arXiv subjects

Mati Wax

Publications and source records attributed to Mati Wax.

4 recordsLinked to original sources

arXiv2022

Brain Source Localization by Alternating Projection

We present a novel solution to the problem of localizing magnetoencephalography (MEG) and electroencephalography (EEG) brain signals. The solution is sequential and iterative, and is based on minimizing the least-squares criterion by the Alternating Projection algorithm. Results from simulated and experimental MEG data from a human subject demonstrated robust performance, with consistently superior localization accuracy than scanning methods belonging to the beamformer and multiple-signal classification (MUSIC) families. Importantly, the proposed solution is more robust to forward model errors resulting from head rotations and translations, with a significant advantage in highly correlated sources.

eess.SP↗

arXiv2020

Localization of MEG and EEG Brain Signals by Alternating Projection

We present a novel solution to the problem of localization of MEG and EEG brain signals. The solution is sequential and iterative, and is based on minimizing the least-squares (LS)criterion by the Alternating Projection (AP) algorithm, which is well known in the context of array signal processing. Unlike existing scanning solutions belonging to the beamformer and multiple-signal classification (MUSIC) families, the algorithm has good performance in low signal-to-noise ratio (SNR) and can cope with closely spaced sources and any mixture of correlated sources. Results from simulated and experimental MEG data from a real phantom demonstrated robust performance across an extended SNR range, the entire inter-source correlation range, and across multiple sources, with consistently superior localization accuracy than popular scanning methods.

eess.SP↗

arXiv2018

Direct Localization of Multiple Sources by Partly Calibrated Arrays

We present novel solutions to the problem of direct localization of multiple narrow-band and arbitrarily correlated sources by partly calibrated arrays, i.e., arrays composed of fully calibrated sub-arrays yet lacking inter-array calibration. The solutions presented vary in their performance and computational complexity. We present first a relaxed maximum likelihood solution whose concentrated likelihood involves only the unknown locations of the sources and requires an eigen-decomposition of the array covariance matrix at every potential location. To reduce the computational load, we introduce an approximation which eliminates the need for such an eigen-decomposition at every potential location. To further reduce the computational load, novel MUSIC-like and MVDR-like solutions are presented which are computationally much simpler than the existing solutions. The performance of these solutions is evaluated and compared via simulations.

eess.SP↗

arXiv2017

Constant Modulus Beamforming via Convex Optimization

We present novel convex-optimization-based solutions to the problem of blind beamforming of constant modulus signals, and to the related problem of linearly constrained blind beamforming of constant modulus signals. These solutions ensure global optimality and are parameter free, namely, do not contain any tuneable parameters and do not require any a-priori parameter settings. The performance of these solutions, as demonstrated by simulated data, is superior to existing methods.

cs.IT↗