Search

Emotions processing is a complex mechanism that involves different physiological systems. In particular, the Central Nervous System (CNS) is considered to play a key role in this mechanism and one of the main modalities to study the CNS activity is the Electroencephalographic signal (EEG). To elicit emotions, different kinds of stimuli can be used e.g.: audio, visual or a combination of the two. Literature studies focus more on the correct classification of the different types of emotions or which kind of stimulation gives the best performance in terms of classification accuracy. However, it is still unclear how the different stimuli elicit the emotions and which are the results in terms of brain activity. In this paper, we analysed and compared EEG signals given by eliciting emotions using audio and visual stimuli or a combination of the latter two. Data were collected during experiments conducted in our laboratories using IAPS and IADS dataset. Our study confirmed literature physiological studies about emotions highlighting higher brain activity in the frontal and central regions and in the δ and θ bands for each kind of stimulus. However, audio stimulation was found to have higher responses when compared to the other two modalities of stimulation in almost all the comparisons performed. Higher values of the δ/β ratios, an index related to negative emotions, have been achieved when using only sounds as stimuli. Moreover, the same type of stimuli, resulted in higher δ-β coupling, suggesting a better attention control. We concluded that stimulating subjects without letting them know (seeing) what is actually happening may give a higher perception of emotions, even if this mechanism remains highly subjective. Clinical Relevance- This paper suggests that audio stimuli may give higher perception of the elicited emotion resulting in higher brain activity in the physiological areas and more focused subjects. Thus using only audio in emotion related studies may give more reliable and consistent results.

Limb amputation is a global problem. Prosthetic limbs can enhance the quality of life of amputees. To this end, anthropomorphic design and intuitive manipulation are two essential requirements. This paper presents a motor control framework for prosthetic control through Brain-Machine Interface (BMI) using Finite Automata Theory, and NeuCube Evolving Spiking Neural Network (SNN) architecture. Voluntary control of prosthetics requires decoding motor commands from the Central Nervous System of the amputee. Selection of the most suitable biomedical signal depends on many parameters such as level of amputation and muscle atrophy. Non-invasive BMI's have the possibility of supporting a wider range of amputees as it extracts the motor commands from the brain. In this paper, we present a proof of concept study on whether a cognitive computational model that is inspired by the motor control of the human body through muscle synergies combined with an anthropomorphic mechanical design, can result in accurate and robust prosthetic control through a noninvasive BMI. In future, learning of a complex Finite Automata that reflects complex upper limb motor behaviours will be investigated.

quadriplegia is paralysis where the person is unable to move all his limbs. The primary cause of quadriplegia is a spinal cord injury, but other conditions such as cerebral palsy and strokes can cause a similar paralysis. The amount of impairment resulting from a spinal cord injury depends on the part of the spinal cord injured and the amount of damage done. Injury to the spinal cord can be devastating because the spinal cord and the brain are the main parts of the central nervous system, which sends messages throughout your body. This paper proposes algorithm to detect the eye movement, known as Coherence algorithm. The proposed paper uses eye to control the wheel chair. The Coherence algorithm detects whether it is the movement of eye towards left or right. The eye blinking feature is also used by the algorithm to control the starting and stopping of wheelchair, then a heart beat sensor is used to detect the heart beat values, Whenever the heart beat values crosses a threshold value, then a buzzer is activated and a call is made to the concerned person using GSM.

Astrocytes are known as electrically non-excitable cells, responsible for information processing and ubiquitously expressed through mammalian Central Nervous System (CNS). Any disruption on astrocytic Ca2+ signalling can lead to neurodegenerative disorders like Alzheimer’s Disease (AD). A digital hardware design for astrocyte model with metabotropic Glu receptor 5 (mGluR5)-induced Ca2+ oscillations is presented here. In this paper, a set of piecewise linear functions is presented for this model to reduce the hardware resources and digital design complexity. Hardware synthesis and numerical simulation validate that the designed circuit can mimic the biophysical Ca2+ behavior with lower hardware cost. This model can be utilized for large-scale neurophysiological system of astrocytic Ca2+ excitability.

The paper presents results of mathematical modeling of regulatory mechanisms of pathologies in the human nervous system responsible for acute respiratory failure in patients with COVID-19. Analysis of the characteristic behaviors of solutions of the developed functional differential equations with delay shows extinction modes, stable stationary state, stable self-oscillatory behavior, irregular functioning and the effect of sharp destructive changes - the “black hole” effect in the model of central nervous system regulation.

