A Beamformer-Particle Filter Framework for Localization of Correlated EEG Sources
Abstract
Electroencephalography (EEG)-based brain computer interface (BCI) is the most studied noninvasive interface to build a direct communication pathway between the brain and an external device. However, correlated noises in EEG measurements still constitute a significant challenge. Alternatively, building BCIs based on filtered brain activity source signals instead of using their surface projections, obtained from the noisy EEG signals, is a promising and not well-explored direction. In this context, finding the locations and waveforms of inner brain sources represents a crucial task for advancing source-based noninvasive BCI technologies. In this paper, we propose a novel multicore beamformer particle filter (multicore BPF) to estimate the EEG brain source spatial locations and their corresponding waveforms. In contrast to conventional (single-core) beamforming spatial filters, the developed multicore BPF considers explicitly temporal correlation among the estimated brain sources by suppressing activation from regions with interfering coherent sources. The hybrid multicore BPF brings together the advantages of both deterministic and Bayesian inverse problem algorithms in order to improve the estimation accuracy. It solves the brai...Continue Reading
References
Reconstructing spatio-temporal activities of neural sources using an MEG vector beamformer technique
Citations
Related Concepts
Related Feeds
Brain-Computer Interface
A brain-computer interface, also known as a brain-machine interface, is a bi-directional communication pathway between an external device and a wired brain. Here is the latest research on this topic.