Analysis of a phase synchronized functional network based on the rhythm of brain activities

Rhythm of brain activities represents oscillations of postsynaptic potentials in neocortex, therefore it can serve as an indicator of the brain activity state. In order to check the connectivity of brain rhythm, this paper develops a new method of constructing functional network based on phase synchronization. Electroencephalogram （EEG） data were collected while subjects looking at a green cross in two states, performing an attention task and relaxing with eyes-open. The EEG from these two states was filtered by three band-pass filters to obtain signals of theta （4-7 Hz）, alpha （8-13 Hz） and beta （14-30 Hz） bands. Mean resultant length was used to estimate strength of phase synchronization in three bands to construct networks of both states, and mean degree K and cluster coefficient C of networks were calculated as a function of threshold. The result shows higher cluster coetticient in the attention state than in the eyes-open state in all three bands, suggesting that cluster coefficient reflects brain state. In addition, an obvious fronto-parietal network is found in the attention state, which is a well-known attention network. These results indicate that attention modulates the fronto-parietal connectivity in different modes as compared with the eyes-open state. Taken together this method is an objective and important tool to study the properties of neural networks of brain rhythm,

A Study of Electromyogram Based on Human-Computer Interface

In this paper, a new control system based on forearm electromyogram （EMG） is proposed for computer peripheral control and artificial prosthesis control. This control system intends to realize the commands of six pre-defined hand poses： up, down, left, right, yes, and no. In order to research the possibility of using a unified amplifier for both electroencephalogram （EEG） and EMG, the surface forearm EMG data is acquired by a 4-channel EEG measurement system. The Bayesian classifier is used to classify the power spectral density （PSD） of the signal. The experiment result verifies that this control system can supply a high command recognition rate （average 48%） even the EMG data is collected with an EEG system just with single electrode measurement.

Entropy Production Rate Changes in Lysogeny/Lysis Switch Regulation of Bacteriophage Lambda

According to the chemical kinetic model of lysogeny/lysis switch in Escherichia coli （E. coil） infected by bacteriophage A, the entropy production rates of steady states are calculated. The resuits show that the lysogenic state has lower entropy production rate than lyric state, which provides an explanation on why the lysogenic state of A phage is so stable. We a/so notice that the entropy production rates of both lysogenic state and lyric state are lower than that of saddle-point and bifurcation state, which is consistent with the principle of minimum entropy production for living organism in nonequilibrium stationary state. Subsequently, the relations between CI and Cro degradation rates at two bifurcations and the changes of entropy production rate with CI and Cro degradation are deduced. The theory and method can be used to calculate entropy change in other molecular network.

Study on the Location of the Activated Cerebellum Regions in Manual Movement Related to Handedness

The present study used functional magnetic resonance imaging （fMRI） to study the activated site in the cerebellum during bi-hand movement and uni-hand movement, and the lateralization of the cerebellum in hand movement. The coordinates of the peak activation in the CRB were compared using statistical tests, and the results in the primary motor cortex （PMC） were presented as comparison. The results showed a significant difference in cerebellum during bi-hand movement comparing the right-handed with the left-handed subjects. When comparing the right-handed subjects and left-handed subjects during right-hand movement, the significantly different coordinate in dorsal/ventral orientation was observed. The results also showed the different activated site during bi-hand movement and non-dominant hand movement. Our study took the point of view of the lateralization in cerebellum, and the results demonstrated the different site of the peak activation in cerebellum during bi-hand movement and uni-hand movement, which suggested that the handedness also existed in the cerebellum.

Estimation of the Hemodynamic Response during Motor Imagery Using Bayesian RBF Neural Network

Hemodynamic response during motor imagery （MI） is studied extensively by functional magnetic resonance imaging （fMRI） technologies. To further understand the human brain functions under MI, a more precise classification of the brain regions corresponding to each brain function is desired. In this study, a Bayesian trained radial basis function （RBF） neural network, which determines the weights and regularization parameters automatically by Bayesian learning, is applied to make a precise classification of the hemodynamic response to the tasks during the MI experiment. To illustrate the proposed method, data with MI task performance from 1 subject was used. The results demonstrate that this approach splits the hemodynamic response to different tasks successfully.

Study of Relation between EFL Students'Cognitive Style and Pragmatic Competence

Individual difference is one of the important factors affecting the success of learning English as foreign language（EFL）. However, very little research has dabbled in the correlation between pragmatic competence and cognitive styles among college students. Two questionnaires（the embedded figure test and the pragmatic competence test） were designed and used to conduct our study among seniors with non-English majors from key universities. The data was analyzed with SPSS 21.0 and results showed that pragmatic competence of the subjects was much lower than expected. Besides, there was a significant and positive correlation between field dependent（FD）cognitive style and inter-language pragmatic competence while no significant correlation was found with field independent（FI）. A regression model was also formulated to obtain more specific information. Lastly,some practical advice is offered for teachers to boost students＇ pragmatic competence in the future with FD/FI cognitive styles into proper consideration.

