EEG signal enhancement using cascaded S-Golay filter

Abstract

Electroencephalogram (EEG) is the most popular signal used for diagnosis of brain disorders. A good quality EEG signal provides the proper interpretation and identification of physiological and pathological phenomena. However, these recordings are often corrupted by different kinds of noise. As Savitzky Golay smoothing filter (SGSF) preserves the peaks and minimize the signal distortion, its use in cascade may further enhance this capability. Therefore in the present work cascaded SGSF (CSGSF) is proposed to filter the noisy EEG signals. The CSGSF combines two successive Savitzky Golay filters. For comparative analysis, other cascaded arrangements like cascaded moving average filter (CMAF), MAF-SGSF, SGSF-Binomial and single stage SGSF are also designed. These filters are tested on artificial EEG signals added with white Gaussian noise and non Gaussian noise. These filters are also tested on real time EEG signals. The filtered signals are assessed through signal to noise ratio (SNR), signal to signal plus noise ratio (SSNR), SNR improvement (SNRI), mean square error (MSE) and correlation coefficient (COR). It is revealed from the results that CSGSF outperforms the other designed filters in case of artificial and real time EEG signals.