The optic nerve is a part of the central nervous system surrounded by cerebrospinal fluid and is encased in a sheath. Changes to the cerebrospinal fluid due to injury, tumor rupture and so on can increase intracranial pressure (ICP) and can result in changes in the sheath diameter. Measuring the changes in the sheath can be done through ultrasound imaging with which the optic nerve sheath diameter can be measured. Since this approach is non-invasive, it would reduce the cost for patients and healthcare if sheath diameter could be used as a predictor of increase in ICP. However, the manual measurement of the nerve sheath diameter is very time consuming and could be affected by human errors. In this paper we propose an image processing approach in which the optic nerve sheath diameter is measured automatically. In our proposed method, we first denoise images and then detect the region of interest using a simple line integral method. After that by analyzing super-pixels we measure the diameter. We compared the results of the proposed method with manual measurements from two experts. The average percentage of error between the proposed method and the experts' measurements did not substantially differ from the error between the two experts.

Upright stance maintenance under perturbed condition is a complex phenomenon in which the Central Nervous System(CNS) is engaged to regulate the balance preventing subject to fall. Many models of unperturbed stance are present in literature. However, the necessity to model balance maintenance in presence of external disturbance is still an open problem. In this paper, a new model representing the human balance maintenance under perturbed condition is presented. An adaptive sliding mode approach is used to model the action played by the CNS, the control parameters are then optimized in order to match real and simulated data. The trend of optimized parameters seems to reveal the development of different postural strategies throughout the experimental trials.

Multiple Sclerosis (MS) is an immune-mediated inflammatory disease of the central nervous system which damages the myelin sheath enveloping nerve cells causing severe physical disability in patients. Relapsing Remitting Multiple Sclerosis (RRMS) is one of the most common form of MS and it is characterized by a series of attacks of new or increasing neurologic symptoms, followed by periods of remission. Recently, many treatments were proposed and studied to contrast the RRMS progression. Among these drugs Daclizumab, an antibody tailored against the interleukin −2 receptor of T cells, exhibited promising results. Unfortunately, more recent studies on Daclizumab highlight severe adverse effects, that led to its retirement from the EU marketing authorization process. Motivated by these recent studies, in this paper we describe how computational modelling can be efficiently exploited to improve our understanding on Daclizumab mechanism of action, and on how this mechanism leads towards the observed undesirable effects.

In this short paper we present the recent results on fault-tolerance in brain-inspired wireless communications, where the density of deployed transmission points is high. We follow the analogy to human brain and add dedicated functionality of microglia - a glial cell acting as the immune defense in the central nervous system.

The blood brain barrier (BBB) is a specialized membrane that covers the blood vessel in the central nervous system. This structure regulates the passage of certain substances for keeping the homeostasis into the brain. But this selective permeability is a drawback in treatments for neurodegenerative diseases, because the BBB prevents the passage of drugs to the affected brain cells. The use of focused ultrasound (FUS) has proven to be a promising tool for the BBB disruption in specific zones, which improves the release of drugs. One of the problems of FUS is the high attenuation of cranium. This paper presents a study of the acoustic field pattern and the disruption of the BBB in rat's brain for different diameters of craniotomies. These experiments were carried out in rats wistar. A 2 MHz FUS transducer of 10 mm radius and a focal length of 20 mm was used. It was found that a craniotomy of 1 cm allows the focus to be on the substantia nigra, our area of interest, without excessive field distortion and transmission losses.

Epilepsy is a neurological disease that is referred to as a disorder of the central nervous system characterized by the loss of consciousness and convulsions. Epileptic patients are subject to epileptic seizures caused by abnormal electrical discharges that lead to uncountable movements, convulsions and the loss of consciousness. Approximately 50 million people around the world are diagnosed with epilepsy, children and adults in the age range of 65–70 years old are effected the most. Although the main cause of this disease is unknown, however, most of the symptoms of the epilepsy seizure can be medically treated. Epileptic patients are subject to seizures that cause uncontrollable movements and loss of consciousness which can lead to serious injuries, and sometimes death. As a result, computerized seizure detection techniques are vital solutions for epileptic patients to protect them from dangers at the time of a seizure. In this paper, we propose an epilepsy seizures detecting method that can be implemented in a hardware device to help epileptic patients. The Electroencephalogram (EEG) is widely recognized for diagnosing and assessing brain activities and disorder. Our study utilized an EEG datasets that is used in various research regarding epilepsy detection. We processed the EEG signal in both time and frequency domains and applied a Chebyschev filter for preprocessing the signal, then, by using Wavelet Analysis, we decomposed the filtered signal into five sub-bands in both time and frequency domain. However, we only used the Delta sub-band for further processing. Discrete Wavelet Transform was used for feature extraction, then thresholding was implemented in order to determine the noisy part of the signal. Moreover, we applied some widely used classifiers for epilepsy seizure detection, and compared our results with other approches.

