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 brain activity localization problem without prior information about approximate areas of source locations. Moreover, the multicore BPF reduces the dimensionality of the problem to half compared with the PF solution, thus alleviating the curse of dimensionality problem. The results, based on generated and real EEG data, show that the proposed framework recovers correctly the dominant sources of brain activity.
Highlights
E LECTROENCEPHALOGRAPHY (EEG) is a widely used technology for brain study because it is noninvasive, relatively cheap, portable and with an excellent temporal resolution
They can be divided in two main classes, [2]: 1) imaging models, which explain the data with a dense set of current dipoles distributed at fixed locations; and 2) equivalent current dipole models, which assume a small number of focal sources at locations to be estimated from the data
We propose to deal with this problem by the iterative multicore BF-particle filter (PF) procedure where starting from randomly generated assumption for the dipole spatial coordinates of the active dipoles the PF converges to a small number of dominant sources
Summary
E LECTROENCEPHALOGRAPHY (EEG) is a widely used technology for brain study because it is noninvasive, relatively cheap, portable and with an excellent temporal resolution. Popular deterministic parametric solutions include the multiple signal classification (MUSIC) algorithm and its modified versions [7], the methods for inverse problems [8], the construction of spatial filters by data-independent [9] or data-driven methods [10] and blind source separation techniques [11], [12] These approaches are based on the assumption that the brain source locations are known a priori or perform a search of the overall head volume to find their positions. Since the number of the identified sources (by PF) is smaller than the suppressed single correlated interferers or nulling entire brain volumes as in previous works [27], [28], the computational complexity of the proposed combined solution is significantly lower; 3) Satisfactory reconstruction accuracy was obtained for very low EEG signal to noise ratios (less than 8 dB) which is an additional advantage of the hybrid approach.
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