Background: Dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) provides functional information on the microcirculation in tissues by analyzing the enhancement kinetics which can be used as biomarkers for prostate lesions detection and characterization.Objective: The purpose of this study is to investigate spatiotemporal patterns of tumors by extracting semi-quantitative as well as w...

The automated classification of electroencephalographic (EEG) signals is a very difficult task, especially when these signals become large and noisy. A comprehensive methodology for automated analysis of multichannel EEG signals recorded during long-term monitoring (LTM) has been proposed. The methodology is based on multichannel adaptive segmentation, subsequent comprehensive feature extractio...

A method of automatic adaptive segmentation of EEGs, whereby the boundaries between different patterns of activity appearing in a given channel are identified and demarcated, has been applied to a group of clinical EEGs. The EEGs were especially selected from a library of 70 recordings so as to include a diversity of normal and abnormal EEG patterns. In the subsequent steps of the analysis, lik...

If several mental states can be reliably distinguished by recognizing patterns in EEG, then a paralyzed person could communicate to a device like a wheelchair by composing sequencesof these mental states. In this article, we report on a study comparing four representations of EEG signals and their classification by a two-layer neural network with sigmoid activation functions. The neuralnetwork ...

Introduction: It has been shown that intelligence as a general mental ability is related to the structure and function of the brain regions. However, the specificity of these regional dependencies to the intelligence scores in the typical and atypical developed individuals needs to be well understood. In this study, we hypothesized that neural correlates of IQ should not have a fixed pattern ra...

The simultaneous acquisition and subsequent analysis of EEG and fMRI data is challenging owing to increased noise levels in the EEG data. A common method to integrate data from these two modalities is to use aspects of the EEG data, such as the amplitudes of event-related potentials (ERP) or oscillatory EEG activity, to predict fluctuations in the fMRI data. However, this relies on the acquisit...