Complex wavelet transform driven de-noising method for an EOG signals

Human activities are recognized from the Electrooculogram (EOG) signal generated from the movement of eye. Hence early, accurate preprocessing of EOG signals is important. In recent years, this became an active area of research. The EOG signal captured using acquisition device is corrupted with the noise and device intrinsic, thus pre processing (noise reduction) is first and foremost step in any further analysis & activity recognition. In this paper a novel method of De-noising EOG signals using Dual Tree complex wavelet transform (DT-CWT) is proposed. The Denoising results obtained are compared with conventional wavelet (DWT) de-noising method. To demonstrate the efficacy of the proposed method, SNR calculations and the statistical analysis are evaluated. The proposed method is best suitable for real time EOG based applications like human-machine communication devices for disabled persons, eye movement analysis and gaming applications.