[A Novel Channel Selection Method for Brain-computer Interface Based on Relief-SBS].

Regarding to the channel selection problem during the classification of electroencephalogram(EEG)signals,we proposed a novel method,Relief-SBS,in this paper.Firstly,the proposed method performed EEG channel selection by combining the principles of Relief and sequential backward selection(SBS)algorithms.And then correlation coefficient was used for classification of EEG signals.

A novel channel selection method for optimal classification in different motor imagery BCI paradigms.

Background: For sensorimotor rhythms based brain-computer interface (BCI) systems, classification of different motor imageries (MIs) remains a crucial problem. An important aspect is how many scalp electrodes (channels) should be used in order to reach optimal performance classifying motor imaginations. While the previous researches on channel selection mainly focus on MI tasks paradigms without feedback, the present work aims to investigate the optimal channel selection in MI tasks paradigms with real-time feedback (two-class control and four-class control paradigms).

[The blind source separation method based on self-organizing map neural network and convolution kernel compensation for multi-channel sEMG signals].

A new method based on convolution kernel compensation (CKC) for decomposing multi-channel surface electromyogram (sEMG) signals is proposed in this paper. Unsupervised learning and clustering function of self-organizing map (SOM) neural network are employed in this method. An initial innervations pulse train (IPT) is firstly estimated, some time instants corresponding to the highest peaks from the initial IPT are clustered by SOM neural network.

Surface EMG decomposition based on K-means clustering and convolution kernel compensation.

A new approach has been developed by combining the K-mean clustering (KMC) method and a modified convolution kernel compensation (CKC) method for multichannel surface electromyogram (EMG) decomposition. The KMC method was first utilized to cluster vectors of observations at different time instants and then estimate the initial innervation pulse train (IPT). The CKC method, modified with a novel multistep iterative process, was conducted to update the estimated IPT.

Assessing dynamic spectral causality by lagged adaptive directed transfer function and instantaneous effect factor.

It is of significance to assess the dynamic spectral causality among physiological signals. Several practical estimators adapted from spectral Granger causality have been exploited to track dynamic causality based on the framework of time-varying multivariate autoregressive (tvMVAR) models. The nonzero covariance of the model's residuals has been used to describe the instantaneous effect phenomenon in some causality estimators.

A reconstruction algorithm of magnetoacoustic tomography with magnetic induction for an acoustically inhomogeneous tissue.

Magnetoacoustic tomography with magnetic induction (MAT-MI) is a noninvasive electrical conductivity imaging approach that measures ultrasound wave induced by magnetic stimulation, for reconstructing the distribution of electrical impedance in a biological tissue. Existing reconstruction algorithms for MAT-MI are based on the assumption that the acoustic properties in the tissue are homogeneous. However, the tissue in most parts of human body has heterogeneous acoustic properties, which leads to potential distortion and blurring of small buried objects in the impedance images.

Design and implementation of an internet-based electrical engineering laboratory.

This paper describes an internet-based electrical engineering laboratory (IEE-Lab) with virtual and physical experiments at Zhejiang University. In order to synthesize the advantages of both experiment styles, the IEE-Lab is come up with Client/Server/Application framework and combines the virtual and physical experiments. The design and workflow of IEE-Lab are introduced.

Numerical study of magnetoacoustic signal generation with magnetic induction based on inhomogeneous conductivity anisotropy.

Magnetoacoustic tomography with magnetic induction (MAT-MI) is a noninvasive imaging modality for generating electrical conductivity images of biological tissues with high spatial resolution. In this paper, we create a numerical model, including a permanent magnet, a coil, and a two-layer coaxial cylinder with anisotropic electrical conductivities, for the MAT-MI forward problem. We analyze the MAT-MI sources in two cases, on a thin conductive boundary layer and in a homogeneous medium, and then develop a feasible numerical approach to solve the MAT-MI sound source densities in the anisotropic conductive model based on finite element analysis of electromagnetic field.

EEG-based motor imagery classification accuracy improves with gradually increased channel number.

The question of how many channels should be sed for classification remains a key issue in the study of Brain-Computer Interface. Several studies have shown that a reduced number of channels can achieve the optimal classification accuracy in the offline analysis of motor imagery paradigm, which does not have real-time feedback as in the online control. However, for the cursor movement control paradigm, it remains unclear as to how many channels should be selected in order to achieve the optimal classification.

[Research progress and prospect of electrical properties tomography for prostate].

We reviewed the research progress and prospect of electrical properties tomography for prostate in this paper. After the introduction of the basic principles of electrical impedance tomography (EIT) and magnetic resonance electric impedance tomography (MREIT), we presented the applications of the two techniques in electrical properties tomography of the prostate in detail. We then discussed the application prospects of induced current magnetic resonance electric impedance tomography (IC-MREIT) and magnetic resonance electrical properties tomography (MREPT) in the diagnoses of prostate cancer.

Magnetoacoustic tomography with magnetic induction (MAT-MI) for breast tumor imaging: numerical modeling and simulation.

Magnetoacoustic tomography with magnetic induction (MAT-MI) was recently introduced as a noninvasive electrical conductivity imaging approach with high spatial resolution close to ultrasound imaging. In this study, we test the feasibility of the MAT-MI method for breast tumor imaging using numerical modeling and computer simulation. Using the finite element method, we have built three-dimensional numerical breast models with varieties of embedded tumors for this simulation study.

