Noise Cancellation of Ocular and Muscular Artifacts from EEG Signals Based on Adaptive Filtering
G. Geetha, S. N. Geethalakshmi · International Journal of Computer and Electrical Engineering · 2012
The Electroencephalogram (EEG) is a useful tool for clinical diagnosis. Artifacts in EEG records are caused by various factors like line interference, Electro-oculogram (EOG), Electro-Cardiogram ECG, Electromyogram EMG. These noise sources increase the difficulty in analyzing the EEG and for obtaining proper clinical information. Regression based methods for removing various artifacts require various procedures for preprocessing and calibration that are inconvenient and time consuming. Independent Component Analysis (ICA) (1) method requires off-line processing of data collected from a sufficiently larger number of channels and its success depends on correct identification of noise components. When application requires real-time removal of artifacts or when calibration trials cannot be conducted owing to various constraints, this method becomes unsuitable. This paper describes a method of removing EOG and EMG artifacts from EEG based on adaptive filtering using RLS algorithm.