Effect of Level of Processing Prior to Response on Cognitive Control:Evidence from ERP

Based on the idea that intentions have different penetrability to perception and thought, four Stroop-like tasks, AA, AW, WA, and WW are used. Event-related brain potentials are recorded as participants completed these tasks, and standardized low resolution brain electromagnetic tomography （sLORETA） is used to localize the sources at specific time points. These results show that there is an interference effect in the AA and WA tasks, but not in the AW or WW tasks. The activated brain areas related to the interference effect in the AA task are the PFC （prefrontal cortex） and ACC （anterior cingulated cortex）, and PFC aetivation takes place prior to ACC activation, but only in WA task. Combined with previous results, a new neural mechanism of cognitive control is proposed.

fMRI Study Revealing Neural Mechanisms of the Functions of SOA in Spatial Orienting

It is well documented that orienting attention plays an important role in visual search. However, it remains unclear how the executive brain regions will act when two different stimulus onset asynchrony （SOA） are used in visual search. In this work, event-related fMRI was used to investigate neural mechanisms on the functions of SOA in endogenous and exogenous orienting. The results showed that in the endogenous orienting, long SOA versus short SOA resulted in widespread cortical activation mainly including right medial frontal gyrus and bilateral middle frontal gyri. Conversely, in exogenous orienting, long SOA compared to short SOA resulted in only activations in bilateral middle frontal gyri. These findings indicated that these two spatial orienting involved different brain areas and neural mechanisms.

A review of functional magnetic resonance imaging for Brainnetome

The functional brain network using blood-oxygen-level-dependent（BOLD） functional magnetic resonance imaging（fMRI） has revealed the potentials for probing brain architecture,as well as for identifying clinical biomarkers for brain diseases.In the general context of Brainnetome,this review focuses on the development of approaches for modeling and analyzing functional brain networks with BOLD fMRI.The prospects for these approaches are also discussed.

Function projective synchronization between two different complex networks with correlated random disturbances

Although function projective synchronization in complex dynamical networks has been extensively studied in the literature, few papers deal with the problem between two different complex networks with correlated random disturbances. In this paper, we present some novel techniques to analyze the problem of synchronization. A probability approach is introduced to obtain an almost sure synchronization criterion. We also present some efficient approaches to analyze the problem of exponential synchronization. For the problem of synchronization in some complex networks, our approaches not only can replace the LaSalle-type theorem but also allow improvements of existing results in the literature. Finally, some numerical examples are provided to demonstrate the effectiveness of the proposed approaches.

Species Permanence Analysis of an Ecological Model with an Impulsive Control Strategy

In this paper,on the basis of the theories and methods of ecology and ordinary differential equations,an ecological model with an impulsive control strategy is established.By using the theories of impulsive equations,small amplitude perturbation skills and compar-ison technique,we get the condition which guarantees the global asymptotical stability of the prey-x-eradication and predator-y-eradication periodic solution.It is proved that the system is permanent.Furthermore,numerical simulations are also illustrated which agree well with our theoretical analysis.All these results may be useful in study of the dynamic complexity of ecosystems.

An Algorithm for Idle-State Detection and Continuous Classifier Design in Motor-Imagery-Based BCI

Abstract-The development of asynchronous brain-computer interface （BCI） based on motor imagery （M1） poses the research in algorithms for detecting the nontask states （i.e., idle state） and the design of continuous classifiers that classify continuously incoming electroencephalogram （EEG） samples. An algorithm is proposed in this paper which integrates two two-class classifiers to detect idle state and utilizes a sliding window to achieve continuous outputs. The common spatial pattern （CSP） algorithm is used to extract features of EEG signals and the linear support vector machine （SVM） is utilized to serve as classifier. The algorithm is applied on dataset IVb of BCI competition Ⅲ, with a resulting mean square error of 0.66. The result indicates that the proposed algorithm is feasible in the first step of the development of asynchronous systems.

No-Reference Image Quality Assessment Method Based on Visual Parameters

Recent studies on no-reference image quality assessment (NR-IQA) methods usually learn to evaluate the image quality by regressing from human subjective scores of the training samples. This study presented an NR-IQA method based on the basic image visual parameters without using human scored image databases in learning. We demonstrated that these features comprised the most basic characteristics for constructing an image and influencing the visual quality of an image. In this paper, the definitions, computational method, and relationships among these visual metrics were described. We subsequently proposed a no-reference assessment function, which was referred to as a visual parameter measurement index (VPMI), based on the integration of these visual metrics to assess image quality. It is established that the maximum of VPMI corresponds to the best quality of the color image. We verified this method using the popular assessment database—image quality assessment database (LIVE), and the results indicated that the proposed method matched better with the subjective assessment of human vision. Compared with other image quality assessment models, it is highly competitive. VPMI has low computational complexity, which makes it promising to implement in real-time image assessment systems.

Semi-Supervised Learning Based on Manifold in BCI

A Laplacian support vector machine （LapSVM） algorithm, a semi-supervised learning based on manifold, is introduced to brain-computer interface （BCI） to raise the classification precision and reduce the subjects＇ training complexity. The data are collected from three subjects in a three-task mental imagery experiment. LapSVM and transductive SVM （TSVM） are trained with a few labeled samples and a large number of unlabeled samples. The results confirm that LapSVM has a much better classification than TSVM.