Multiple sclerosis (MS) is a chronic autoimmune disease which affects various areas of inflammatory demyelination within the central nervous system. The symptoms of MS are muscle weakness, weak reflexes, muscle spasm, difficult to move, miss-coordination and unbalance with others. Image processing plays a vital role in diagnosing multiple sclerosis. This paper proposes a novel method to detect the brain disease multiple sclerosis by segmenting brain into right and left hemispheres which are then divided into 16 quadrants. The disease is identified by the presence of higher pixels in specific quadrants of white matter and regions near to Cerebro spinal fluid(CSF); also by the absence of higher intensity pixels on gray matter. A total of 10 multiple sclerosis and healthy brain images are taken for experimentation. The result obtained show that the proposed method successfully identifies multiple sclerosis.

To the present day, a multitude of studies aims to understand how the Central Nervous System (CNS) translates neural pulses to muscle motor tasks, through the analysis of surface EMG (sEMG) recordings. One of the most considerable methods applies the Non-Negative Matrix Factorization (NMF) to data recorded from sEMG electrodes, to extract coordinated motor patterns, the so-called muscle synergies, which hypothesize a modular control of the muscles by the CNS. The application of NMF could lead to novel applications in muscular rehabilitation or advanced prosthesis motor control. However, its application is still restricted to research laboratories, because the complexity of the NMF algorithm does not allow an efficient implementation on embedded systems, such as portable or wearable devices. This paper presents a FPGA-based hardware acceleration of a NMF algorithm, the Projected Gradient Alternating Least Square (ALS-PG), on a Xilinx Zynq7000 System-on-Chip (SoC) development board. In particular, we propose a case study for the pre-processing of sEMG in a hand prosthesis control system, where the NMF is subject to realtime processing constraints. Preliminary hardware optimizations produced a overall speedup of 2.92x of the algorithm (23.9x considering only the accelerated sections) compared to the software implementation executed on the board ARM core. The real-time constraint could not be met because the entire process is slower than a standard CPU implementation (6.14x worse), nevertheless, the overall system shown a promising increase in the power efficiency of 1.65x.

The paper presents a new isometric method of determining the functional condition of the central nervous system. The method is based on the analysis of involuntary oscillatory movements of limbs of a human body secreted on the background of voluntary movements. A variant of the hardware implementation of the method is shown, the results of testing people with different health status are demonstrated.

Deep Brain Local Field Potentials (LFP) are one of the most promising techniques in the process of understanding the inner workings of our the central nervous system (CNS). The information gathered on neural activity is a key component of interfaces between the human brain and artificial devices. The LFPs recorded from subthalamic nucleus (STN) are important sources of information related to the preparation, execution and imaging of movements. In this paper, time and frequency domain features are used to decode movements and its laterality, left or right sided visually cued movements from STN LFPs. First the frequency components of LFPs are separated using wavelet packet transform (WPT). The feature selection process involves combining three approaches using time window based power features, causality features computed using granger causality and cross correlation, and frequency domain features computed using discrete cosine transform (DCT). A weighted sequential feature selection process is then used to prune the feature vector. A Bayesian classifier along with cross validation procedure is applied in the classification process. The classification results show a marked improvement in performance over previous reports.

Diffusion Weighted Magnetic Resonance Imaging (DWMRI) and tractography approaches are the only tools that can be utilized to estimate structural connections between different brain areas, non-invasively and in-vivo. A first step that is commonly utilized in these techniques includes the estimation of the underlying fibre orientations and their uncertainty in each voxel of the image. A popular method to achieve that is implemented in the FSL software, provided by the FMRIB Centre at University of Oxford, and is based on a Bayesian inference framework. Despite its popularity, the approach has high computational demands, taking normally more than 24 hours for analyzing a single subject. In this paper, we present a GPU-optimized version of the FSL tool that estimates fibre orientations. We report up to 85x of speed-up factor between the GPU and its sequential counterpart CPU-based version. © 2012 IEEE.

