A method of removing Ocular Artifacts from EEG using Discrete Wavelet Transform and Kalman Filtering

Abstract

Electroencephalogram (EEG) is a noninvasive method to record electrical activity of brain and it has been used extensively in research of brain function due to its high time resolution. However raw EEG is a mixture of signals, which contains noises such as Ocular Artifact (OA) that is irrelevant to the cognitive function of brain. To remove OAs from EEG, many methods have been proposed, such as Independent Components Analysis (ICA), Discrete Wavelet Transform (DWT), Adaptive Noise Cancellation (ANC) and Wavelet Packet Transform (WPT). In this paper, we present a novel hybrid de-noising method which uses Discrete Wavelet Transform (DWT) and Kalman Filtering to remove OAs in EEG. Firstly, we used this method on simulated data. The Mean Squared Error (MSE) of DWT-Kalman method was 0.0017, significantly lower compared to results using WPT-ICA and DWT-ANC, which were 0.0468 and 0.0052, respectively. Meanwhile, the Mean Absolute Error (MAE) using DWT-Kalman achieved an average of 0.0052, which also performed better than WPT-ICA and DWT-ANC, which were 0.0218 and 0.0115, respectively. Then we applied the proposed approach to the raw data collected by our prototype three-channel EEG collector and 64-channel Braincap from BRAIN PRODUCTS. On both data, our method achieved satisfying results. This method does not rely on any particular electrode or the number of electrodes in certain system, so it is recommended for ubiquitous applications.