EOG artifact removal using a wavelet neural network

Authors:
Hoang-Anh T. Nguyen;John Musson;Feng Li;Wei Wang;Guangfan Zhang;Roger Xu;Carl Richey;Tom Schnell;Frederic D. Mckenzie;Jiang Li
Affiliations:
Department of Modeling, Simulation and Visualization Engineering, Norfolk, VA 23529, USA;Department of Electrical and Computer Engineering, Old Dominion University, Norfolk, VA 23529, USA;Department of Electrical and Computer Engineering, Old Dominion University, Norfolk, VA 23529, USA;Signal Processing Group, Intelligent Automation, Inc., Rockville, MD 20855, USA;Signal Processing Group, Intelligent Automation, Inc., Rockville, MD 20855, USA;Signal Processing Group, Intelligent Automation, Inc., Rockville, MD 20855, USA;Department of Industrial Engineering, University of Iowa, IA 52242, USA;Department of Industrial Engineering, University of Iowa, IA 52242, USA;Department of Modeling, Simulation and Visualization Engineering, Norfolk, VA 23529, USA;Department of Electrical and Computer Engineering, Old Dominion University, Norfolk, VA 23529, USA
Venue:
Neurocomputing
Year:
2012

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Abstract

In this paper, we developed a wavelet neural network (WNN) algorithm for electroencephalogram (EEG) artifact. The algorithm combines the universal approximation characteristics of neural networks and the time/frequency property of wavelet transform, where the neural network was trained on a simulated dataset with known ground truths. The contribution of this paper is two-fold. First, many EEG artifact removal algorithms, including regression based methods, require reference EOG signals, which are not always available. The WNN algorithm tries to learn the characteristics of EOG from training data and once trained, the algorithm does not need EOG recordings for artifact removal. Second, the proposed method is computationally efficient, making it a reliable real time algorithm. We compared the proposed algorithm to the independent component analysis (ICA) technique and an adaptive wavelet thresholding method on both simulated and real EEG datasets. Experimental results show that the WNN algorithm can remove EEG artifacts effectively without diminishing useful EEG information even for very noisy datasets.