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Susceptibility-weighted magnetic resonance imaging is a powerful tool for high resolution imaging of the vasculature, aiding in the diagnosis of many pathologic conditions. The technique is especially beneficial at higher field strengths where traditional sequences that measure cerebral blood volume suffer from severe distortions, rendering them inapplicable at 7 T. However, conventional susceptibility-weighted...
Susceptibility-weighted magnetic resonance imaging is a powerful tool for high resolution imaging of the vasculature, aiding in the diagnosis of many pathologic conditions. The technique is especially beneficial at higher field strengths where traditional sequences that measure cerebral blood volume suffer from severe distortions, rendering them inapplicable at 7 T. However, conventional susceptibility-weighted...
The objective in bioelectric measurements such as ECG and EEG is to register the signal arising from sources in the region of interest. It is also desired that signal-to-noise ratio (SNR) of a measurement is high. The sensitivity of an ideal measurement should focus on and be greater on the target areas in comparison to other areas of the volume conductor. Previously the half-sensitivity volume (HSV)...
The objective in bioelectric measurements such as ECG and EEG is to register the signal arising from sources in the region of interest. It is also desired that signal-to-noise ratio (SNR) of a measurement is high. The sensitivity of an ideal measurement should focus on and be greater on the target areas in comparison to other areas of the volume conductor. Previously the half-sensitivity volume (HSV)...
A 64-run, 2-level partial factorial experimental analysis was conducted on a 2D axisymmetric finite-element model of the convection-enhanced drug delivery to the parenchyma of the brain. The purpose of this ANOVA analysis was to determine the relative importance of eight factors and their interaction in the volume of distribution for the drug. The analysis revealed that the infusion flowrate and concentration,...
A 64-run, 2-level partial factorial experimental analysis was conducted on a 2D axisymmetric finite-element model of the convection-enhanced drug delivery to the parenchyma of the brain. The purpose of this ANOVA analysis was to determine the relative importance of eight factors and their interaction in the volume of distribution for the drug. The analysis revealed that the infusion flowrate and concentration,...
The issue of subject-specific parameter selection in an electroencephalogram (EEG)-based brain-computer interface (BCI) is tackled in this paper. Hjorth- and Barlow-based feature extraction procedures (FEPs) are investigated along with linear discriminant analysis (LDA) for classification. These are well-known nonparametric FEPs but their simplicity prevents them from matching the performance of more...
The issue of subject-specific parameter selection in an electroencephalogram (EEG)-based brain-computer interface (BCI) is tackled in this paper. Hjorth- and Barlow-based feature extraction procedures (FEPs) are investigated along with linear discriminant analysis (LDA) for classification. These are well-known nonparametric FEPs but their simplicity prevents them from matching the performance of more...
In this paper, we present a new automatic method for sleep spindle detection. It consist of a generalisation of the Schimicek's method [12] that takes more types of artefacts into account and uses variable thresholds regarding the statistical properties of the signal. Validity of our process is examined on the basis of visual spindle scoring performed by an expert. Results obtained are compared to...
In this paper, we present a new automatic method for sleep spindle detection. It consist of a generalisation of the Schimicek's method [12] that takes more types of artefacts into account and uses variable thresholds regarding the statistical properties of the signal. Validity of our process is examined on the basis of visual spindle scoring performed by an expert. Results obtained are compared to...
The goal of this paper is to improve on single-trial classification of electro-encephalography (EEG) using linear methods. The paper proposes to combine the classification of the spatial distribution of activity with the classification of its temporal profile. The work is based on the idea that a current source in the brain has a reproducible temporal profile with a static spatial projection to the...
The goal of this paper is to improve on single-trial classification of electro-encephalography (EEG) using linear methods. The paper proposes to combine the classification of the spatial distribution of activity with the classification of its temporal profile. The work is based on the idea that a current source in the brain has a reproducible temporal profile with a static spatial projection to the...
Gravity transform measures cooperative neural activity being utilized for the analysis of neural assemblies. In this paper we verify the applicability of the gravity transform to specify components of neural assemblies, which could be combined, leading ultimately to a reduction of the input dimensionality in brain-machine interface models. Our analysis was performed on data collected from rats performing...
Gravity transform measures cooperative neural activity being utilized for the analysis of neural assemblies. In this paper we verify the applicability of the gravity transform to specify components of neural assemblies, which could be combined, leading ultimately to a reduction of the input dimensionality in brain-machine interface models. Our analysis was performed on data collected from rats performing...
A multi-input modeling approach is introduced to quantify hippocampal neural dynamics. It is based on the Volterra modeling approach extended to multiple inputs. The computed Volterra kernels allow quantitative description of hippocampal transformations and define a predictive model that can produce responses to arbitrary input patterns. Electrophysiological data from several CA3 and CA1 cells in...
A multi-input modeling approach is introduced to quantify hippocampal neural dynamics. It is based on the Volterra modeling approach extended to multiple inputs. The computed Volterra kernels allow quantitative description of hippocampal transformations and define a predictive model that can produce responses to arbitrary input patterns. Electrophysiological data from several CA3 and CA1 cells in...
In this paper, we present a comprehensive neural network based modeling and validation framework for reverse engineering gene regulatory interactions. We employ two approaches, Gene Set Stochastic Sampling and Sensitivity Analysis, to infer these interactions. We first apply these methods to a simulated artificial dataset to ensure their correctness and accuracy. True biological interactions are then...
In this paper, we present a comprehensive neural network based modeling and validation framework for reverse engineering gene regulatory interactions. We employ two approaches, Gene Set Stochastic Sampling and Sensitivity Analysis, to infer these interactions. We first apply these methods to a simulated artificial dataset to ensure their correctness and accuracy. True biological interactions are then...
Rheoencephalography (REG) is impedance plethysmography applied to the head, and provides an indirect measurement of the pulsatility of the cerebral blood volume. To extend REG as a clinical and research tool, it is necessary to evaluate the sensitivity of REG measurement to local brain conductivity changes. By means of the analytical solution of a four-sphere geometrical model of the head, maps of...
Rheoencephalography (REG) is impedance plethysmography applied to the head, and provides an indirect measurement of the pulsatility of the cerebral blood volume. To extend REG as a clinical and research tool, it is necessary to evaluate the sensitivity of REG measurement to local brain conductivity changes. By means of the analytical solution of a four-sphere geometrical model of the head, maps of...
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