Study Of Ocular Artifacts Removal Based On Wavelet Transform And Kalman Filter

Electroencephalogram(EEG) is a non-invasive method to record electrical activity of brain and it has been used extensively in research subjects of brain function due to its high time resolution. The EEG is so weak that it is always interfered with other noises, especially the ocular artifacts, which makes the subsequent analysis of EEG hard. Hence, how to eliminating ocular artifacts from EEG becomes the current research hotspot. Now, although there are many methods of ocular artifacts removal, lots of them are limited by various conditions, for example, some methods need the synchronous electrooculorgram when they are used to eliminate ocular artifacts. Since the channels’ number of the portable EEG-recording device is finite, so these methods are not used directly.In order to solve the above-mentioned problem, we have proposed a new model combined by wavelet transform and Kalman filter for automatically eliminating ocular artifacts. Since the artifacts appear randomly in the EEG, so this method detect artifacts areas and just deal with them to avoid impacting on EEG out of these areas. Then, wavelet is employed to decompose artifacts areas into 4 layers and the approximation coefficients is used to construct coarse ocular artifacts due to the electrooculogram mainly concentrating in the low frequency band. Because the ocular artifacts contains low-frequency EEG, Kalman filter is applied to process the constructed ocular artifacts to estimate the pure artifacts, which are subtracted from the raw EEG to acquire clean EEG at last.We compare the proposed model with ICA-wavelet model and adaptive-filter-wavelet model using simulated data to test effectiveness of the new method. In time domain, the Mean Squared Error(MSE) of the new method is 0.0017, while the MSE of the other two models are 0.0468 and 0.0052, respectively. In frequency domain, the Mean Absolutely Error(MAE) using the new method achieved an average of 0.0052, the average of MAE of the other two model are 0.0218 and 0.0115, respectively. Obviously, both MSE and average of MAE of the new model are significantly lower those of the other two models, which indicate our proposed model achieved better result of ocular artifacts removal. To further test practicability of the new model, we use it to eliminate ocular artifacts in EEG which are recorded by BrainCap and three-channel EEG collector, respectively, it turns out that the proposed model can remove artifacts effectively. Taken together, the new model can eliminate ocular artifacts in EEG automatically, and it is recommended for portable applications.