The Model Of Ocular Artifacts Removal Based On DWT And Adaptive Filter For Portable Applications

With the development of brain science application, the removal of all kinds of interference in EEG signals, especially the ocular artifacts removal is an important research. How to develop specific proposals to remove the ocular artifacts and obtain the "cleaned" EEG signals is still a very challenging content. Most time, in the original recorded EEG signals, the amplitude of interference have a large amplitude or even submerged useful EEG signal completely. So the results of noise removal have a direct impact on the further analysis of EEG signals. Currently, although there is a lot research which have made a breakthrough in ocular artifacts removal, some methods lost a part of useful EEG signal while removing the ocular artifacts, and some methods need a reference channel to record the synchronizing ocular artifacts. Those methods cannot meet the requirements of portable environment, maybe there is only one channel EEG signal.In allusion to the characteristics of removing ocular artifacts from EEG signals in a portable environment, a new model to remove ocular artifacts from electroencephalograms (EEGs) is presented. The model is based on discrete wavelet transformation (DWT) and adaptive noise cancellation (ANC). A particularly novel feature of the new model is the use of DWTs to construct an ocular artifact (OA) reference signal, using the three lowest frequency wavelet coefficients of the EEGs. Through processing the simulated and real contaminated EEG signal, this paper analysis and verify the validity of our model, and the model in this paper compared with the independent component analysis (ICA) model and stationary wavelet transforms (SWT) method. The results show that the new model demonstrates an improved performance with respect to the recovery of true EEG signals and also has a better tracking performance. Because the new model requires only single channel sources, it is well suited for use in portable environments where constraints with respect to acceptable wearable sensor attachments usually dictate single channel devices. The model is also applied and evaluated against data recorded within the EUFP7Project-Online Predictive Tools for Intervention in Mental Illness (OPTIMI). Through the above analysis, the proposed model is effective in removing ocular artifacts and meets the requirements of portable systems used for patient monitoring as typified by the OPTIMI project.