A multi-resolution approach to localize neural sources of P300 event-related brain potential. Issue 133 (September 2016)

• Record Type: Journal Article
• Authors: Sabeti, M.; Katebi, S.D.; Rastgar, K.; Azimifar, Z.
• Is Part Of: Computer methods and programs in biomedicine. Issue 133(2016)
• Page Start: 155
• Page End: 168
• Publication Date: 2016-09
• Subjects: P300 -- Source localization -- Spatial filter; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2016.05.013
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: We analyzed P300 neural generators of 30 healthy subjects aged 18–30 years. The linearly constrained minimum variance (LCMV) is a kind of spatial beamformer filter. We applied time-reduction region-suppression LCMV (TR-LCMV) to localize neural sources. TR-LCMV with multi-resolution approach is used. Our result showed that frontal, temporal, parietal and cingulate gyrus are the most prominent sources of P300. Abstract: Background and objective: P300 is probably the most well-known component of event-related brain potentials (ERPs). Using an oddball paradigm, a P300 component can be identified, that is, elicited by the target stimuli recognition. Since P300 is associated with attention and memory operations of the brain, investigation of this component can improve our understanding of these mechanisms. The present study is aimed at identifying the P300 generators in 30 healthy subjects aged 18–30 years using time-reduction region-suppression linearly constrained minimum variance (TR-LCMV) beamformer. Methods: In our study, TR-LCMV beamformer with multi-resolution approach is proposed, coarse-resolution space to find the approximated coherent source locations, fine-resolution space to estimate covariance matrix for dimension reduction of determined regions, and normal-resolution space to localize the P300 generators in the brain. Results: Our results over simulated and real data showed that this approach is a suitable tool to the analysis of ERP fields with localizing superior and inferior frontal lobe, middle temporal gyrus, parietal lobe, and cingulate gyrus as the most prominent sources of P300. The result of P300 localization was finally compared with the other localization methods and it is demonstrated that enhanced performance is achieved. Conclusions: Our results showed that the P300 originates from a widespread neuronal network in the brain and not from a specific region. Our finding over simulated and real data demonstrated the ability of the TR-LCMV algorithm for P300 source localization.