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The establishment of brain metabolic network is based on 18 fluoro-deoxyglucose positron emission computed tomography ( 18 F-FDG PET) analysis, which reflect the brain functional network connectivity in normal physiological state or disease state. It is now applied to basic and clinical brain functional network research. In this paper, we constructed a metabolic network for the cerebral cortex firstly according to 18 F-FDG PET image data from patients with temporal lobe epilepsy (TLE).Then, a statistical analysis to the network properties of patients with left or right TLE and controls was performed. It is shown that the connectivity of the brain metabolic network is weakened in patients with TLE, the topology of the network is changed and the transmission efficiency of the network is reduced, which means the brain metabolic network connectivity is extensively impaired in patients with TLE. It is confirmed that the brain metabolic network analysis based on 18 F-FDG PET can provide a new perspective for the diagnose and therapy of epilepsy by utilizing PET images.

Transcranial electrical stimulation (TES) is a non-invasive neuromodulation technique with great potential. Electrode optimization methods based on simulation models of individual TES field could provide personalized stimulation parameters according to individual variations in head tissue structure, significantly enhancing the stimulation accuracy of TES. However, the existing electrode optimization methods suffer from prolonged computation times (typically exceeding 1 d) and limitations such as disregarding the restricted number of output channels from the stimulator, further impeding their clinical applicability. Hence, this paper proposes an efficient and practical electrode optimization method. The proposed method simultaneously optimizes both the intensity and focality of TES within the target brain area while constraining the number of electrodes used, and it achieves faster computational speed. Compared to commonly used electrode optimization methods, the proposed method significantly reduces computation time by 85.9% while maintaining optimization effectiveness. Moreover, our method considered the number of available channels for the stimulator to distribute the current across multiple electrodes, further improving the tolerability of TES. The electrode optimization method proposed in this paper has the characteristics of high efficiency and easy operation, potentially providing valuable supporting data and references for the implementation of individualized TES.

Motor imagery is often used in the fields of sports training and neurorehabilitation for its advantages of being highly targeted, easy to learn, and requiring no special equipment, and has become a major research paradigm in cognitive neuroscience. Transcranial direct current stimulation (tDCS), an emerging neuromodulation technique, modulates cortical excitability, which in turn affects functions such as locomotion. However, it is unclear whether tDCS has a positive effect on motor imagery task states. In this paper, 16 young healthy subjects were included, and the electroencephalogram (EEG) signals and near-infrared spectrum (NIRS) signals of the subjects were collected when they were performing motor imagery tasks before and after receiving tDCS, and the changes in multiscale sample entropy (MSE) and haemoglobin concentration were calculated and analyzed during the different tasks. The results found that MSE of task-related brain regions increased, oxygenated haemoglobin concentration increased, and total haemoglobin concentration rose after tDCS stimulation, indicating that tDCS increased the activation of task-related brain regions and had a positive effect on motor imagery. This study may provide some reference value for the clinical study of tDCS combined with motor imagery.

Medical image registration plays an important role in medical diagnosis and treatment planning. However, the current registration methods based on deep learning still face some challenges, such as insufficient ability to extract global information, large number of network model parameters, slow reasoning speed and so on. Therefore, this paper proposed a new model LCU-Net, which used parallel lightweight convolution to improve the ability of global information extraction. The problem of large number of network parameters and slow inference speed was solved by multi-scale fusion. The experimental results showed that the Dice coefficient of LCU-Net reached 0.823, the Hausdorff distance was 1.258, and the number of network parameters was reduced by about one quarter compared with that before multi-scale fusion. The proposed algorithm shows remarkable advantages in medical image registration tasks, and it not only surpasses the existing comparison algorithms in performance, but also has excellent generalization performance and wide application prospects.

The brain-computer interface (BCI) based on motor imagery electroencephalography (MI-EEG) enables direct information interaction between the human brain and external devices. In this paper, a multi-scale EEG feature extraction convolutional neural network model based on time series data enhancement is proposed for decoding MI-EEG signals. First, an EEG signals augmentation method was proposed that could increase the information content of training samples without changing the length of the time series, while retaining its original features completely. Then, multiple holistic and detailed features of the EEG data were adaptively extracted by multi-scale convolution module, and the features were fused and filtered by parallel residual module and channel attention. Finally, classification results were output by a fully connected network. The application experimental results on the BCI Competition IV 2a and 2b datasets showed that the proposed model achieved an average classification accuracy of 91.87% and 87.85% for the motor imagery task, respectively, which had high accuracy and strong robustness compared with existing baseline models. The proposed model does not require complex signals pre-processing operations and has the advantage of multi-scale feature extraction, which has high practical application value.

