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In this paper, we consider the problem of quantifying synchrony between multiple simultaneously recorded electroencephalographic signals. These signals exhibit nonlinear dependencies and non-Gaussian statistics. A copula based approach is presented to model the joint statistics. We then consider the application of copula derived synchrony measures for early diagnosis of Alzheimer's disease. Results...
It has frequently been reported in the medical literature that the EEG of Alzheimer disease (AD) patients is less synchronous than in healthy subjects. In this paper, it is explored whether loss in EEG synchrony can be used to diagnose AD at an early stage. Multiple synchrony measures are applied to two different EEG data sets: (1) EEG of pre-dementia patients and control subjects; (2) EEG of mild...
Noninvasive measurement techniques like EEG (electroencephalography) or MEG (magnetoencephalography) provide a good time resolution but suffer of a lack of spatial resolution. Source reconstruction is a solution for increasing the spatial resolution. It requires to solve an ill-posed inverse problem where the challenge is to restrict the source space, making a compromise between smooth and sparse...
The 17 papers in this special issue present significant contributions in three primary areas: data acquisition and preprocessing; brain activity detection and pattern analysis; and brain functional connectivity. The papers selected in this special issue, provide an overview of the state-of-art in the exciting and fruitful field of signal processing and modeling of brain fMRI. We hope that this collection...
In addition to helping better understand how the human brain works, the brain-computer interface neuroscience paradigm allows researchers to develop a new class of bioengineering control devices and robots, offering promise for rehabilitation and other medical applications as well as exploring possibilities for advanced human-computer interfaces.
The early detection of Alzheimer's disease (AD) is an important challenge. In this paper, we propose a novel method for early detection of AD using only electroencephalographic (EEG) recordings for patients with mild cognitive impairment (MCI) without any clinical symptoms of the disease who later developed AD. In our method, first a blind source separation algorithm is applied to extract the most...
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