Evaluating the performance of BSBL methodology for EEG source localization on a realistic head model

Saha, Sajib and Rana, Rajib and Nesterets, Yakov and Tahtali, Murat and de Hoog, Frank and Gureyev, Timur (2017) Evaluating the performance of BSBL methodology for EEG source localization on a realistic head model. International Journal of Imaging Systems and Technology, 27 (1). pp. 46-56. ISSN 0899-9457

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Source localization in EEG represents a high dimensional inverse problem, which is severely ill-posed by nature. Fortunately, sparsity constraints have come into rescue as it helps solving the ill-posed problems when the signal is sparse. When the signal has a structure such as block structure, consideration of block sparsity produces better results. Knowing sparse Bayesian learning is an important member in the family of sparse recovery, and a superior choice when the projection matrix is highly coherent (which is typical the case for EEG), in this work we evaluate the performance of block sparse Bayesian learning (BSBL) method for EEG source localization. It is already accepted by the EEG community that a group of dipoles rather than a single dipole are activated during brain activities; thus, block structure is a reasonable choice for EEG. In this work we use two definitions of blocks: Brodmann areas and automated anatomical labelling (AAL), and analyze the reconstruction performance of BSBL methodology for them. A realistic head model is used for the experiment, which was obtained from segmentation of MRI images. When the number of simultaneously active blocks is 2, the BSBL produces overall localization accuracy of less than 5 mm without the presence of noise. The presence of more than 3 simultaneously active blocks and noise significantly affect the localization performance. Consideration of AAL based blocks results more accurate source localization in comparison to Brodmann area based blocks.

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Item Type: Article (Commonwealth Reporting Category C)
Refereed: Yes
Item Status: Live Archive
Additional Information: This is an open access article found at: arXiv.org > physics > arXiv:1504.06949
Faculty / Department / School: Current - Institute for Resilient Regions
Date Deposited: 22 Feb 2017 02:40
Last Modified: 19 Dec 2017 06:33
Uncontrolled Keywords: EEG methodology; block sparse Bayesian learning (BSBL); EEG source localization; Brodmann areas; automated anatomical labelling (AAL)
Fields of Research : 08 Information and Computing Sciences > 0899 Other Information and Computing Sciences > 089999 Information and Computing Sciences not elsewhere classified
Socio-Economic Objective: C Society > 92 Health > 9299 Other Health > 929999 Health not elsewhere classified
Identification Number or DOI: 10.1002/ima.22209
URI: http://eprints.usq.edu.au/id/eprint/30056

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