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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.
We propose a novel technique to assess functional brain connectivity in EEG/MEG signals. Our method, called Sparsely-Connected Sources Analysis (SCSA), can overcome the problem of volume conduction by modeling neural data innovatively with the follow
We present a deep learning solution to the problem of localization of magnetoencephalography (MEG) brain signals. The proposed deep model architectures are tuned for single and multiple time point MEG data, and can estimate varying numbers of dipole
Schizophrenia (SZ) is a mental disorder whereby due to the secretion of specific chemicals in the brain, the function of some brain regions is out of balance, leading to the lack of coordination between thoughts, actions, and emotions. This study pro
Epilepsy is one of the most crucial neurological disorders, and its early diagnosis will help the clinicians to provide accurate treatment for the patients. The electroencephalogram (EEG) signals are widely used for epileptic seizures detection, whic
Machine learning methods, such as deep learning, show promising results in the medical domain. However, the lack of interpretability of these algorithms may hinder their applicability to medical decision support systems. This paper studies an interpr