EEG Signal Separation using Improved EEMD - Fast IVA Algorithm Sugumar D.*,**, Dr. Vanathi P. T.*** *Research Scholar, Anna University, India **Signal Processing Lab, Department of Electrical Technology, Karunya University, Coimbatore, Tamilnadu, India ***Department of Electronics and Communication Engineering, PSG College of Technology, Coimbatore, Tamilnadu, India Online published on 23 March, 2017. Abstract Blind Source Separation (BSS) is one of the promising approaches to retrieve the information from the non Gaussian independent components of the mixtures. The number of sources and the mixing method of the sources are unknown and hence the term “blind”. Joint blind source separation (JBSS) is a means to extract common sources simultaneously found across multiple datasets, e.g., electroencephalogram (EEG). In this study, by separating the signal into its possible independent components, the simplification and comprehension of analysis of EEG signals was aimed. In this paper, Improved EEMD-Fast IVA algorithm is proposed to separate into its possible common independent vector found across multiple channels. The investigation on simulated EEG signals demonstrates the better result of the proposed algorithm. Moreover, a comparative study of Improved EEMD-Fast IVA with the reported BSS methods like STFT-ICA, Wavelet-ICA and IVA is presented. Through such an analysis, it was thought that early diagnosis of any neurological disease such as epilepsy, Parkinson's disease, sleep disorders as well as information regarding the location and size of problematic zone become possible. Top Keywords Blind Source Separation (BSS), Electroencephalogram (EEG), Fast Independent Vector Analysis (Fast IVA), Improved EEMD. Top |