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Tikhonov Regularization Methods for the Inverse Scalp Electroencephalography

[+] Author Affiliations
Menglu Wu, Xiaolin Chen

Washington State University, Vancouver, WA

Paper No. IMECE2009-10538, pp. 277-283; 7 pages
  • ASME 2009 International Mechanical Engineering Congress and Exposition
  • Volume 2: Biomedical and Biotechnology Engineering
  • Lake Buena Vista, Florida, USA, November 13–19, 2009
  • Conference Sponsors: ASME
  • ISBN: 978-0-7918-4375-8 | eISBN: 978-0-7918-3863-1
  • Copyright © 2009 by ASME


Electroencephalography (EEG) source localization of brain activity is of high diagnostic value. Noninvasive numerical procedures can be developed to help reconstruct the cortical brain activities from the low-spatial-resolution scalp EEG measurement. In this paper, Tikhonov regularization methods are employed to tackle the solution difficulty associated with the ill-posed reconstruction problem. Three different techniques, namely the L-curve method, the generalized cross validation (GCV) and the discrepancy principle (DP), are implemented to help identify an optimum parameter for the numerical regularization. The numerical procedures are verified by comparing reconstruction results with available theoretical potential solutions for a simplified concentric sphere head model. All three parameter selection methods achieve good results and the L-curve method produces the best regularization effect among the three when the noise level is high in the contaminated scalp data input. More studies are performed on a computational model of an anatomically realistic human head. Our results show that the combination of Tikhonov regularization with the L-curve parameter selection method can effectively regularize the ill-posed inverse EEG problem for brain potential reconstruction.

Copyright © 2009 by ASME



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