Epilepsy is the most common disease of central nervous system. According to World Health Organization about 50 million people worldwide and 80% people from developing regions are suffering from epilepsy. Electroencephalogram (EEG) is one of the non-invasive techniques available for seizure detection. In this paper we have proposed non-linear feature based epileptic seizure detection using least square support vector machine (LSSVM) classifier. We have developed low computational and more accurate system for real time epileptic seizure detection. Symbolic entropy, Lempel-Ziv complexity and sample entropy are extracted and LSSVM classifier is used to classify data into ictal, healthy and inter-ictal EEG signals. LSSVM classifier in One-verse-All approach, One-verse-One approach, and multiclass classifier approach classifies ictal EEG signal with an accuracy of 81.67%, 91.25 % and 82.22 % respectively. Hence, the proposed One-verse-One approach has detected ictal EEG signal with highest accuracy and sensitivity.

Experimental researches in recent years have proven that glial cells and specifically astrocytes play several important roles in the central nervous system. Due to difficulties in investigation of astrocyte’s functions in neural information processing mechanisms, an appropriate method is computational modeling approach. In this paper, we use the Li-Rinzel model for astrocyte and Izhikevich model for neurons to create a tripartite synapse model and then the role of astrocyte in neural synchronization is explored. Next, the new mechanism of stimulating astrocyte to support impaired astrocyte to maintain normal neural oscillations is proposed. Simulation results show that the malfunction of astrocyte which in turn induces hyper synchronization of the coupled neurons could be compensated by stimulating the astrocyte using an external stimulator.

Drug delivery to subcortical regions is susceptible to the blood-brain barrier (BBB) hindering the molecular exchange between the blood stream and the brain parenchyma. Focused ultrasound coupled with the administration of microbubbles has been proven to open the BBB locally, transiently and non-invasively both in rodents and in Non-Human-Primates (NHPs). The development of this disruption technique independent of MRI monitoring is of outstanding importance yet restrained to the targeting optimization. The current paper establishes the linear relationship of the incidence angle with the volume of BBB opening (V BBB ) and the applied pressure when sonicating the Caudate Nucleus and the Putamen region of five NHPs. In addition, the effect of central nervous system structures on the opening morphology is evaluated by identification of the gray-to-white-matter ratio at the opening site. Finally, the targeting accuracy is assessed by estimation of the geometric and angle shift of the opening from the targeted region. Interestingly, results prove a monotonic increase of the opening volume with close to normal incidence angles. Moreover, 80.35% of the opening lies on gray matter regions compared to only 19.41% attributed to the white matter. The BBB opening was found to be shifted axially, towards the transducer, and laterally with an average angle shift at 4.5°. Finally, we were able to reproduce our findings by employing the same stereotactic and ultrasonic parameters in different experiments and animals. This study documents the a priori prediction of the opening volume through manipulation of the angle and pressure establishing the predictability, accuracy and safety of the current technique on NHPs.

Human motions are a result of complex-neural interactions between the central nervous system (CNS), sensory and musculoskeletal systems. In this paper, we are focusing in investigating these interactions relying mainly on the concept of sensory- and muscle-synergies. We hypothesize that the CNS is processing and transferring data from sensors and muscles in a unique low-dimensional signaling to simplify the complexity of environmental inputs and to facilitate recruiting muscles patterns. A pilot study involving computing sensory and muscle synergies of seven healthy participants while performing posture balance tasks was conducted to validate our hypotheses. Changes on the participant's muscles lengths during performing the task were used to represent proprioceptors and compute sensory synergies. The resultant muscles activities, on the other hand, were recorded and used to estimate muscle synergies. Experimental results suggest that the environmental inputs were translated into lower dimensional signals and used to move the target limb to the desired position immediately after the balance disturbance. Participants who showed better posture response were found to be likely to have a stronger correlation between the utilized sensory and muscle synergies. This preliminary study is considered fundamental to understand the neural strategies among the CNS, sensory and musculoskeletal systems.

Central nervous system (CNS) is composed of the brain and the spinal cord. Any impairment in the functionality of these parts can cause serious disabilities such as movement impairment and urinary incontinence. Spinal cord injury (SCI) and stroke are two conditions affected by injury of the CNS. This article presents a systematic review of the efficiencies as well as the inefficiencies of the Surface-Functional Electrical Stimulation (S-FES) that benefits from closed loop and control strategies to be engaged in rehabilitative interventions for paraplegic SCI patients. This study resulted in nine papers for review. To evaluate the evidence, the feasibility, fatigue resistance, safety and tracking error factors of each reported study were considered. From this study, it was concluded that the integration of control system into S-FES can promote rehabilitation process while minimize or control counterproductive elements such as fatigue and spasm as well as monitoring the activity for better safety measures during the S-FES intervention.

