Evaluating the performance of BSBL methodology for EEG source localization on a realistic head model. Issue 1 (21st March 2017)
- Record Type:
- Journal Article
- Title:
- Evaluating the performance of BSBL methodology for EEG source localization on a realistic head model. Issue 1 (21st March 2017)
- Main Title:
- Evaluating the performance of BSBL methodology for EEG source localization on a realistic head model
- Authors:
- Saha, Sajib
Rana, Rajib
Nesterets, Yakov
Tahtali, Murat
de Hoog, Frank
Gureyev, Timur - Abstract:
- ABSTRACT: In this paper, we evaluate the performance of block sparse Bayesian learning (BSBL) method for EEG source localization. By exploiting the internal block structure, the BSBL method solves the ill‐posed inverse problem more efficiently than other methods that do not consider block structure. Simulation experiments were conducted on a realistic head model obtained by segmentation of MRI images of the head. Two definitions of blocks were considered: Brodmann areas and automated anatomical labeling (AAL). The experiments were performed both with and without the presence of noise. Six different noise levels were considered having SNR values from 5 dB to 30 dB with 5dB increment. The evaluation reveals several potential findings—first, BSBL is more likely to produce better source localization than sparse Bayesian learning (SBL), however, this is true up until a limited number of simultaneously active areas only. Experimental results show that for 71‐channel electrodes setup BSBL outperforms SBL for up to three simultaneously active blocks. From four simultaneously active blocks SBL turns out to be marginally better and the difference between them is statistically insignificant. Second, different anatomical block structures such as Brodmann areas or AAL does not seem to produce any significant difference in EEG source localization relying on BSBL. Third, even when the block partitions are not known exactly BSBL ensures better localization than SBL as soon as blockABSTRACT: In this paper, we evaluate the performance of block sparse Bayesian learning (BSBL) method for EEG source localization. By exploiting the internal block structure, the BSBL method solves the ill‐posed inverse problem more efficiently than other methods that do not consider block structure. Simulation experiments were conducted on a realistic head model obtained by segmentation of MRI images of the head. Two definitions of blocks were considered: Brodmann areas and automated anatomical labeling (AAL). The experiments were performed both with and without the presence of noise. Six different noise levels were considered having SNR values from 5 dB to 30 dB with 5dB increment. The evaluation reveals several potential findings—first, BSBL is more likely to produce better source localization than sparse Bayesian learning (SBL), however, this is true up until a limited number of simultaneously active areas only. Experimental results show that for 71‐channel electrodes setup BSBL outperforms SBL for up to three simultaneously active blocks. From four simultaneously active blocks SBL turns out to be marginally better and the difference between them is statistically insignificant. Second, different anatomical block structures such as Brodmann areas or AAL does not seem to produce any significant difference in EEG source localization relying on BSBL. Third, even when the block partitions are not known exactly BSBL ensures better localization than SBL as soon as block structure persists in the signal. © 2017 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 27, 46–56, 2017 … (more)
- Is Part Of:
- International journal of imaging systems and technology. Volume 27:Issue 1(2017:Mar.)
- Journal:
- International journal of imaging systems and technology
- Issue:
- Volume 27:Issue 1(2017:Mar.)
- Issue Display:
- Volume 27, Issue 1 (2017)
- Year:
- 2017
- Volume:
- 27
- Issue:
- 1
- Issue Sort Value:
- 2017-0027-0001-0000
- Page Start:
- 46
- Page End:
- 56
- Publication Date:
- 2017-03-21
- Subjects:
- electroencephalography -- source localization -- Brodmann map -- automated anatomical labeling -- sparse reconstruction -- BSBL
Imaging systems -- Periodicals
Image processing -- Periodicals
621.367 - Journal URLs:
- http://onlinelibrary.wiley.com/journal/10.1002/(ISSN)1098-1098 ↗
http://onlinelibrary.wiley.com/ ↗ - DOI:
- 10.1002/ima.22209 ↗
- Languages:
- English
- ISSNs:
- 0899-9457
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - 4542.299000
British Library DSC - BLDSS-3PM
British Library HMNTS - ELD Digital store - Ingest File:
- 25.xml