Existing neuroregulatory techniques can achieve precise stimulation of the whole brain or cortex, but high-focus deep brain stimulation has been a technical bottleneck in this field. In this paper, based on the theory of negative permeability emerged in recent years, a simulation model of magnetic replicator is established to study the distribution of the induced electric field in the deep brain and explore the possibility of deep focusing, which is compared with the traditional magnetic stimulation method. Simulation results show that a single magnetic replicator realized remote magnetic source. Under the condition of the same position and compared with the traditional method of stimulating, the former generated smaller induced electric field which sharply reduced with distance. By superposition of the magnetic field replicator, the induced electric field intensity could be increased and the focus could be improved, reducing the number of peripheral wires while guaranteeing good focus. The magnetic replicator model established in this paper provides a new idea for precise deep brain stimulation, which can be combined with neuroregulatory techniques in the future to lay a foundation for clinical application.

Electroencephalogram ( EEG) is characterized by high temporal resolution, and various EEG analysis methods have developed rapidly in recent years. The EEG microstate analysis method can be used to study the changes of the brain in the millisecond scale, and can also present the distribution of EEG signals in the topological level, thus reflecting the discontinuous and nonlinear characteristics of the whole brain. After more than 30 years of enrichment and improvement, EEG microstate analysis has penetrated into many research fields related to brain science. In this paper, the basic principles of EEG microstate analysis methods are summarized, and the changes of characteristic parameters of microstates, the relationship between microstates and brain functional networks as well as the main advances in the application of microstate feature extraction and classification in brain diseases and brain cognition are systematically described, hoping to provide some references for researchers in this field.

Constrained spherical deconvolution can quantify white matter fiber orientation distribution information from diffusion magnetic resonance imaging data. But this method is only applicable to single shell diffusion magnetic resonance imaging data and will provide wrong fiber orientation information in white matter tissue which contains isotropic diffusion signals. To solve these problems, this paper proposes a constrained spherical deconvolution method based on multi-model response function. Multi-shell data can improve the stability of fiber orientation, and multi-model response function can attenuate isotropic diffusion signals in white matter, providing more accurate fiber orientation information. Synthetic data and real brain data from public database were used to verify the effectiveness of this algorithm. The results demonstrate that the proposed algorithm can attenuate isotropic diffusion signals in white matter and overcome the influence of partial volume effect on fiber direction estimation, thus estimate fiber direction more accurately. The reconstructed fiber direction distribution is stable, the false peaks are less, and the recognition ability of cross fiber is stronger, which lays a foundation for the further research of fiber bundle tracking technology.

In recent years, due to the emergence of ultrafast ultrasound imaging technology, the sensitivity of detecting slow and micro blood flow with ultrasound has been dramatically improved, and functional ultrasound imaging (fUSI) has been developed. fUSI is a novel technology for neurological imaging that utilizes neurovascular coupling to detect the functional activity of the central nervous system (CNS) with high spatiotemporal resolution and high sensitivity, which is dynamic, non-invasive or minimally invasive. fUSI fills the gap between functional magnetic resonance imaging (fMRI) and optical imaging with its high accessibility and portability. Moreover, it is compatible with electrophysiological recording and optogenetics. In this paper, we review the developments of fUSI and its applications in neuroimaging. To date, fUSI has been used in various animals ranging from mice to non-human primates, as well as in clinical surgeries and bedside functional brain imaging of neonates. In conclusion, fUSI has great potential in neuroscience research and is expected to become an important tool for neuroscientists, pathologists and pharmacologists.

Traditional depression research based on electroencephalogram (EEG) regards electrodes as isolated nodes and ignores the correlation between them. So it is difficult to discover abnormal brain topology alters in patients with depression. To resolve this problem, this paper proposes a framework for depression recognition based on brain function network (BFN). To avoid the volume conductor effect, the phase lag index is used to construct BFN. BFN indexes closely related to the characteristics of "small world" and specific brain regions of minimum spanning tree were selected based on the information complementarity of weighted and binary BFN and then potential biomarkers of depression recognition are found based on the progressive index analysis strategy. The resting state EEG data of 48 subjects was used to verify this scheme. The results showed that the synchronization between groups was significantly changed in the left temporal, right parietal occipital and right frontal, the shortest path length and clustering coefficient of weighted BFN, the leaf scores of left temporal and right frontal and the diameter of right parietal occipital of binary BFN were correlated with patient health questionnaire 9-items (PHQ-9), and the highest recognition rate was 94.11%. In addition, the study found that compared with healthy controls, the information processing ability of patients with depression reduced significantly. The results of this study provide a new idea for the construction and analysis of BFN and a new method for exploring the potential markers of depression recognition.

