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Unit Commitment: An Escort Dynamics Approach
Power Generation, Operation, and Control
Oral insulin does not alter gut microbiota composition of NOD mice
eng_Latn
30,500
Modified transformation formulae between fracture toughness and CTOD of ductile metals considering pre-deformation effects
Chip Formation Mechanism Using Finite Element Simulation
Oral insulin does not alter gut microbiota composition of NOD mice
eng_Latn
30,501
Increasing process margin in SiGe heterojunction bipolar technology by adding carbon
Emerging SiGe HBT reliability issues for mixed-signal circuit applications
Oral insulin does not alter gut microbiota composition of NOD mice
eng_Latn
30,502
A novel partial-SOI LDMOSFET (>800 V) with n-type floating buried layer in substrate
Improvement of SOI Trench LDMOS Performance With Double Vertical Metal Field Plate
Oral insulin does not alter gut microbiota composition of NOD mice
eng_Latn
30,503
Physical vapour deposition of rare-earth fluorides
Application of PVD technique to the fabrication of erbium doped ZrF4-based glass ceramic for optical amplification
Oral insulin does not alter gut microbiota composition of NOD mice
eng_Latn
30,504
RHEOLOGICAL CHARACTERIZATION OF INULIN
The rheological properties of different GNPs
Oral insulin does not alter gut microbiota composition of NOD mice
yue_Hant
30,505
Polished injection moulds’ and surface defects’ influence on the quality of plastic components
Gloss and surface topography of ABS: A study on the influence of the injection molding parameters
Oral insulin does not alter gut microbiota composition of NOD mice
eng_Latn
30,506
Kinetics of sherd (body) formation during slip casting
Enhanced casting rate by dynamic heating during slip casting
Oral insulin does not alter gut microbiota composition of NOD mice
eng_Latn
30,507
Oxide insertion layer in organic semiconductor devices
Organic electroluminescent diodes
Oral insulin does not alter gut microbiota composition of NOD mice
eng_Latn
30,508
is novolog an insulin?
Insulin aspart is a fast-acting insulin analog marketed by Novo Nordisk as NovoLog/NovoRapid. It is a manufactured form of human insulin; where a single amino acid has been exchanged. This change helps the fast-acting insulin analog be absorbed quickly into the bloodstream.
NovoLog® is a fast-acting mealtime insulin that helps lower mealtime blood sugar spikes. It has been proven to help control high blood sugar in people with diabetes when taken with a long-acting insulin. And, it has been used by millions of people since 2001.
eng_Latn
30,509
Global aetiology and epidemiology of type 2 diabetes mellitus and its complications
A metagenome-wide association study of gut microbiota in type 2 diabetes
Active and passive distraction using a head-mounted display helmet: effects on cold pressor pain in children.
eng_Latn
30,510
Using mobile device screens for authentication
The Resurrecting Duckling: Security Issues for Ad-hoc Wireless Networks
The early infant gut microbiome varies in association with a maternal high-fat diet
eng_Latn
30,511
Electroencephalogram and Sensory Evoked Potentials: Findings in an Unresponsive Patient With Pontine Infarct
Diffuse Theta Activity and Spindle-Like Bursts during Coma after Cardiac Arrest
Noninvasive imaging using an array of electric potential sensors
eng_Latn
30,512
Background and Purpose ::: Extracranial internal carotid artery (ICA) dissection is an important cause of ischemic stroke in younger adults. The optimal medical and surgical strategies for managing these lesions have not been well established. We report a case series of extracranial ICA reconstruction using overlapping flow-diverter stents as a rescue therapy for the treatment of symptomatic ICA dissection in patients presenting with recurrent ischemic stroke and/or severe hemispheric hypoperfusion who failed medical management.
PURPOSE ::: To provide evidence for the endovascular repair of patients with extracranial carotid artery dissection. ::: ::: ::: MATERIALS AND METHODS ::: A comprehensive literature review was performed whereby all studies that reported on the results of endoluminal repair of extracranial carotid artery dissection and provided information about primary technical and clinical success were identified. The Pubmed, Embase, and Medline databases were searched between January 1997 and February 2008 by two independent observers by using combinations of search terms "endovascular repair," "extracranial carotid artery," and "carotid dissection." ::: ::: ::: RESULTS ::: After studies were selected according to the given criteria, 13 studies were included in our statistical analysis. The number of reported patients was 62, with a total of 63 extracranial carotid artery dissections. The mean patient age was 43.3 years. The mean follow-up period was 15.7 months +/- 8.7. Various causes were responsible for the disease, including a blunt neck injury in 28 patients (45%), spontaneous dissection in 21 (37%), and iatrogenic trauma during invasive radiologic procedure in 17.7% patients. The technical success rate was 100% (63 of 63 procedures). The primary and 1-year patency rate of the stents and/or stent-grafts was 100%. The overall major adverse cardiovascular events rate was 11% (seven strokes). The total follow-up mortality rate was 0%. ::: ::: ::: CONCLUSIONS ::: The current status of the reported cases in the literature regarding the treatment of carotid artery dissection by means of stent placement shows excellent early and 1-year patency rates and a low major adverse cardiovascular event rate. However, further evaluation is necessary to draw robust conclusions.
