Independent Component Analysis And Its Application In Eeg Noise Separation

Brain signals are bioelectrical signals of the human brain, which are classified into two categories: electroencephalograph (EEG) and evoked potential (EP). They have a number of clinical applications including the diagnosis of a variety of neurological disorders, physiological analysis and critical care and operating room monitoring. So, the extraction of EEG and EP is an important project in neuroscience.Independent component analysis (ICA) is a technique which extracts statistically independent components from a set of measured signals. It is an analytical method based on higher-order statistical characters of signals, and is widely used in signal processing. Recently, ICA develops very quickly, and more and more researchers are devoted into studying new algorithms and applying them to noise reduction of EEG and EP.Based on ICA algorithm with reference signals, a method removing blinking artifacts is proposed in this thesis. The main idea of it is: first select one channel of EEG with obvious blinking artifacts, and obtain reference signals from it. Then extract the blinking artifact first with ICA algorithm, and at last get pure EEG signals. The idea and steps of the ICA algorithm with reference signals are thoroughly discussed, and the results of processing real signals are also proposed in the thesis. The advantages of the new algorithm are: it has all the advantages of fastICA algorithm, and it can separate the artifact in one independent component; it also makes the reconstruction of EEG easier than other algorithms.Both kinds of ICA algorithms have advantages and disadvantages. Infomax algorithm can learn on line, but it converges slowly. FastICA algorithm converges very quickly, but it cannot learn on line. It also needs prewhitening and large size of data, and it is sensitive to the initial value of the demixing matrix. This thesis proposes a new algorithm that combines two existent algorithms, the improved infomax algorithm and the fastICA algorithm. Utilizing the initial weights obtained by the improved infomax algorithm, we can not only reduce the length of data which fastICA algorithm needs, but also enhance the convergence stability of fastICA algorithm. The effectiveness of the algorithm is verified by computer simulations. This new algorithm is applied in noise reduction of EP, and realizes fast estimation of EP.