Block based Normalized LMS Adaptive Filtering Technique for Denoising EEG Artefacts
Pages : 386-389
The electroencephalogram (EEG) is an important bioelectric signal for studying human brain characteristics as well as detection of abnormalities like epilepsy. However, the EEG recorded often contains strong artefacts produced by many sources like Powerline Interference (PLI) and Electrocardiogram (ECG). Existing regression–based methods for removing artefacts require various procedures for pre-processing and calibration that are inconvenient and time consuming. This paper introduces Block based Normalized LMS (BBNLMS) Adaptive algorithm and its sign variant algorithms for removing the PLI and ECG artefacts from the contaminated EEG signal. The simulation results show that the performance of the BBNLMS algorithm is superior to that of conventional LMS algorithm in terms of Signal to noise ratio.
Keywords: Adaptive filters, Block based Normalized LMS, PLI, ECG, Signal to noise Ratio
Article published in International Journal of Current Engineering and Technology, Vol.7, No.2 (April-2017)