Advance Approach for Effective EEG Artefacts Removal

Abstract

Electrical impulses generated by nerve firings in the brain diffuse through the head and can be measured by electrodes placed on the scalp and is termed as electroencephalogram (EEG). The artefacts, such as eye blinks etc., in EEG recordings obscures the underlying processes and makes analysis difficult. Large amounts of data must often be discarded because of contamination by artefacts. To overcome this difficulty, signal separation techniques are used to separate artefacts from the EEG data of interest. Some artefacts, such as eye blinks, produce voltage changes of much higher amplitude than the endogenous brain activity. EEG data may be contaminated at many points during the recording and transmission process. Most of the artefacts are biologically generated by sources external to the brain. Improving technology can decrease externally generated artefacts, such as line noise, but biological artefacts signals must be removed after the recoding process. This paper proposes a new technique for removing the artefacts from the EEG signal which uses kurtosis based on difference of Gaussian and Super-Gaussian signal and Spatially-Constrained ICA (SCICA) and Daubechies wavelet techniques. Threshold plays an important role in separating the artefacts from the non-artefact EEG. Otsu’s Threshold is been adopted as the thresholding method in this paper.