This paper demonstrates the possibility of in vivo imaging for neuroimmunological assessments using ultra high-field magnetic resonance imaging (UHF-MRI). UHF-MRI provides a highly sensitive MR microimaging technique could be used to identify previously invisible pathologies and cellular dynamics in the central nervous system (CNS) of living animals. Our technique could reveal the mechanisms underlying the immune responses and cell dynamics during neuroinflammation, CNS diseases, and also in the normal state.

The central pattern generator(CPG) is used to control robot very well. But the adjustment of CPG parameters and it's interaction with the external inputs are still open problems. Based on biological knowledge, the central nervous system rules the switch of the CPG modes. In this paper we focus on the coupling relationship between the cerebral cortex and the CPG and establish the mathematical model to analyze their interaction mechanism. Firstly, we propose the model of the interaction between the neural mass model(NMM) and the CPG, which describes the relationship between the cerebral cortex and the CPG. Then the period doubling and the phase diagrams are used to describe changes in system behavior and the interaction mechanism. Through analysis on dynamic characteristics of the new model, we find that many modes described in state equilibrium points in the cerebral cortex correspond to the locomotion patterns and the change of state equilibrium points in the cerebral cortex leads to the change of gait patterns. The mode in the cerebral cortex relies not only on the excitatory input from other cortical areas, but also on the external input of CPG. And the results can explain the biological phenomenon that the increasing electrical stimulation would lead to a gait switch in a decerebrated animal.

Highly flexible continuum arms have been investigated thoroughly. Materials and actuators employed include cables, pneumatic muscles, and also shape memory alloys (SMAs). SMA is a smart material that can be applied to continuum robot due to their intrinsic compliance. It can elongate or contract by applying external forces and return to its original shape when given appropriate current. However, control of the SMA actuated robot is still a challenge. This paper takes inspiration from octopus neurophysiology and designs a hierarchical controller for the SMA based continuum robotic arm.

Studies on behavior control are important for bio-robots designation. For auto or manual navigation of the bio-robots, the accuracy of the command execution is especially critical. In this paper, we reported a precise “STOP” command for the rat-robots by optical stimulation of the central nervous system (CNS). We labeled dorsolateral periaqueductal gray (dlPAG) neurons with light sensitive channelrhodopsin-2 (ChR2) and directly probed the optical fiber to reactivate these neurons. The rats showed freezing behavior only upon the optical stimulation with an appropriate range of laser intensity and stimulation frequency. Neuron spikes and local field potential (LFP) were simultaneously recorded with optical stimulation by optrodes on free moving rat-robots. Together, our findings demonstrated the utility of deep brain optical stimulation for the stopping behavior of rat-robot control and indicated a potential application of optogenetics for precise control of bio-robots in further work.

Cerebrospinal fluid (CSF) plays an important role to protect the brain from minor external damages as well as preserving the nutrients for the central nervous system (CNS) and stabilizing the chemical regulation in the system. In most cases, many neurodegenerative disorders such as hydrocephalus and Alzheimer's diseases may affect the normal volume and pressure of the CSF flow within brain and CNS. Thus, analyses on these complex behaviors have become substantially important. Recent studies showed that the use of cine phase contrast magnetic resonance imaging (PC-MRI) has facilitated further understanding of the CSF flow movements due to its capability to analyze the pulsatile motions over a specific range of time. However, the main drawback of the technique is the presence of partial volume effect, which cause blurring boundary between the CSF and the soft tissue regions. In this paper, we review the current progress on developing effective image segmentation methods that may assist to overcome the partial volume effect of the images. Firstly, the basic of the CSF and its pulsatile motion within brain are introduced. Then, the current image segmentation methods are discussed and this leads to the recent trend and direction of the CSF pulsatile segmentation.

This paper describes the development of a system feedback or biofeedback physiological activity aimed at the rehabilitation of patients with severe upper extremity motor disorders caused by the central nervous system. In the proposed system use of surface electromyographic signals acquire the information of muscle activity patient and then a stage of processing is done a visual and auditory feedback through a graphical interface. This information may provide feedback to patient recognition of residual activity allows us to train their muscles proprioception and skill altered in the event of brain injury.

An online neuro-robotic platform in the form of a small vehicle, the "RatCar" has been developed. First, a rat had neural electrodes implanted in the motor cortices to record extracellular potentials. Then, our system combined the rat and its vehicle body by hanging the rat under its floor. In this paper, an experimental platform is proposed to observe and analyze motor commands by correlating neural signals with locomotion states. It was designed to operate in 2 modes; (a) adaptively correlating neural signals and locomotion states to determine motor commands, and (b) applying the estimated motor commands to control the vehicle according to the intention of the rat. As a result, time-varying correlation between neural and locomotion activities has been adaptively visualized in real time to analyze motor commands in various body conditions. In addition, a control of the vehicle has been improved.

