Based On The Pca, Ica Eeg Artifact Elimination

The analysis and processing of EEG signal are very important, not only in clinic diagnosis and treatment of some brain diseases, but also in the life science research field.As ERP is contaminated with EEG, Various recorded biomedical signals practically are mixture of different independent source signals , artifacts and noises, such as EOG,ECG,EMG and other noises. It is very important for us to extract the meaningful signals from such a mixture.In traditional method of signal analysis, Principal Component Analysis-PCA decompose recordings implements into mutual orthogonal signals. It is based on two order cumulate. Independent Component Analysis-ICA is a high order cumulate signal analysis method, and it can suppress Gauss noise and Colored noise, and can separate independent None Gauss signal . ICA has an important value in the biomedical signal processing and is worthy of being completely studied.In this paper, we analyse ICA theory and algorithm, and use this method to remove EOG artifacts from EEG recordings. The experiment results show that it is a promising method. We also study PCA method, and found its computational efficiency in removing EOG artifacts from EEG.The innovated works we have finished are as following:Present the methods using PCA and ICA to remove the EOG artifacts in EEG signals automatically, and resume the original signal without the saw tooth fluctuation. 2,The software can fulfill the function of reading and writing the data of NEUR SCAN' files such as CNT and AVG, and translating between the CNT,AVG and DAT. These are the basis of further research in EEG signal processing system software.