Study On Independent Component Analysis And Its Application In Event-related Potential

Event-Related Potential(ERP) reflects neural activity of the brain of cognitive process, and is a useful tool of studying human advanced cognitive function, so it has a widely application in clinical medicine and cognitive science. But ERP signal is often heavily contaminated by sophisticated background noise such as spontaneous Electroencephalogram(EEG), Electrooculogram(EOG), Electromyogram(EMG), Electrocardiogram(ECG) etc., and is very weak compared with the background noise in which it is embedded. For this reason, it is necessary to extract ERP signal from the strong background noise.Independent Component Analysis(ICA) is a novel multi-dimensional signal processing method developed recently and used to analyze the mutually independent nongaussian signals. When some certain assumptions are satisfied, ICA can effectively separate the underlying independent sources only from the synchronous multichannel observed mixtures.This thesis studies the theories and algorithms of ICA and explores its applications in ERP denoising and P3 subcomponents extraction. The innovated works we have finished are as follows:1) Studying the two typical algorithms of ICA based on the nongaussian maximum principle and information maximum principle .2) Studying the ICA-based method to remove EEG artifacts in order to efficiently extract ERP signal from the strong background noise. In the experiments we analyze the EEG independent components separated by ICA algorithm, by means of the time analysis, frequency analysis and scalp topography analysis, to find the noise components. The manifold analytical method enhances the reliability of the components analysis. Moreover, the traditional ERP denoising by ICA is commonly confined to remove non-neural artifacts such as EOG and EMG, but in this thesis, we remove not only EOG and EMG but also the time-lock spontaneous EEG such as a rhythms and μ. rhythms in order to obtain better ERP denoising effect. The experimental results show, by the new ERP denoising method, ERP signal is efficiently extracted from the strong background noise.3) Studying P3 subcomponents extraction based on ICA. According to the properties that ICA can separate the underlying independent sources from the observed mixtures, P3 subcomponents are successfully extracted from the P3 complex. Study of those subcomponents can help people to further explore human advanced cognitive function, and make the study of P3 come up to higher level.