EEG Remoral Based On Independent Component Analysis

The purpose of EEG signal processing is to extract the hidden or weak paterns that probably have some physiological and/or psycho-physiological significance from EEG signals in sophisticated noise background and then to apply them to the research on clinical medicine or cognitive science.Independent component analysis(ICA) is a new signal separating technique with BSS developing in the field of statistical signal processing.ICA is featured by decomposing the observed record mutually independent components without other transcendental knowledge except for mutually independent Source signals.In this thesis the theory of independent component analysis as well as its application in EEG signal processing are studied.1 Introduce the theoretic base of ICA and correlative knowledge,which can separate the multiple fixed EEG based on statistical character of multiple elements.2 Expatiate the algorithm of ICA by the numbers,analyse the advantage, disadvantage and capability of each one respectively.3 Classify the noise in the EEG,discuss how to make use of fixed ICA to separate and remove the artifacts in the EEG signals.Compared with other algorithms,the experiments indicate that ICA has a prominent advantage.The problem has introduced fixed ICA and make programs to realize it with Matlab.meanwhile, study how to make use of fixed ICA to effectively detect, separate and remove a wide variety of artifacts from EEG recordings,such as ECG, EOG experiments are respectively done by ICA to remove the artifacts in the EEG signals.The experimental results show the ICA can effectively remove the artifacts,almost do not lose the useful information in EEG signals.