Removal of Ocular Atrifacts from Single Channel EEG Signal Using DTCWT with Quantum Inspired Adaptive Threshold

Abstract

While acquiring EEG signal for recording brain activities, we often receive signals from other muscle activities which are added with the brain activity signal thus resulting in a contaminated EEG signal. Muscle activities such as eyeblink (EB) and eye ball movement are referred as Ocular Artifacts (OAs) which highly affect EEG signals. In Brain Computer Interface (BCI) systems, removal of OAs is important for correctly converting the brain thoughts into commands in order to control the external device. Various techniques like Independent component Analysis (ICA), and Principle Component Analysis (PCA) are widely used for the elimination of OAs but these techniques require multi channel EEG signals for processing. In this paper we have proposed the use of dual tree complex wavelet transform (DTCWT) with quantum inspired adaptive wavelet threshold algorithm for the elimination of OAs from single channel EEG signal. We have estimated Relative Root Mean Square Error (RRMSE). Results show better performance in reduction of ocular artifacts when using DTCWT with quantum inspired adaptive threshold. © 2018 IEEE.