Improved Algorithm for Brain Signal Analysis

Abstract

Blind Source Separation (BSS) is an approach to extract the meaningful data from the non Gaussian independent element of the combined sources. The count and the mixing of pattern from the different sources are not known and hence the name ‘blind’. Joint blind source separation (JBSS) algorithm is beneficial to get common sources at a time exist across multiple dataset like ElectroEncephaloGram (EEG). In this research work, extract of the signal from expected independent elements using an effective algorithm is presented. It is compared with several other BSS algorithms like STFT, ICA, EEMD and IVA. This analysis also helps to early diagnosis of neurological diseases such as brain hypoxia, epilepsy, sleep disorders, and Parkinson’s disease etc. The observational results have higher SNR and Average Correlation Coefficient (ACC) values for the proposed algorithms compared to other BSS techniques.