Sleep is a complex physiological process of great significance to physical and mental health, and its research scope involves multiple disciplines. At present, the quantitative analysis of sleep mainly relies on the "gold standard" of polysomnography (PSG). However, PSG has great interference to the human body and cannot reflect the hemodynamic status of the brain. Functional near infrared spectroscopy (fNIRS) is used in sleep research, which can not only meet the demand of low interference to human body, but also reflect the hemodynamics of brain. Therefore, this paper has collected and sorted out the related literatures about fNIRS used in sleep research, concluding sleep staging research, clinical sleep monitoring research, fatigue detection research, etc. This paper provides a theoretical reference for scholars who will use fNIRS for fatigue and sleep related research in the future. Moreover, this article concludes the limitation of existing studies and points out the possible development direction of fNIRS for sleep research, in the hope of providing reference for the study of sleep and cerebral hemodynamics.

Brain image segmentation algorithm based on deep learning is a research hotspot at present. In this paper, firstly, the significance of brain image segmentation and the content of related brain image segmentation algorithm are systematically described, highlighting the advantages of brain image segmentation algorithms based on deep learning. Then, this paper introduces current brain image segmentation algorithms based on deep learning from three aspects: the brain image segmentation algorithms based on problems existent to brain image, the brain image segmentation algorithms based on prior knowledge guidance and the application of general deep learning models in brain image segmentation, so as to enable researchers in relevant fields to understand current research progress more systematically. Finally, this paper provides a general direction for the further research of brain image segmentation algorithm based on deep learning.

Electroencephalography (EEG) signals are strongly correlated with human emotions. The importance of nodes in the emotional brain network provides an effective means to analyze the emotional brain mechanism. In this paper, a new ranking method of node importance, weighted K -order propagation number method, was used to design and implement a classification algorithm for emotional brain networks. Firstly, based on DEAP emotional EEG data, a cross-sample entropy brain network was constructed, and the importance of nodes in positive and negative emotional brain networks was sorted to obtain the feature matrix under multi-threshold scales. Secondly, feature extraction and support vector machine (SVM) were used to classify emotion. The classification accuracy was 83.6%. The results show that it is effective to use the weighted K -order propagation number method to extract the importance characteristics of brain network nodes for emotion classification, which provides a new means for feature extraction and analysis of complex networks.

The research on brain functional mechanism and cognitive status based on brain network has the vital significance. According to a time-frequency method, partial directed coherence (PDC), for measuring directional interactions over time and frequency from scalp-recorded electroencephalogram (EEG) signals, this paper proposed dynamic PDC (dPDC) method to model the brain network for motor imagery. The parameters attributes (out-degree, in-degree, clustering coefficient and eccentricity) of effective network for 9 subjects were calculated based on dataset from BCI competitions IV in 2008, and then the interaction between different locations for the network character and significance of motor imagery was analyzed. The clustering coefficients for both groups were higher than those of the random network and the path length was close to that of random network. These experimental results show that the effective network has a small world property. The analysis of the network parameter attributes for the left and right hands verified that there was a significant difference on ROI2 ( P = 0.007) and ROI3 ( P = 0.002) regions for out-degree. The information flows of effective network based dPDC algorithm among different brain regions illustrated the active regions for motor imagery mainly located in fronto-central regions (ROI2 and ROI3) and parieto-occipital regions (ROI5 and ROI6). Therefore, the effective network based dPDC algorithm can be effective to reflect the change of imagery motor, and can be used as a practical index to research neural mechanisms.

This study uses mind-control game training to intervene in patients with mild cognitive impairment to improve their cognitive function. In this study, electroencephalogram (EEG) data of 40 participants were collected before and after two training sessions. The continuous complexity of EEG signals was analyzed to assess the status of cognitive function and explore the effect of mind-control game training on the improvement of cognitive function. The results showed that after two training sessions, the continuous complexity of EEG signal of the subject increased (0.012 44 ± 0.000 29, P < 0.05) and amplitude of curve fluctuation decreased gradually, indicating that with increase of training times, the continuous complexity increased significantly, the cognitive function of brain improved significantly and state was stable. The results of this paper may show that mind-control game training can improve the status of the brain cognitive function, which may provide support and help for the future intervention of cognitive dysfunction.

