Brain source imaging based on EEG aims to reconstruct the neural activities producing the scalp potentials. This includes solving the forward and inverse problems. The aim of the inverse problem is to estimate the activity of the brain sources based on the measured data and leadfield matrix computed in the forward step. Spatial filtering, also known as beamforming, is an inverse method that reconstructs the time course of the source at a particular location by weighting and linearly combining the sensor data. In this paper, we considered a temporal assumption related to the time course of the source, namely sparsity, in the Linearly Constrained Minimum Variance (LCMV) beamformer. This assumption sounds reasonable since not all brain sources are active all the time such as epileptic spikes and also some experimental protocols such as electrical stimulations of a peripheral nerve can be sparse in time. Developing the sparse beamformer is done by incorporating L1-norm regularization of the beamformer output in the relevant cost function while obtaining the filter weights. We called this new beamformer SParse LCMV (SP-LCMV). We compared the performance of the SP-LCMV with that of LCMV for both superficial and deep sources with different amplitudes using synthetic EEG signals. Also, we compared them in localization and reconstruction of sources underlying electric median nerve stimulation. Results show that the proposed sparse beamformer can enhance reconstruction of sparse sources especially in the case of sources with high amplitude spikes. 相似文献
The mixing performance of the oil‐in‐water dispersion system was evaluated. Using an electrical resistance tomography system composed of two measuring planes, the effect of parameters such as impeller type, impeller speed, oil type, and oil volume fraction on the mixing performance through axial mixing indices were explored. The oil type and the oil volume fraction were identified as the most influential factors on the mixing index. Castor oil, with the highest viscosity of the tested oils, was found as the most difficult oil to disperse. The Scaba impeller was the most efficient impeller in dispersing oil in water. The interactions between oil type and impeller type as well as between impeller speed and oil type, had the greatest impact on the mixing index. 相似文献
We propose a modified Fitzhugh-Nagumo neuron (MFNN) model. Based on this model, an integer-order MFNN system (case A) and a fractional-order MFNN system (case B) were investigated. In the presence of electromagnetic induction and radiation, memductance and induction can show a variety of distributions. Fractional-order magnetic flux can then be considered. Indeed, a fractional-order setting can be acceptable for non-uniform diffusion. In the case of an MFNN system with integer-order discontinuous magnetic flux, the system has chaotic and non-chaotic attractors. Dynamical analysis of the system shows the birth and death of period doubling, which is a sign of antimonotonicity. Such a behavior has not been studied previously in the dynamics of neurons. In an MFNN system with fractional-order discontinuous magnetic flux, different attractors such as chaotic and periodic attractors can be observed. However, there is no sign of antimonotonicity.
DOSE is unique among structure editor generatiors in its interpretive approach. This approach leads to very fast turn-around time for changes and provides multi-language facilities for no aditional effort or cost. This article compares the interpretive approach to the compilation approach of other structure editor generators. It describes some of the design and implementation decisions made and remade durign this project and the lessons learned. It emphasizes the advantages and disadvantages of DOSE with respect to other structure editing systems. 相似文献