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

Article

Saha, Sajib, Rana, Rajib, Nesterets, Yakov, Tahtali, Murat, 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. https://doi.org/10.1002/ima.22209
Article Title

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

ERA Journal ID36561
Article CategoryArticle
AuthorsSaha, Sajib (Author), Rana, Rajib (Author), Nesterets, Yakov (Author), Tahtali, Murat (Author), de Hoog, Frank (Author) and Gureyev, Timur (Author)
Journal TitleInternational Journal of Imaging Systems and Technology
Journal Citation27 (1), pp. 46-56
Number of Pages11
Year2017
PublisherJohn Wiley & Sons
Place of PublicationUnited States
ISSN0899-9457
1098-1098
Digital Object Identifier (DOI)https://doi.org/10.1002/ima.22209
Web Address (URL)https://onlinelibrary.wiley.com/doi/10.1002/ima.22209
Abstract

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.

KeywordsEEG methodology; block sparse Bayesian learning (BSBL); EEG source localization; Brodmann areas; automated anatomical labelling (AAL)
Contains Sensitive ContentDoes not contain sensitive content
ANZSRC Field of Research 2020461199. Machine learning not elsewhere classified
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Byline AffiliationsCommonwealth Scientific and Industrial Research Organisation (CSIRO), Australia
University of Southern Queensland
University of New South Wales
Institution of OriginUniversity of Southern Queensland
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