Document Type
Conference Paper
Publication Date
2011
Publication Title
Selected Papers and Presentations Presented at MODSIM World 2010 Conference & Expo
Pages
820-845
Conference Name
MODSIM World 2010 Conference & Expo, October 13-15, 2010, Hampton, Virginia
Abstract
In this paper we developed a wavelet neural network. (WNN) algorithm for Electroencephalogram (EEG) artifact removal without electrooculographic (EOG) recordings. The algorithm combines the universal approximation characteristics of neural network and the time/frequency property of wavelet. We compared the WNN algorithm with the ICA technique and a wavelet thresholding method, which was realized by using the Stein's unbiased risk estimate (SURE) with an adaptive gradient-based optimal threshold. Experimental results on a driving test data set show that WNN can remove EEG artifacts effectively without diminishing useful EEG information even for very noisy data.
Rights
"Distribution/Availability Statement: Unclassified - Unlimited"
Original Publication Citation
Nguyen, H.-A. T., Musson, J., Li, J., McKenzie, F., Zhang, G., Xu, R., Richey, C., & Schnell, T. (2011) EEG artifact removal using a wavelet neural network. In T.E. Pinelli (Ed.), Selected papers and presentations presented at MODSIM World 2010 Conference & Expo (pp. 820-845). NASA Center for Aerospace Information.
Repository Citation
Nguyen, Hoang-Anh T.; Musson, John; Li, Jiang; McKenzie, Frederick; Zhang, Guangfan; Xu, Roger; Richey, Carl; Schnell, Tom; and Pinelli, Thomas E. (Ed.), "EEG Artifact Removal Using a Wavelet Neural Network" (2011). Electrical & Computer Engineering Faculty Publications. 366.
https://digitalcommons.odu.edu/ece_fac_pubs/366
ORCID
0000-0003-0091-6986 (Li)
Included in
Artificial Intelligence and Robotics Commons, Electrical and Computer Engineering Commons, Neurology Commons, Theory and Algorithms Commons