Selective attention promotes the perception of brain to outside world and coordinates the allocation of limited brain resources. It is a cognitive process which relies on the neural activities of attention-related brain network. As one of the important forms of brain activities, neural oscillations are closely related to selective attention. In recent years, the relationship between selective attention and neural oscillations has become a hot issue. The new method that using external rhythmic stimuli to influence neural oscillations, i.e., neural entrainment, provides a promising approach to investigate the relationship between selective attention and neural oscillations. Moreover, it provides a new method to diagnose and even to treat the attention dysfunction. This paper reviewed the research status on the relationship between selective attention and neural oscillations, and focused on the application prospects of neural entrainment in revealing this relationship and diagnosing, even treating the attention dysfunction.

This paper is aimed to analyze the topological properties of structural brain networks in depressive patients with and without anxiety and to explore the neuropath logical mechanisms of depression comorbid with anxiety.Diffusion tensor imaging and deterministic tractography were applied to map the white matter structural networks.We collected 20 depressive patients with anxiety(DPA),18 depressive patients without anxiety(DP),and 28 normal controls(NC)as comparative groups.The global and nodal properties of the structural brain networks in the three groups were analyzed with graph theoretical methods.The result showed that1 the structural brain networks in three groups showed small-world properties and highly connected global hubs predominately from association cortices;2DP group showed lower local efficiency and global efficiency compared to NC group,whereas DPA group showed higher local efficiency and global efficiency compared to NC group;3significant differences of network properties(clustering coefficient,characteristic path lengths,local efficiency,global efficiency)were found between DPA and DP groups;4DP group showed significant changes of nodal efficiency in the brain areas primarily in the temporal lobe and bilateral frontal gyrus,compared to DPA and NC groups.The analysis indicated that the DP and DPA groups showed nodal properties of the structural brain networks,compared to NC group.Moreover,the two diseased groups indicated an opposite trend in the network properties.The results of this study may provide a new imaging index for clinical diagnosis for depression comorbid with anxiety.

Overexcitation of neurons in brain can lead to epilepsy seizures,and the key to control epilepsy seizures is to keep the balance between excitation and inhibition.In this paper,epileptiform index is presented to denote the seizure degree and used as control variable of PID controller to control epilepsy seizures.Neural mass model(NMM)is used as a test-bed to simulate the change of seizure degree with the increase of excitatory strength and two control strategies.Experimental results showed that the increase of excitatory strength could lead to a substantial increase of epileptiform index and trigger seizures.PID controller which is used to decrease excitatory strength or increase inhibitory strength can keep excitation-inhibition balance and inhibit epilepsy seizures.Epileptiform index can describe the linear and nonlinear feature of electroencephalogram(EEG)comprehensively,and PID controller is simple and independent of underlying physiological structure,which lays the foundation for its application in the clinic.

Fatigue is an exhaustion state caused by prolonged physical work and mental work, which can reduce working efficiency and even cause industrial accidents. Fatigue is a complex concept involving both physiological and psychological factors. Fatigue can cause a decline of concentration and work performance and induce chronic diseases. Prolonged fatigue may endanger life safety. In most of the scenarios, physical and mental workloads co-lead operator into fatigue state. Thus, it is very important to study the interaction influence and its neural mechanisms between physical and mental fatigues. This paper introduces recent progresses on the interaction effects and discusses some research challenges and future development directions. It is believed that mutual influence between physical fatigue and mental fatigue may occur in the central nervous system. Revealing the basal ganglia function and dopamine release may be important to explore the neural mechanisms between physical fatigue and mental fatigue. Future effort is to optimize fatigue models, to evaluate parameters and to explore the neural mechanisms so as to provide scientific basis and theoretical guidance for complex task designs and fatigue monitoring.

Magnetic resonance (MR) images can be used to detect lesions in the brains of patients with multiple sclerosis (MS). An automatic method is presented for segmentation of MS lesions using multispectral MR images in this paper. Firstly, a Pd-w image is subtracted from its corresponding T1-w images to get an image in which the cerebral spinal fluid (CSF) is enhanced. Secondly, based on kernel fuzzy c-means clustering (KFCM) algorithm, the enhanced image and the corresponding T2-w image are segmented respectively to extract the CSF region and the CSF-MS lesions combinatoin region. A raw MS lesions image is obtained by subtracting the CSF region from CSF-MS region. Thirdly, based on applying median filter and thresholding to the raw image, the MS lesions were detected finally. Results were tested on BrainWeb images and evaluated with Dice similarity coefficient (DSC), sensitivity (Sens), specificity (Spec) and accuracy (Acc). The testing results were satisfactory.