Averaged event-related potential (ERP) data recorded from the human scalp reveal electroencephalographic (EEG) activity that is reliably time-locked and phase-locked to experimental events. We report here the application of a method based on information theory that decomposes one or more ERPs recorded at multiple scalp sensors into a sum of components with fixed scalp distributions and sparsely activated, maximally independent time courses. Independent component analysis (ICA) decomposes ERP data into a number of components equal to the number of sensors. The derived components have distinct but not necessarily orthogonal scalp projections. Unlike dipole-fitting methods, the algorithm does not model the locations of their generators in the head. Unlike methods that remove second-order correlations, such as principal component analysis (PCA), ICA also minimizes higher-order dependencies. Applied to detected—and undetected—target ERPs from an auditory vigilance experiment, the algorithm derived ten components that decomposed each of the major response peaks into one or more ICA components with relatively simple scalp distributions. Three of these components were active only when the subject detected the targets, three other components only when the target went undetected, and one in both cases. Three additional components accounted for the steady-state brain response to a 39-Hz background click train. Major features of the decomposition proved robust across sessions and changes in sensor number and placement. This method of ERP analysis can be used to compare responses from multiple stimuli, task conditions, and subject states.
eng_Latn
30,513
Certainty-Based Reduced Sparse Solution for Dense Array EEG Source Localization
Improved MEG/EEG source localization with reweighted mixed-norms
Inability of the Submaximal Treadmill Stress Test to Predict the Location of Coronary Disease
eng_Latn
30,514
Use and Value of Ordering Emergency Electroencephalograms and Videoelectroencephalographic Monitoring After Business Hours in a Children's Hospital: 1-Year Experience
Emergency EEG and factors associated with nonconvulsive status epilepticus.
Incidence of venous thromboembolism verified by necropsy over 30 years.
eng_Latn
30,515
A Computer Analysis of EEG Spectral Signatures from Normal and Dyslexic Children
Hierarchical modeling of EEG signals
Delayed Reactive Distractor Suppression in Aging Populations
eng_Latn
30,516
An efficient signal acquisition with an adaptive rate A/D conversion
An Event-Driven Efficient Segmentation and De-Noising of Multi-Channel EEG Signals
Error diffusion coding for A/D conversion
eng_Latn
30,517
Spatio-temporal EEG measures and their application to human intracranially recorded epileptic seizures.
EEG analysis using wavelet-based information tools
Self-insight into emotional and cognitive abilities is not related to higher adjustment
eng_Latn
30,518
A Fast Algorithm for Computing the P-curvature
Automatic Classification of Restricted Lattice Walks
Cortical Proprioceptive Processing Is Altered by Aging
eng_Latn
30,519
An exploratory data analysis method for identifying brain regions and frequencies of interest from large-scale neural recordings
Performing Behavioral Tasks in Subjects with Intracranial Electrodes
Self-insight into emotional and cognitive abilities is not related to higher adjustment
eng_Latn
30,520
Non-uniform quantized huffman compression technique for EEG data
CPRI Data Compression Using Non-Uniform Quantized Huffman Technique in C-RAN
CPRI Data Compression Using Non-Uniform Quantized Huffman Technique in C-RAN
eng_Latn
30,521
Monitoring Sedation Levels by EEG Spectral Analysis
Continuous monitoring of depth of sedation by EEG spectral analysis in patients requiring mechanical ventilation
Continuous monitoring of depth of sedation by EEG spectral analysis in patients requiring mechanical ventilation
eng_Latn
30,522
Innovation Method for Dynamic Independent Component Analysis: A New Concept and Algorithm
Adaptive Blind Signal and Image Processing: Learning Algorithms and Applications
Brain Injury Does Not Alter the Intrinsic Differentiation Potential of Adult Neuroblasts
eng_Latn
30,523
Imputing Missing Values in EEG with Multivariate Autoregressive Models
Brain–computer interfaces for communication and control
Fitting autoregressive models for prediction
eng_Latn
30,524
Divide-and-conquer approach for brain machine interfaces: nonlinear mixture of competitive linear models
Point process modeling on decoding and encoding for Brain Machine Interfaces
An instrumental variable approach finds no associated harm or benefit from early dialysis initiation in the United States
eng_Latn
30,525
One of the most challenging issues in task-related fMRI data analysis consists of deriving a meaningful functional brain parcellation. The joint parcellation detection estimation (JPDE) model addresses this issue through an automatic inference of the parcels directly from fMRI data. However, for doing so, the number of parcels needs to be fixed a priori and an appropriate initialization for the mask parcellation must be provided too. Hence, this difficult task generally depends on the subject. In this paper, an automatic model selection approach is proposed to overcome this limitation at the subject-level. Our approach relies on a non-parametric Bayesian approach that estimates the number of parcels online using a Dirichlet process mixture model combined with a hidden Markov random field. The inference is carried out using a variational expectation maximization strategy. As compared to a standard model selection approach in the original JPDE framework, our non-parametric extension appears more efficient