Purification of Brain Signals Using Various Blind Source Separation Techniques

Abstract

There arelot of challenges when analyzing the brain signals that did not yet have a basic solution, as there are electrical activities between neurons in the brain,related to all activities in the body. This activity can be seen using a non-surgical technique that is called EEG (i.e. Electroencephalography)such as the appearance of artifactsthrough the registration process, which increases the difficulty of analyzing the signals of the brain, so the technique of blind source separation(BSS) has been used to overcome the problem of artifacts and to separate the main sources (Mixed) without making noise aroundthe original sources.. Therefore, the system for rejecting allartifacts based on the algorithms for separating the blind source has been proposed, bymaking a comparison between four separation algorithms and choosing the best ones according to criteria. After passing a datasetsimulated through thosecriteria, the proposed system can remove the artifacts including Electrocardiogram (ECG), Electrooculogram (EOG)as well as apower line noise interference (LN)) and other EEG mixtures. The proposed method's influenceis checked by two performance indexes Interference to signal ratio (ISR) and (SNR) signal to noise ratio.The results indicated that the BEFICA algorithm is the best and most efficient, as it achieved the highest ratio of VSNR among four separation algorithms due to its developmental advantages.