SVD based technique for noise reduction in electroencephalographic signals

Abstract

Noise reduction is a subject of importance in many applications of signal processing such as communication, instrumentation, etc. In a recent paper (Callaerts et al., 1988), singular value decomposition (SVD) has been shown to be a very robust and numerically reliable tool for extracting the desired signals from noisy data. Along similar lines, in this paper, we explore the application of SVD for extracting the actual electroencephalographic (EEG) signals from electrooculogram (EOG) contaminated EEG signals. The key point which made the SVD technique suitable for this EEG enhancement problem is the orthogonality between the EEG and EOG signals. The problem has been set up in such a way that the SVD technique could be applied directly to the recorded signals. Studies were performed on simulated as well as real signals. Signal to noise ratio (SNR) and linear prediction (LP) spectra (along with time plots) are used as measures for comparing the performance of the proposed algorithm for the cases of one EOG channel and two EOG channels. Studies on simulated as well as actual signals show that the technique is very effective in minimizing EOG artefacts from noisy EEG signals.