in terms of computational time and does not require finely tuned initialization. Our method is first validated on synthetic data to demonstrate its robustness in selecting the right model order and providing accurate estimates for the parcellation, the hemodynamic response function (HRF) shapes and the activation maps. The method is then validated on real fMRI data in two regions of interest (ROIs): right motor and bilateral occipital ROIs. The results show the ability of the proposed method to aggregate parcels with similar behaviour from a hemodynamic point of view, while discriminating them from other parcels having different hemodynamic properties. The HRF estimates of the dfferent hemodynamic territories obtained with our approach are close the the canonical HRF shape in both the right motor and the bilateral occipital cortices. The discrimination power of the proposed approach is increased compared to its ancestors where the results on real data show its ability to discriminate HRF profiles with different Full Width at Half Maximum (FWHM). The robust performance of detecting the elicited task-related activity is confirmed by comparing the neural response level estimates obtained using our approach with those obtained using the joint detection estimation (JDE) model.
Inference from fMRI data faces the challenge that the hemodynamic system, that relates the underlying neural activity to the observed BOLD fMRI signal, is not known. We propose a new Bayesian model for task fMRI data with the following features: (i) joint estimation of brain activity and the underlying hemodynamics, (ii) the hemodynamics is modeled nonparametrically with a Gaussian process (GP) prior guided by physiological information and (iii) the predicted BOLD is not necessarily generated by a linear time-invariant (LTI) system. We place a GP prior directly on the predicted BOLD time series, rather than on the hemodynamic response function as in previous literature. This allows us to incorporate physiological information via the GP prior mean in a flexible way. The prior mean function may be generated from a standard LTI system, based on a canonical hemodynamic response function, or a more elaborate physiological model such as the Balloon model. This gives us the nonparametric flexibility of the GP, but allows the posterior to fall back on the physiologically based prior when the data are weak. Results on simulated data show that even with an erroneous prior for the GP, the proposed model is still able to discriminate between active and non-active voxels in a satisfactory way. The proposed model is also applied to real fMRI data, where our Gaussian process model in several cases finds brain activity where a baseline model with fixed hemodynamics does not.
Berzelius failed to make use of Faraday's electrochemical laws in his laborious determination of equivalent weights.
eng_Latn
30,526
A new imaging algorithm for electric capacitance tomography
LabVIEW based data acquisition system for electrical capacitance tomography
Self-insight into emotional and cognitive abilities is not related to higher adjustment
eng_Latn
30,527
Selection of Optimal Frequency Bands of the Electroencephalogram Signal in Eye-brain-computer Interface
Real-Time EEG Classification of Voluntary Hand Movement Directions using Brain Machine Interface
No iron fertilization in the equatorial Pacific Ocean during the last ice age
eng_Latn
30,528
An efficient hybrid linear and kernel CSP approach for EEG feature extraction
A subject transfer framework for EEG classification
On the Fundamental Limits of Recovering Tree Sparse Vectors From Noisy Linear Measurements
eng_Latn
30,529
A comparison of cepstral editing methods as signal pre-processing techniques for vibration-based bearing fault detection
Rolling Bearing Fault Signal Extraction Based on Stochastic Resonance-Based Denoising and VMD
Self-insight into emotional and cognitive abilities is not related to higher adjustment
eng_Latn
30,530
In recent years, Internet of Things (IoT) technology has brought many applications and developments for wearable devices, and the use of non-invasive electroencephalography (EEG) instruments to measure attention has been a topic of discussion. However, the correlation between attention and cognitive load has rarely been analyzed by data mining. For this reason, this study used head-mounted non-invasive EEG instruments based on IoT technology to collect attention values related to two courses and extracurricular activities and used a cognitive load questionnaire to investigate the cognitive loads of subjects. Correlation analysis was carried out through data mining technology to find the correlation between attention and cognitive load. In addition, six short-term experiments and relaxation experiments were designed to measure the subjects’ maximum attention and minimum attention values, so as to propose a strategy for setting the attention baseline. According to the results of the various experiments, subjects suffering from overload showed a state of inattention during the whole activity while subjects suffering a high load showed low sustained attention; only subjects with a medium load showed high sustained attention. Subjects with a low load showed inattention for nearly the entire activity. In this study, a strategy for setting an attention baseline was proposed to normalize the attention values from different EEG instruments. The correlation between attention value and cognitive load is analyzed using association rule mining technology so that the change of cognitive load could be effectively estimated by measuring the attention value instead of using questionnaire in the future.