Brain-computer interface (BCI) systems identify brain signals through extracting features from them. In view of the limitations of the autoregressive model feature extraction method and the traditional principal component analysis to deal with the multichannel signals, this paper presents a multichannel feature extraction method that multivariate autoregressive (MVAR) model combined with the multiple-linear principal component analysis (MPCA), and used for magnetoencephalography (MEG) signals and electroencephalograph (EEG) signals recognition. Firstly, we calculated the MVAR model coefficient matrix of the MEG/EEG signals using this method, and then reduced the dimensions to a lower one, using MPCA. Finally, we recognized brain signals by Bayes Classifier. The key innovation we introduced in our investigation showed that we extended the traditional single-channel feature extraction method to the case of multi-channel one. We then carried out the experiments using the data groups of IV-III and IV - I. The experimental results proved that the method proposed in this paper was feasible.

In this paper, the Fourier transform based minimum mean square error (FT-based MMSE) method is used to calculate the regional cerebral blood volume (rCBV) in magnetic resonance (MR) perfusion imaging, and the method is improved to handle the existing noise in the imaging process. In the experiments with signal-to-noise ratio (SNR) of 50 dB, the rCBV values were compared with the results using MMSE method. The effects of different SNRs on the estimation of rCBV were analyzed. The experimental results showed that MMSE was a simple way to filter the measurement noise, and could calculate rCBV accurately. Compared with other existing methods, the present method is not sensitive to environment, and furthermore, it is suitable to deal with the perfusion images acquired from the environment with larger SNR.

Post-traumatic stress disorder (PTSD) is a mental disorder causing great distress to individuals, families and even society, and there is not yet effective way of unified prevention and treatment up till now. Lots of neuroimaging techniques, however, such as the magnetic resonance imaging, are widely used to the study of the pathogenesis of PTSD with the development of medical imaging. Functional magnetic resonance imaging (fMRI) can be applied to detect the abnormalities not only of the brain morphology but also of the function of various cerebral areas and neural circuit, and plays an important role in studying the pathogenesis of psychiatric diseases. In this paper, we mainly review the task-related and resting-state functional magnetic resonance imaging studies of the PTSD, and finally suggest possible directions for future research.

At present, an effective detecting method for brain function impairment for the patients with the glioma is urgently needed in clinic, because it may help us understand its pathogenesis. This paper proposes a method of combining diffusion tensor tracing technology and 'small world' network. It utilizes the degree of brain function network to study complex network topological properties of the patients with the glioma in temporal lobe area. The experimental results showed that the brain networks of the patients with the glioma of different grades were destroyed compared with those of the normal persons, but the destruction degree is independent of the tumor grades. The distribution of functional connections is index truncated power-law accompanied by significant heterogeneity. Meanwhile, the stronger functional areas of information in the glioma have transferred and there exists lack of language function area and sensory function area.

Aiming at local field potential, the present paper introduces a method of estimating lag of neuron activities between brain areas based on windowed Harmonic wavelet transform (WHWT). Firstly, the WHWT of signals of two brain areas are calculated. Secondly, the instantaneous amplitude of the signals is calculated and finally, these amplitudes are cross-correlated and the lag at which the cross-correlation peak occurs is determined as the lag of neurons activities. Comparing with amplitude cross-correlation based on Gabor wavelet transform (GWT) or Hilbert transform (HT), this method is more precise and efficient in estimating the directionality and lag.

Granger causality model is an analysis method that requires no priori knowledge and emphasizes time sequence. Such model applied to brain effective connectivity network can reflect the directional connectivity among brain regions or neurons. This paper reviews the principle of Granger causality model, basic test steps and improved models, analyzes and discusses applications and existing problems of Granger causality model in brain effective connectivity network.

In order to choose a fast and efficient real-time method in beta wave information extraction, we compared the result and the efficiency of the information separation of both fast Fourier transform (FFT) and wavelet transform of EEG beta band in the present paper. Our work provides the basis for the EEG data come from the real-time health assessment of 3DTV. We took the EEGs of 5 healthy volunteers before, after and during the process of watching 3DTV and meanwhile recorded the results. The trends of the relative energy and the time cost of two methods were compared by using both the FFT and wavelet packet transform (WPT) which was to extract the feature of EEG beta wave. It demonstrated that (1) Results of the two methods were consistent in the trends of watching 3DTV; (2) Results of the differences in two methods were consistent before and after watching 3DTV; (3) FFT took less time than the wavelet transform in the same case. It is concluded that the results of both FFT and Wavelet transform are consistent in feature extraction of EEG, and a fast method to work with the large quantities of EEG data obtained in the experiments can be offered in the future.

