Estimation of Number of Levels of Scaling the Principal Components in Denoising EEG Signals
B.Krishna KumarDepartment of ECE, Methodist College of Engineering and Technology, Hyderabad-500001, Telangana State, India.
Corresponding Author E-mail: saisantu2004@yahoo.co.in
Abstract:
Electroencephalogram (EEG) is basically a standard method for investigating the brain’s electrical action in diverse psychological and pathological states. Investigation of Electroencephalogram (EEG) signal is a tough task due to the occurrence of different artifacts such as Ocular Artifacts (OA) and Electromyogram. By and large EEG signals falls in the range of DC to 60 Hz and amplitude of 1-5 µv. Ocular artifacts do have the similar statistical properties of EEG signals, often interfere with EEG signal, thereby making the analysis of EEG signals more complex[1]. In this research paper, Principal Component Analysis is employed in denoising the EEG signals. This paper explains up to what level the scaling of principal components have to be done. This paper explains the number of levels of scaling the principal components to get the high quality EEG signal. The work has been carried out on different data sets and later estimated the SNR.
Keywords: Denoising; Multi Scale PCA (MSPCA); Principal Components; PCA; SNR Back to TOC