Electroencephalographic measurements are commonly used in medical and research areas. This review article presents an introduction into EEG measurement. Its purpose is to help with orientation in EEG field and with building basic knowledge for performing EEG recordings. The article is divided into two parts. In the first part, background of the subject, a brief historical overview, and some EEG related research areas are given. The second part explains EEG recording. Modern medicine applies variety of imaging techniques of the human body. The group of electrobiological measurements comprises items as electrocardiography (ECG, heart), electromyography (EMG, muscular contractions), electroencephalography (EEG, brain), magnetoencephalography (MEG, brain), electrogastrography (EGG, stomach), electrooptigraphy (EOG, eye dipole field). Imaging techniques based on different physical principles include computer tomography (CT), magnetic resonance imaging (MRI), functional MRI (fMRI), positron emission tomography (PET), and single photon emission computed tomography (SPECT). Electroencephalography is a medical imaging technique that reads scalp electrical activity generated by brain structures. The electroencephalogram (EEG) is defined as electrical activity of an alternating type recorded from the scalp surface after being picked up by metal electrodes and conductive media (1). The EEG measured directly from the cortical surface is called electrocortiogram while when using depth probes it is called electrogram. In this article, we will refer only to EEG measured from the head surface. Thus electroencephalographic reading is a completely non-invasive procedure that can be applied repeatedly to patients, normal adults, and children with virtually no risk or limitation. When brain cells (neurons) are activated, local current flows are produced. EEG measures mostly the currents that flow during synaptic excitations of the dendrites of many pyramidal neurons in the cerebral cortex. Differences of electrical potentials are caused by summed postsynaptic graded potentials from pyramidal cells that create electrical dipoles between soma (body of neuron) and apical dendrites (neural branches). Brain electrical current consists mostly of Na+, K+, Ca++, and Cl- ions that are pumped through channels in neuron membranes in the direction governed by membrane potential (2). The detailed microscopic picture is more sophisticated, including different types of synapses involving variety of neurotransmitters. Only large populations of active neurons can generate electrical activity recordable on the head surface. Between electrode and neuronal layers current penetrates through skin, skull and several other layers. Weak electrical signals detected by the scalp electrodes are massively amplified, and then displayed on paper or stored to computer memory (3). Due to capability to reflect both the normal and abnormal electrical activity of the brain, EEG has been found to be a very powerful tool in the field of neurology and clinical neurophysiology. The human brain electric activity starts around the 17-23 week of prenatal development. It is assumed that at birth the full number of neural cells is already developed, roughly 10 11 neurons (4). This makes an average density of 10 4 neurons per cubic mm. Neurons are mutually connected into neural nets through synapses. Adults have about 500 trillion (5.10 14 ) synapses. The number of synapses per one neuron with age increases, however the number of neurons with age decreases, thus the total number of synapses decreases with age too. From the anatomical point of view, the brain can be divided into three sections: cerebrum, cerebellum, and brain stem. The cerebrum consists of left and right hemisphere with highly convoluted surface layer called cerebral cortex. The cortex is a
Berzelius failed to make use of Faraday's electrochemical laws in his laborious determination of equivalent weights.
eng_Latn
30,531
Recent theoretical and experimental work in imaging neuroscience reveals that activations inferred from functional MRI data have sparse structure. We view sparse representation as a problem in Bayesian inference, following a machine learning approach, and construct a structured generative latent-variable model employing adaptive sparsity-inducing priors. The construction allows for automatic complexity control and regularization as well as denoising. Experimental results with benchmark datasets show that the proposed algorithm outperforms standard tools for model-free decompositions such as independent component analysis.