Limb state feedback is of great importance for achieving stable and adaptive control of FES neuroprostheses. A natural way to determine limb state is to measure and decode the activity of primary afferent neurons in the limb. The feasibility of doing so has been demonstrated by [1] and [2]. Despite positive results, some drawbacks in these works are associated with the application of reverse regression techniques for decoding the afferent neuronal signals. Decoding methods that are based on direct regression are now favored over reverse regression for decoding neural responses in higher regions in the central nervous system [3]. In this paper, we apply a direct regression approach to decode the movement of the hind limb of a cat from a population of primary afferent neurons. We show that this approach is more principled, more efficient, and more generalizable than reverse regression.

This paper proposed a multimodal fusion between brain and peripheral signals for emotion detection. The input signals were electroencephalogram, galvanic skin resistance, temperature, blood pressure and respiration, which can reflect the influence of emotion on the central nervous system and autonomic nervous system respectively. The acquisition protocol is based on a subset of pictures which correspond to three specific areas of valance-arousal emotional space (positively excited, negatively excited, and calm). The features extracted from input signals, and to improve the results, correlation dimension as a strong nonlinear feature is used for brain signals. The performance of the Quadratic Discriminant Classifier has been evaluated on different feature sets: peripheral signals, EEG's, and both. In comparison among the results of different feature sets, EEG signals seem to perform better than other physiological signals, and the results confirm the interest of using brain signals as peripherals in emotion assessment. According to the improvement in EEG results compare in each raw of the table, it seems that nonlinear features would lead to better understanding of how emotional activities work.

Described in this paper are the two types of reflexometers which analyze human visual and motor reaction. They are used for evaluation of functional state of human central nervous system.

Epilepsy is a neurological condition, which affects the nervous system. Automatic seizure detection is very important in clinical practice and has to be achieved by analyzing the electroencephalogram (EEG). Seizures are the clinical manifestations of excessive and hypersynchronous activity of the neurons in the cerebral cortex and represent one of the most frequent malfunctions of the human central nervous system. Therefore, the search for precursors and predictors of a seizure in the human EEG is of utmost clinical relevance and may even lead to a deeper understanding of the seizure generating mechanisms. In this paper, the normal, pre-ictal (background) and ictal (epileptic) EEG signals are studied using higher order spectra. HOS based measures are shown to be able to distinguish epileptic EEG from normal and background EEG with high confident level ( p-value of less than 0.05).

Neural spike train decoding is an important task for understanding how the biological nervous system performs computation and communication. Interest in this field has grown due to recent advances in neural prosthetics, as well as the need to explore non-traditional computational architectures. Current methods deal poorly with or ignore altogether the non-linearities inherent in neural computation. In this paper, we explore polynomial kernel regression as a method for for dealing with neural non-linearities. Two experiments, based on sensory perception and motor control, demonstrate the ability of the approach to decode the neural spike code using synthetic data.

Multiple sclerosis (MS) is a chronic inflammatory disease of the central nervous system, which leads to focal plaques of demyelination and axonal impairment in the central nervous system. Multiple sclerosis occurred in brain tissue, optic nerve and spinal cord, mainly in brain tissue, which leads to severe progressive neurological dysfunction, such as blindness, paralysis and death. There are about 65,000 patients with MS in China, and recently the incidence rate of MS is increasing, which is the most familiar factor for non-traumatic deformity in the third or fourth decade of life. In this paper, regions of interesting were chosen in MRI T2WI as research object; we used a new neighborhood difference method to analyze the texture features of MRI for patients with MS. Four parameters were extracted from the region of interests by neighborhood difference method. Via screening statistical significant parameters, we found two eigenvectors to represent the sample. We discussed the microcosmic variety of brain white matter throughout extracted texture parameters and found there was significant difference between normal brain white matter and normal appearing white matter, which was proved by the statistical analysis. This study will be beneficial to the research of early diagnosis and treatment estimate of MS.

The central nervous system is composed of many different interacting neurons all working together to provide sensory, motor, and visceral signals to the specific organism. The environment in which those neurons operate is crucial to the survival of the organism. Some environmental factors may include pH, temperature, or the intra-and extracellular ion concentration. If any of these factors are changed in a substantial way, the electrophysiological properties of the neuron may be affected. Some of the important electrophysiological properties of the neuron include the resting potential, the action potential, and the firing rate. These properties can be measured directly using sharp microelectrode methodologies. The performed experiments consist of recording the resting potential, action potential, and firing rate from a neuron in the CNS cerebral ganglion of the Lymnaea stagnalis. This paper addresses how various levels of pH affect the electrophysiological properties of neurons in the CNS of L. stagnalis.