[A study of brain inner tissue water molecule self-diffusion model based on Monte Carlo simulation].

The study of water molecule self-diffusion process is of importance not only for getting anatomical information of brain inner tissue, but also for shedding light on the diffusion process of some medicine in brain tissue. In this paper, we summarized the self-diffusion model of water molecule in brain inner tissue, and calculated the self-diffusion coefficient based on Monte Carlo simulation under different conditions. The comparison between this result and that of Latour model showed that the two self-diffusion coefficients were getting closer when the diffusion time became longer, and that the Latour model was a long time-depended self-diffusion model.

Imaging electric properties of biological tissues by RF field mapping in MRI.

The electric properties (EPs) of biological tissue, i.e., the electric conductivity and permittivity, can provide important information in the diagnosis of various diseases.

Magnetic resonance electric property imaging of brain tissues.

The electric properties (EPs) of brain tissues, i.e., the electric conductivity and permittivity, can provide important information for diagnosis of various brain disorders.

Induced current magnetic resonance electrical impedance tomography of brain tissues based on the J-substitution algorithm: a simulation study.

We have investigated induced current magnetic resonance electrical impedance tomography (IC-MREIT) by means of computer simulations. The J-substitution algorithm was implemented to solve the IC-MREIT reconstruction problem. By providing physical insight into the charge accumulating on the interfaces, the convergence characteristics of the reconstruction algorithm were analyzed.

Solving the forward problem of magnetoacoustic tomography with magnetic induction by means of the finite element method.

Magnetoacoustic tomography with magnetic induction (MAT-MI) is a recently proposed imaging modality to image the electrical impedance of biological tissue. It combines the good contrast of electrical impedance tomography with the high spatial resolution of sonography. In this paper, a three-dimensional MAT-MI forward problem was investigated using the finite element method (FEM).

A new method to derive white matter conductivity from diffusion tensor MRI.

We propose a new algorithm to derive the anisotropic conductivity of the cerebral white matter (WM) from the diffusion tensor MRI (DT-MRI) data. The transportation processes for both water molecules and electrical charges are described through a common multicompartment model that consists of axons, glia, or the cerebrospinal fluid (CSF). The volume fraction (VF) of each compartment varies from voxel to voxel and is estimated from the measured diffusion tensor.

Noninvasive imaging of head-brain conductivity profiles.

Magnetic resonance electrical impedance tomography (MREIT) is a recently introduced non-invasive conductivity imaging modality, which combines the magnetic resonance current density imaging (CDI) and the traditional electrical impedance tomography (EIT) techniques. MREIT is aimed at providing high spatial resolution images of electrical conductivity, by avoiding solving the well-known ill-posed problem in the traditional EIT. In this paper, we review our research activities in MREIT imaging of head-brain tissue conductivity profiles.

[A review of electrical impedance tomography based on MRI technique].

In this paper, a review of a new electrical impedance tomography technique-magnetic resonance electrical impedance tomography (MREIT) is presented. Some medical imaging methods are briefly introduced. The basic theory of MREIT is given as well as its realization methods and developing status.

[Adaptive finite element realistic head modeling].

Finite element realistic geometry head modeling is of significance in high-resolution electroencephalogram research. A new adaptive method of finite element realistic head modeling method is presented. An automatic refinement algorithm is developed to perform local adaptive meshing, which improves the efficiency of the modeling.

Effects of holes on EEG forward solutions using a realistic geometry head model.

Holes in the skull and the scalp are associated with intracranial monitoring procedures. The purpose of the present study is to evaluate the effects of holes on extracranial electroencephalogram (EEG) and intracranial electrocorticogram (ECoG) recordings. The finite difference method (FDM) was used to model the head volume conductor with a hole of varying size.

Detecting Human Head Conductivity Distribution by Means of a RSM- MREIT Algorithm.

We have developed a new Magnetic Resonance Electrical Impedance Tomography (MREIT) algorithm, RSM-MREIT. This new algorithm uses Response Surface Methodology (RSM) and Simplex method to reconstruct subject's conductivity distribution. RSM-MREIT algorithm uses only one component of the measured magnetic flux density to reconstruct the conductivity images, and, consequently, solves the rotation problem in MREIT.

Finite element modeling of human head from MRI.

The FEM geometry modeling of realistic head is a key issue for the research on FEM-based EEG/MEG. In this paper, a methodology is developed to construct this kind of model. By using this method, a five-layer realistic head FEM model is obtained, and with its application in FEM-based EEG, a satisfying result shows the reliability of the model.

A fast method to derive realistic FEM models based on BEM models.

A fast method for constructing a FEM head model based on the relevant BEM head model is presented. The method has been evaluated and shown to provide an alternative means of deriving FEM head models. The availability of such fast method would facilitate the realistic head modeling for EEG/MEG research.

A finite difference method for solving the three-dimensional EEG forward problem.

A finite difference method (FDM) has been implemented to solve the electroencephalogram (EEG) forward problem. This method has been evaluated by means of computer simulations, by comparing with analytic solutions in a three-sphere concentric head model. The effects of dipole eccentricity, spacing of finite difference model and number of grid nodes on solution accuracy are also addressed in the simulations.