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.
|Number of pages||11|
|Journal||International Journal of Imaging Systems and Technology|
|Publication status||Published - 1 Mar 2017|
- automated anatomical labeling
- Brodmann map
- source localization
- sparse reconstruction