Vigilance is defined as the ability to maintain attention or alertness over prolonged periods of time. Since Mid-20th century, following the increasing man-machine communication, high level of vigilance has been demanded in many areas including driving safety, medical care and therapy, aerospace and military affairs, etc. Therefore, finding quick methods to improve the level of vigilance has become a key issue in medical study. Based on physical regulation, chemical regulation and physiological regulation, the research progress has been summarized in this paper. We, furthermore, also try to predict the future trend in this academic area and develop some tentative ideas about seeking more effective and convenient ways to improve the level of brain vigilance.

Using human electroencephalogram (EEG) to control external devices in order to achieve a variety of functions has been focus of the field of brain-computer interface (BCI) research. P300 is experiments which stimulate the eye to produce EEG by using letters flashing, and then identify the corresponding letters. In this paper, some improvements based on the P300 experiments were made??. Firstly, the matrix of flashing letters were modified into words which represent a certain sense. Secondly, the BCI2000 procedures were added with the corresponding source code. Thirdly, the smart car systems were designed using the radiofrequency signal. Finally it was realized that the evoked potentials were used to control the state of the smart car.

This paper introduces the characteristics of the Hilbert-Huang transform (HHT), and studies the classification of movement imagery EEG based on the HHT method and BP neural network. After preprocessed, the movement imagery EEG data were descomposed with empirical mode decomposition (EMD) into a series of intrinsic mode functions (IMFs). Then the low frequency IMFs were removed, and the rest of IMFs were conducted by Hilbert transform to get Hilbert marginal spectrum. The marginal spectrum subtracted values between the channal C3 and channal C4 were selected as the original features which were then decreased the dimension by the principal components analysis so as to be jointed with EEG complexity to construct the feature vector. The BP neural network was utilized to classify the EEG pattern of left and right hand motor imagery. The brain computer interface (BCI) competition II data set III was selected to carry out the discrimination, and the classification accuracy rate is up to 87.14%, which is a comparably good result and proves HHT to be a feasible and effective method on EEG analysis.

In this paper, time courses of discrete cortical current sources evoked by target and distractor stimulus are presented in a visual three-stimulus oddball paradigm. 64-channel electroencephalogram (EEG) signals were recorded in healthy subjects in the paradigm where the P3b component of the P300 is evoked in the detection of rare events (target and distractor) and the P3a component is mainly produced by distractor events. A regional source model with constraints of spatial coordinates from fMRI was then applied to event-related potentials (ERPs) data in the target and distractor conditions. Activities of regional sources indicated bilateral inferior parietal lobe, posterior parietal cortex and inferior temporal cortex mainly contribute to the P3b, while the P3a was mainly produced by bilateral insula, bilateral precentral sulcus and cingulate gyrus. Target processing involved parietal lobe, inferior temporal cortex and left insula engaged in stimulus-driven attention process, goal-directed attention process, categorization of visual stimuli and memory retrieval, while distractor processing involved right insula and cingulate gyrus engaged in attention switching and reengagement of attention resource.

EEG Synchronization is considered the conformity of the brain functional areas. Advanced brain function requires many nervous systems with a specific function in relevant brain regions (areas) to achieve integration and coordination at different levels. In this paper, a new method for phase synchronization analysis-Mutually Approximate Entropy is proposed to process different frequency band of EEG signal during audio-visual stimulation and get Similar results with the method of Synchronization Index and Mutual Information Entropy. This showed that the Mutually Approximate Entropy can lead to a good indication of the phase synchronization between two leads. The paper also explored the brain reaction zone by the results of the phase synchronization analysis. The research work lays the foundation for the brain-computer interface design.

Vigilance is the body level of awareness for objective things. It has been used in security, medical and other fields since people used it as an objective indicator. Therefore automatical vigilance detection has become a major issue needed to be resolved as soon as possible. The methods of vigilance detection at home and abroad in recent years was analyzed in this paper, which will benefit the research and the people dedicated in vigilance detection.

To relax the presumption of strictly linear patterns in the common spatial patterns (CSP), we studied the kernel CSP (KCSP). A new multi-class KCSP (MKCSP) approach was proposed in this paper, which combines the kernel approach with multi-class CSP technique. In this approach, we used kernel spatial patterns for each class against all others, and extracted signal components specific to one condition from EEG data sets of multiple conditions. Then we performed classification using the Logistic linear classifier. Brain computer interface (BCI) competition III&#95;3a was used in the experiment. Through the experiment, it can be proved that this approach could decompose the raw EEG singles into spatial patterns extracted from multi-class of single trial EEG, and could obtain good classification results.

