Analysis of EOG signals using wavelet transform for detecting eye blinks

Abstract

Eye ball movements are vital signs in some of the neurological disorders and it can be tracked by acquiring electrooculogram (EOG) signals. EOG is an obtrusive, inexpensive and non-invasive means of recording eye ball movements. The source for EOG signal is cornea-retinal potential (CRP) and is generated due to the movements of eye balls within the conductive environment of the skull. While recording the EOG signal, it will be contaminated by electromyography (EMG) signal. As the EOG is a non stationary signal, the multi resolution analysis using wavelet decomposition offers the best solution to denoise the EOG signal. In this paper, the author proposed a new wavelet based method to detect eye ball moments from signal conditioned EOG. Comparative wavelet analysis is performed by considering different statistical measures. Test results reveal that the Symlet based method provides better efficacy in eliminating noise from EOG signals.