Journal article
EOG artifact removal using a wavelet neural network
Neurocomputing (Amsterdam), Vol.97, pp.374-389
11/15/2012
DOI: 10.1016/j.neucom.2012.04.016
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.
Details
- Title: Subtitle
- EOG artifact removal using a wavelet neural network
- Creators
- Hoang-Anh T Nguyen - Department of Modeling, Simulation and Visualization Engineering, Norfolk, VA 23529, USAJohn Musson - Department of Electrical and Computer Engineering, Old Dominion University, Norfolk, VA 23529, USAFeng Li - Department of Electrical and Computer Engineering, Old Dominion University, Norfolk, VA 23529, USAWei Wang - University of Iowa, BiologyGuangfan Zhang - Signal Processing Group, Intelligent Automation, Inc., Rockville, MD 20855, USARoger Xu - Signal Processing Group, Intelligent Automation, Inc., Rockville, MD 20855, USACarl Richey - Department of Industrial Engineering, University of Iowa, IA 52242, USATom Schnell - University of Iowa, Industrial and Systems EngineeringFrederic D McKenzie - Department of Modeling, Simulation and Visualization Engineering, Norfolk, VA 23529, USAJiang Li - Department of Electrical and Computer Engineering, Old Dominion University, Norfolk, VA 23529, USA
- Resource Type
- Journal article
- Publication Details
- Neurocomputing (Amsterdam), Vol.97, pp.374-389
- Publisher
- Elsevier B.V
- DOI
- 10.1016/j.neucom.2012.04.016
- ISSN
- 0925-2312
- eISSN
- 1872-8286
- Grant note
- DOI: 10.13039/100000104, name: National Aeronautics and Space Administration, award: NNX10CB27C
- Language
- English
- Date published
- 11/15/2012
- Academic Unit
- Electrical and Computer Engineering; Occupational and Environmental Health; Industrial and Systems Engineering; Neurology; Public Policy Center (Archive)
- Record Identifier
- 9984025081202771
Metrics
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