Real-Time Brain-Computer Interface System Based on Motor Imagery

Abstract-A brain-computer interface （BCI） real- time system based on motor imagery translates the user＇s motor intention into a real-time control signal for peripheral equipments. A key problem to be solved for practical applications is real-time data collection and processing. In this paper, a real-time BCI system is implemented on computer with electroencephalogram amplifier. In our implementation, the on-line voting method is adopted for feedback control strategy, and the voting results are used to control the cursor horizontal movement. Three subjects take part in the experiment. The results indicate that the best accuracy is 90%.

Towards a better prediction of subcellular location of long non-coding RNA

The spatial distribution pattern of long non-coding RNA(lncRNA)in cell is tightly related to their function.With the increment of publicly available subcellular location data,a number of computational methods have been developed for the recognition of the subcellular localization of lncRNA.Unfortunately,these computational methods suffer from the low discriminative power of redundant features or overfitting of oversampling.To address those issues and enhance the prediction performance,we present a support vector machine-based approach by incorporating mutual information algorithm and incremental feature selection strategy.As a result,the new predictor could achieve the overall accuracy of 91.60%.The highly automated web-tool is available at lin-group.cn/server/iLoc-LncRNA(2.0)/website.It will help to get the knowledge of lncRNA subcellular localization.

Listening to the Brain in Real Time-The Chengdu Brainwave Music

Electroencephalogram （EEG） provides a window for the activity of the human brain. As a novel form of the brain-computer interface （BCI）, the online/offline EEG data may be interpreted through its auditory representation which can be considered as a specific tool in EEG monitoring and analysis. In this work, after a comprehensive comparison of the various designs of brainwave music generations, a waveform event mapping system for music display in real time-- the Chengdu Brainwave Music （CBM） is proposed, which is a special on-line BCI system. In CBM, the user datagram protocol （UDP） is adopted to transport EEG data from the recorder to a music generator. The CBM could possibly be used as an audio feedback tool in BCI, or a monitoring tool in clinic EEG, and a subject specified music therapy method.

The heterogeneity of aging brain： altered functional connectivity in default mode network in older adults during verbal fluency tests

Background Successful aging （SA） and mild cognitive impairment （MCI） are heterogeneous groups of aging.To explore the heterogeneity,the functional connectivity was studied in these populations.Methods The present study utilized functional connectivity magnetic resonance imaging （fcMRI） to investigate default mode network （DMN） in 8 healthy subjects of SA,8 subjects of usual aging （UA）,and 8 MCI patients during verbal fluency tests （VFTs）.Functional connectivity （based seeds） of different groups was analyzed by using statistical test.Results Compared with SA and UA groups,MCI subjects exhibited decreased functional connectivity in the DMN regions,including the inferior parietal Iobule and left angular gyrus （t=3.53,P 〈0.001）.Compared with UA and MCI groups,the SA elderly exhibited increased functional connectivity in the precuneus （t=3.53,P〈0.001）.Conclusions These findings suggested that abnormalities of functional connectivity in DMN might be related with semantic memory impairment in aging.Left angular gyrus and precuneus might be the potential imaging-based biomarker for distinguishing heterogeneous process of elderly.

Authors' response to “Maternal age as a potential explanation of the role of the L allele of the serotonin transporter gene in anxiety and depression in Asians”

In the letter to the editor, Dr. Comings et al. proposed a potential explanation of our findings that the L allele rather than S allele of 5-HTTLPR was associated with higher anxiety levels and reduced amygdala-prefrontal cortex （PFC） connectivity in Han Chinese[1], which demonstrated an ＇allele reversal＇ in the genetics of the 5-HTTLPR gene in Asians versus Caucasians. The authors alleged that this ＇allele reversal＇ might simply result from maternal age and suggested that we test this on our datasets. Unfortunately,

Scale-Free Brain Networks Based on the Event-Related Potential during Visual Spatial Attention

The human brain is thought of as one of the most complex dynamical systems in the universe.The network view of the dynamical system has emerged since the discovery of scale-free networks.Brain functional networks,which represent functional associations among brain regions,are extracted by measuring the temporal correlations from electroencephalogram data.We measure the topological properties of the brain functional network,including degree distribution,average degree,clustering coefficient and the shortest path length,to compare the networks of multi-channel event-related potential activity between visual spatial attention and unattention conditions.It is found that the degree distribution of the brain functional networks under both the conditions is a power law distribution,which reflects a scale-free property.Moreover,the scaling exponent of the attention condition is significantly smaller than that of the unattention condition.However,the degree distribution of equivalent random networks does not follow the power law distribution.In addition,the clustering coefficient of these random networks is smaller than those of brain networks,and the shortest path length of these random networks is large and comparable with those of brain networks.Our results,typical of scale-free networks,indicate that the scaling exponent of brain activity could reflect different cognitive processes.