Adaptive Blind Signal and Image Processing: Learning Algorithms and Applications
Adaptive Blind Signal and Image Processing: Learning Algorithms and Applications
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ElectroEncephaloGram (EEG) records contains data regarding abnormalities or responses to some stimuli in the human brain. Such rhythms are examined by physicians for the purpose of detecting the neural disorders and cerebral pathologies. Because to the occurrences of artifacts, it is complicated to examine the EEG, for they introduce spikes which can be confused with neurological rhythms. Therefore, noise and undesirable signals must be removed from the EEG to guarantee a correct examination and diagnosis. This paper presents a novel technique for removing the artifacts from the ElectroEncephaloGram (EEG) signals. This paper uses Spatially-Constrained Independent Component Analysis (SCICA) to separate the exactly the artificate Independent Components (ICs) from the initial EEG signal. Then, Wavelet Denoising is applied to eliminate the brain activity from extracted artifacts, and finally project back the artifacts to be subtracted from EEG signals to get clean EEG data. This paper uses Daubechies wavelet transform for wavelet denoising. Here, thresholding plays an important role in deciding the artifacts. Therefore, a better thresholding technique called Otsu', thresholding is applied. Experimental result shows that the proposed technique results in better removal of artifacts.