Separation of EEG signals by using Independent Component Analysis

Independent Component Analysis (ICA) is a statistical method used for separating nongaussian independent components of a mixture signal. In this study, by separating the signal into its possible independent components, the simplification and comprehension of analysis of EEG signals was aimed. Through such an analysis it was thought that early diagnosis of some neurological disease such as epilepsy, parkinson´s disease, sleep disorders as well as information regarding the location and size of problematic zone may become possible.