Date of Award

Fall 2010

Document Type

Thesis

Degree Name

Master of Science (MS)

Department

Electrical & Computer Engineering

Program/Concentration

Electrical Engineering

Committee Director

Jiang Li

Committee Member

Frederic D. McKenzie

Committee Member

Yuzhong Shen

Call Number for Print

Special Collections LD4331.E55 N575 2010

Abstract

A wavelet neural network (WNN) technique rs developed for electroencephalogram (EEG) artifact removal without electrooculographic (EOG) recordings. The algorithm combines the universal approximation characteristics of neural networks and the time/frequency property of wavelet, where the neural network was trained on a simulated dataset with known ground truths. The contribution of this thesis is two-fold. First, many EEG artifact removal algorithms, including regression based methods, require reference EOG signals, which are not always available. To remove EEG ai1ifacts, a WNN tries to learn the characteristics of the artifacts first and does not need reference EOG signals once trained. Second, WNNs are computationally efficient, making them a reliable real time algorithm. A WNN algorithm is then compared with the independent component analysis (!CA) technique and an adaptive wavelet thresholding method is used on both simulated and real datasets. Experimental results show that a WNN can remove EEG artifacts effectively without diminishing useful EEG information even for very noisy datasets.

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DOI

10.25777/hthh-w349

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