This paper describes the design of silicon intracortical stimulating microelectrodes for mapping of the functional organization in the central nervous system and potential neural prosthetic applications. Active microelectrodes with different configurations have been developed to facilitate various applications. In-vivo experiments were conducted in selected areas (auditory cortex, DCN, IC) in the auditory pathway of guinea pig models. The stimulating microelectrodes are capable of stimulating neural tissue with controllable current stimuli and selectively stimulating neurons in the DCN to elicit corresponding neural responses in the IC.

Modeling the muscle activity patterns in coordinated reaching movements from surface electromyogram (sEMG) recordings is a key challenge in motor behavior studies. Based on Bayesian network (BN) modeling of sEMG data, this paper presents a framework for discovering and modeling muscle networks and identifying functional muscle groupings. The learned network is further explored for the purpose of classification. We demonstrate the proposed approach on reaching movements in stroke. We found that the specific muscle triples (anterior deltoid, biceps-brachium and lateral deltoid), (biceps-brachium, triceps lateral and lateral deltoid) and (triceps long head, triceps lateral and lateral deltoid, are selectively recruited during reaching movements and are differentially recruited after stroke. We call these computed muscle triplets "directed synergies" to contrast with synergies that are defined by traditional covariance methods. A BN trained on a single healthy subject completely classified and detected the affected side in all stroke subjects. The proposed approach appears a promising technique for muscle network and synergy analysis in motor control

This paper describes microassembly techniques for an out-of-plane three-dimensional microelectrode array for neural stimulating and recording in the central nervous system. An interlocking mechanism has been introduced into the microassembly components to facilitate the process, increase the robustness of the assembled device and improve the yield of the overall system. In-vivo testing has demonstrated full functionality of the microassembled 3D array.

One of the great abilities of the central nervous system (CNS) is that it can learn by itself how to control our body to execute required tasks. Although several motor control models have been proposed to explain well-learned arm reaching movements, those models do not fully consider how the CNS learns to control our body. In this paper, we propose a new motor control model that can learn to generate accurate reaching movements without prior knowledge of arm dynamics. In our model, the control law is learned in a trial-and-error manner using the reward signal. We focus on point-to-point arm reaching task in the sagittal plane and show that accurate reaching movements toward any given point can be learned and generated by our model. Furthermore, the model can predict human subjects' hand trajectories without specifying desired trajectories.

The identification of the timing of the discharges of groups of muscle fibers (motor units) is of utmost importance in research into the strategies employed by the central nervous system in producing muscle force and in the diagnosis of neuromuscular diseases. The process involves the recognition of unique shapes (action potentials) contributed by different motor units at random times throughout a muscle contraction. This paper addresses a specific aspect of the identification process: the decomposition of the compound signal when the action potentials of two or more motor units are superimposed. We propose a cross-time-frequency-based procedure to identify which two (out of a previously identified collection of waveforms) are included in a superposition.

In this paper, we review results obtained by testing healthy control subjects and patients with neurological diseases in a visuo-motor task. This simple task generates complex dynamics which are simulated using nonlinear feedback. By comparing the experimental and simulated dynamics we probe at the properties of the central nervous system controlling human movement.

The dorsal cochlear nucleus (DCN) is part of the first stage of auditory processing in the central nervous system. Experimental evidence has provided a conceptual model for a portion of the DCN neural circuit that serves as a basis for the computational model described in this paper. The model consists of four neural populations arranged tonotopically. The pattern of convergence from one population to another and the strengths of those connections are important model parameters. Lumped parameter electrical circuit models are used to model the membrane potential of individual cells. Synapses are simulated by activating variable conductances in postsynaptic cells according to spike activity in presynaptic cells. The level of detail incorporated in the model is a good compromise between biophysical accuracy and the computational tractability required to simulate relatively large networks. Results are shown demonstrating the ability of the model to replicate features of DCN cross-correlation functions and to simulate DCN response properties with quantitative accuracy. The model is a useful tool for exploring hypotheses regarding DCN structure and function.