In order to increase the accuracy of image guided neurosurgery (IGNS), we developed a brain deformation corrective system (BDCS) based on linear elastic model. To investigate the fidelity of such a system, two cases of cerebral tumor resection were conducted in our paper. According to our experiments, the average shift error after craniotomy was reduced to (1.00 +/- 0.63)mm from (4. 97 +/- 3. 53)mm and the average prediction rate was (72.27 +/- 20.37)%. The results showed that the system was convenient and fast to operate and presented a promising pathway to compensating for brain deformation during neurosurgery.

The long-period CT perfusion imaging leads to an excess amount of radiation dose to the patient. However, the radiation dose could be significantly reduced if a previous normal-dose image is acquired before a set of low-dose scans of perfusion, and a filtering processing is performed on the differences between the current low-dose images and the previous normal-dose image, then the results are added to the previous image. But the selection of plenty of parameters makes the algorithm complicated. This paper proposes an innovative approach performed in sinogram domain instead of in image domain. First a normal-dose image and a set of low-dose projection data are acquired before the perfusion. Second the perfusion information is commendably reconstructed with sparsity constraints of the differences between current low-dose perfusion sinograms and previous low-dose sinogram. Finally, the reconstructed perfusion information is added to the previous normal-dose image. The proposed method was validated by simulated experiments with a set of brain CT perfusion images, which showed that the new method provided more accurate perfusion information; the time-attenuation curve was more close to that for normal-dose scan and the mean transit time more repeatable.

The magnetoencephalography (MEG) can be used as a control signal for brain computer interface (BCI). The BCI also includes the pattern information of the direction of hand movement. In the MEG signal classification, the feature extraction based on signal processing and linear classification is usually used. But the recognition rate has been difficult to improve. In the present paper, a principal component analysis (PCA) and linear discriminant analysis (LDA) method has been proposed for the feature extraction, and the non-linear nearest neighbor classification is introduced for the classifier. The confusion matrix is analyzed based on the results. The experimental results show that the PCA + LDA method is effective in the analysis of multi-channel MEG signals, improves the recognition rate to the extent of the average recognition rate 55.7%, which is better than the recognition rate 46.9% in the BCI competition IV.

Suicide rate in depression is higher than that in general population, therefore, it is a major topic how to prevent the suicide in depression in clinic. In this paper, we review the risk factors in depression with suicidal behaviors and also the latest progress in neuroimaging, neuroendocrinology and molecular genetics, which may reveal the potential neurobiological mechanism of suicide in depression, and thus help the prevention of suicidality and further research.

This paper is focused on head modeling using the sophisticated realistic finite element method (FEM) with five-layer tissues based on segmented data. First, the location of every pixel of the FEM head model was fixed on according to the registration method based on the magnetic resonance imaging (MRI) and computer tomography (CT) images. Then the location information was rebuilt with the hexahedron pattern in the ANSYS FEM software. Finally, the forward problem numerical computation was performed on this FEM head model. The simulation results verified the rationality and reliability of the model applied on the electroencephalograph (EEG)/magnetoencephalograph (MEG) study. The hexahedron meshing realistic head model combined with the MRI scanner information has potential in the future research of EEG/MEG.

The present paper is aimed to study the fractal spectrum of the cerebral computerized tomography in 158 normal infants of different age groups, based on the calculation of chaotic theory. The distribution range of neonatal period was 1.88-1.90 (mean = 1.8913 +/- 0.0064); It reached a stable condition at the level of 1.89-1.90 during 1-12 months old (mean = 1.8927 +/- 0.0045); The normal range of 1-2 years old infants was 1.86-1.90 (mean = 1.8863 +/- 4 0.0085); It kept the invariance of the quantitative value among 1.88-1.91(mean = 1.8958 +/- 0.0083) during 2-3 years of age. ANOVA indicated there's no significant difference between boys and girls (F = 0.243, P > 0.05), but the difference of age groups was significant (F = 8.947, P < 0.001). The fractal dimension of cerebral computerized tomography in normal infants computed by box methods was maintained at an efficient stability from 1.86 to 1.91. It indicated that there exit some attractor modes in pediatric brain development.

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.

Brain-computer interface (BCI) is a system that can create direct connection between brain activity and external devices. In the past 20 years, important' achievements of research on BCI have been made. Now there are lots of research methods based on electroencephalic signals, and researchers are trying to make the BCI system possess the characteristics of real-time and become more natural and practical. This paper presents an overview of real-time and stimulating way to EEG-based BCI research. Through the discussions on the applications of DSP in BCI system, in signal preprocessing and in algorithm optimization, the high lights in real-time research are pointed out. In the discussions about the way to produce EEG signals in BCI, the researchers suggested that the imaging movement be the most ideal way in that it will reduce the discomfort in stimulation by application of the virtual reality technology in BCI system, thus it will be conducive to improvement in the performance of BCI system.

