Selected Papers and Presentations Presented at MODSIM World 2010 Conference & Expo
MODSIM World 2010 Conference & Expo, October 13-15, 2010, Hampton, Virginia
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.
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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.
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.