This paper introduces a novel connectionist approach to neural network modelling that integrates dynamic gene networks within neurons with a neural network model. Interaction of genes in neurons affects the dynamics of the whole neural network. Through tuning the gene interaction network and the initial gene/protein expression values, different states of the neural network operation can be achieved. A generic computational neurogenetic model is introduced that implements this approach. It is illustrated by means of a simple neurogenetic model of a spiking neural network (SNN). Functioning of the SNN can be evaluated for instance by the field potentials, thus making it possible to attempt modelling the role of genes in different brain states such as epilepsy, schizophrenia, and other states, where EEG data is available to test the model predictions.

In this paper we study the emergent structure of networks in which spike-timing dependent synaptic plasticity is induced only by external random noise. We show that such noise driven Hebbian networks are able to develop a broad range of network structures, including scale-free small-world networks. The development of such network structures may provide an explanation of the role of noise and its interplay with Hebbian plasticity. We also argue that this model can be seen as a unification of the famous Watts-Strogatz and preferential attachment models of small-world and scale-free nets. Our results may support Edelman's idea on that the development of central nervous system may have evolutionary components.

This paper presents a study on the ultrasonic hearing aids and perception tests for both normal hearing subjects and profoundly deaf people and the active location of estimated signal sources in the brain. In these experiments auditory activities by bone-conducted ultrasonic stimuli were measured by magnetoencephalography using a 122-channel whole-head neuromagnetometer. Both normal hearing and profoundly deaf people could perceive the ultrasound stimuli; however the active center in the brain of the latter people was found in a different region (superior temporal area) from the former subjects. These results suggest the reorganization of the auditory central nervous system in humans.

This paper studies the modeling of the human spinal cord structure as a receptor for an incident electromagnetic field (EMF), motivated by its linear, conductive, antenna-like properties. The electrical modeling of the human spinal cord is very important in applications such as spinal cord stimulation (SCS), which is used in the management of chronic pain. Towards this end, accurate models have been developed (see Wesselink, W.A. et al., 1998; Struijk, J.J. et al., 1993), which characterize the spinal cord and surrounding anatomical structures as a volume conductor. A concentric cylindrical layer model for the spinal cord structure is employed, and FDTD analysis carried out for an incident plane wave. This study and the development of further accurate models are important in understanding the effects of EMF on the human system, and have the potential for future non-invasive neural diagnosis and therapy (see Thuery, J., 1992).

The paper investigates the use of artificial neural networks (ANN) to unconventionally classify Internet traffic. Structurally and functionally, the classifier used is a feedforward multilayer layer perceptron (FFMLP) network trained using backpropagation. The inputs are random samples of bits from a bit stream (i.e. all the inputs are either 1 or 0). The data was collected and pre-processed, then used to train, test and evaluate the classifier. Despite the lower capability to identify certain data types, the algorithm has shown that it has very good features as a classifier. SMTP, TELNET, FTP, HTTP, IP TELEPHONY and UDP data types were used in the investigation.

The identification of the timing of the discharges of groups of muscle fibers (motor units) is of utmost importance in research into the strategies employed by the central nervous system in producing muscle force as well as in the clinical diagnosis of neuromuscular diseases. The process involves the recognition of unique shapes (action potentials) contributed by different motor units at random times throughout a muscle contraction. This paper addresses a specific aspect, of the identification process: the decomposition of the compound signal when the action potentials of two or more motor units are superimposed. We propose a cross-time-frequency-based procedure to identify which two (out of a previously identified collection of waveforms) are included in a superposition. The procedure also determines the relative delay of the two waveforms.

We have presented studies on FES control commands from central nervous system. This paper aimed to clarify how to detect the intention for movement before or without execution. The activity of 782 neurons was recorded from the forelimb and shoulder area of the primary motor cortex and premotor cortex of a monkey. A significant increase or decrease in neuronal activity was detected preceding the execution of a trained reaching movement. The activity in 317 neurons has enabled a computer to predict the reaching direction before movement onset by using a new analysis method of differences in neuronal activities among reaching direction. These results suggest that neuronal activities of these areas may be used as the control commands for FES.

This paper reports the findings of a pioneering study of central conduction in 21 leprosy patients. Somatosensory evoked potentials (SEP) and compound nerve action potentials (CHAP) are recorded after electrical stimulation of median nerves of subjects. The data fro. both the SEPs and the CHAPs were subjected to time domain, frequency domain and discriminant analyses. The amplitudes and power spectra of the SEPs and the discriminating power of the central conduction time unambiguously prove that the central nervous system (CNS) is spared in leprosy. In contrast, the significant changes in the characteristics of CNAPs indicate a high degree of peripheral nerve abnormality. Thus, that the mycobacterium leprae do not infect the CNS is electrophysiologically established for the first time.