order to evaluate the performance of the methods, signal to noise rate (SNR) and correlation coefficient between pure and filtered signals are calculated. The obtained results suggest the proposed method can reduce EEG noise more efficiently than tile other methods.