Synchronous brain activities are considered as an indicator of the functional binding or integration. In this paper, based on the neural electric activities at various spatial scales, we explained the basic concept and measuring index of neural synchronization; then we summarized the main signal processing methods developed for phase synchronization analysis of electroencephalograph (EEG), including the conventional signal processing method, and modern signal modeling approaches. Finally, the main differences among the different methods were compared and some critical problems in the study of EEG synchronization were discussed.

The robust extracting of evoked potential (EP) has become a vexed question in the process of electroencephalogram owing to the faint signal-to-noise ratio of EP. This paper presents the single trial of EP in time, transform and space field. Several methods such as adaptive filter, wavelet transform, principal component analysis (PCA) and independent component analysis (ICA) have been applied to the process of EP.

In this paper is proposed a new method for functional connectivity detection using regression analysis. First, the resting state functional magnetic resonance imaging (fMRI) data from each voxel is passed through a bandpass filter to obtain frequencies between 0.01 Hz and 0.1 Hz. Then, the region of interest (ROI) is defined and the mean time course of all voxels in ROI is used as a regressor. Finally, the linear relationship between the time course of other voxels and the regressor in the resting brain is estimated. By the application of this new method in simulation data and fMRI data, the relevant validity and reliability are demonstrated.

The resting state cortical functional connectivity is an important method in current brain researches. In this paper, we propose an approach for analyzing and manipulating the resting state functional magnetic resonance imaging (fMRI) data using spatial independent component analysis (sICA) method, and applying the low-frequency oscillations theory to the choice of component of interest (COI) from the component obtained by sICA method. Firstly, we remove all the inactive voxels and independent voxels via Z value. Then, by making a spectrum analysis, we choose the COI with concentrations of energy between 0.01 and 0.1 Hz. And after that, we obtain the functional connectivity networks using hierarchical clustering.

This is a research carried out to explore a pragmatic way of BCI based imaging movement, i. e. to extract the feature of EEG for reflecting different thinking by searching suitable methods of signal extraction and recognition algorithm processing, to boost the recognition rate of communication for BCI system, and finally to establish a substantial theory and experimental support for BCI application. In this paper, different mental tasks for imaging left-right hands movement from 6 subjects were studied in three different time sections (hint keying at 2s, 1s and 0s after appearance of arrow). Then we used wavelet analysis and Feed-forward Back-propagation Neural Network (BP-NN) method for processing and analyzing the experimental data of off-line. Delay time delta t2, delta t1 and delta t0 for all subjects in the three different time sections were analyzed. There was significant difference between delta to and delta t2 or delta t1 (P<0.05), but no significant difference was noted between delta t2 and delta t1 (P>0.05). The average results of recognition rate were 65%, 86.67% and 72%, respectively. There were obviously different features for imaging left-right hands movement about 0.5-1s before actual movement; these features displayed significant difference. We got higher recognition rate of communication under the hint keying at about 1s after the appearance of arrow. These showed the feasibility of using the feature signals extracted from the project as the external control signals for BCI system, and demon strated that the project provided new ideas and methods for feature extraction and classification of mental tasks for BCI.

The finite element method (FEM) plays an important role in solving the brain deformation problem in the image guided neurosurgery system. The position of the brain cortex during the surgery provides the boundary condition for the FEM model. In this paper, the information of brain cortex is represented by the unstructured points and the boundary condition is achieved by the processing of unstructured points. The processing includes the mapping of texture, segmentation, simplification and denoising. The method of k-nearest clustering based on local surface properties is used to simplify and denoise the unstructured point clouds. The results of experiment prove the efficiency of point clouds processing.

A modified Snake algorithm for medical image segmentation based on improved Greedy method and electrostatic field model is presented in this paper. Based on Greedy method, this algorithm features a new adjacent point selection strategy and the corresponding criteria, which can be used for searching the potential snake points. A new external image force based on electrostatic field model and the simplified force field computation based on preprocessing image are also introduced. Comparative experiments indicate the validity of the method.

A novel multi-spectral MR image segmentation method based on multi-modality image fusion is presented in this paper. The basic idea of the method is that every single image is clustered using the FCM clustering method first, and then the final clustering results can be obtained by data fusion. The experimental results show that the presented method separates the white matter, gray matter and cerebro-spinal fluid (CSF) more accurately than does any single image segmentation method.