A single-task functional magnetic resonance imaging (fMRI) experiment may only partially highlight alterations to functional brain networks affected by a particular disorder. Multivariate analysis across multiple fMRI tasks may increase the sensitivity of fMRI-based diagnosis. Prior research using multi-task analysis in fMRI, such as those that use joint independent component analysis (jICA), has mainly assumed that brain activity patterns evoked by different tasks are independent. This may not be valid in practice. Here, we use sparsity, which is a natural characteristic of fMRI data in the spatial domain, and propose a joint sparse representation analysis (jSRA) method to identify common information across different functional subtraction (contrast) images in data from a multi-task fMRI experiment. Sparse representation methods do not require independence, or that the brain activity patterns be nonoverlapping. We use functional subtraction images within the joint sparse representation analysis to generate joint activation sources and their corresponding sparse modulation profiles. We evaluate the use of sparse representation analysis to capture individual differences with simulated fMRI data and with experimental fMRI data. The experimental fMRI data was acquired from 16 young (age: 19–26) and 16 older (age: 57–73) adults obtained from multiple speech comprehension tasks within subjects, where an independent measure (namely, age in years) can be used to differentiate between groups. Simulation results show that this method yields greater sensitivity, precision, and higher Jaccard indexes (which measures similarity and diversity of the true and estimated brain activation sources) than does the jICA method. Moreover, superiority of the jSRA method in capturing individual differences was successfully demonstrated using experimental fMRI data.
Berzelius failed to make use of Faraday's electrochemical laws in his laborious determination of equivalent weights.
eng_Latn
30,532
In conventional feature extraction based on independent component analysis (ICA), feature selection and dimensional reduction are carried out only through PCA preprocessing, so the importance of independent components is not taken into consideration. In order to overcome this problem, a new ICA feature selection based on genetic algorithm is proposed in this paper. To demonstrate its effectiveness, recognition experiments is performed for face recognition and iris recognition.
This paper proposes to generalize the notion of independent component analysis (ICA) to the notion of multidimensional independent component analysis (MICA). We start from the ICA or blind source separation (BSS) model and show that it can be uniquely identified provided it is properly parameterized in terms of one-dimensional subspaces. From this standpoint, the BSS/ICA model is generalized to multidimensional components. We discuss how ICA standard algorithms can be adapted to MICA decomposition. The relevance of these ideas is illustrated by a MICA decomposition of ECG signals.
Averaged event-related potential (ERP) data recorded from the human scalp reveal electroencephalographic (EEG) activity that is reliably time-locked and phase-locked to experimental events. We report here the application of a method based on information theory that decomposes one or more ERPs recorded at multiple scalp sensors into a sum of components with fixed scalp distributions and sparsely activated, maximally independent time courses. Independent component analysis (ICA) decomposes ERP data into a number of components equal to the number of sensors. The derived components have distinct but not necessarily orthogonal scalp projections. Unlike dipole-fitting methods, the algorithm does not model the locations of their generators in the head. Unlike methods that remove second-order correlations, such as principal component analysis (PCA), ICA also minimizes higher-order dependencies. Applied to detected—and undetected—target ERPs from an auditory vigilance experiment, the algorithm derived ten components that decomposed each of the major response peaks into one or more ICA components with relatively simple scalp distributions. Three of these components were active only when the subject detected the targets, three other components only when the target went undetected, and one in both cases. Three additional components accounted for the steady-state brain response to a 39-Hz background click train. Major features of the decomposition proved robust across sessions and changes in sensor number and placement. This method of ERP analysis can be used to compare responses from multiple stimuli, task conditions, and subject states.
eng_Latn
30,533
The cumulative effect of repetitive subconcussive collisions on the structural and functional integrity of the brain remains largely unknown. Athletes in collision sports, like football, experience a large number of impacts across a single season of play. The majority of these impacts, however, are generally overlooked, and their long-term consequences remain poorly understood. This study sought to examine the effects of repetitive collisions across a single competitive season in NCAA Football Bowl Subdivision athletes using advanced neuroimaging approaches. Players were evaluated before and after the season using multiple MRI sequences, including T1-weighted imaging, diffusion tensor imaging (DTI), arterial spin labeling (ASL), resting-state functional MRI (rs-fMRI), and susceptibility weighted imaging (SWI). While no significant differences were found between pre- and post-season for DTI metrics or cortical volumes, seed-based analysis of rs-fMRI revealed significant (p < 0.05) changes in functional connections to right isthmus of the cingulate cortex (ICC), left ICC, and left hippocampus. ASL data revealed significant (p < 0.05) increases in global cerebral blood flow (CBF), with a specific regional increase in right postcentral gyrus. SWI data revealed that 44% of the players exhibited outlier rates (p < 0.05) of regional decreases in SWI signal. Of key interest, athletes in whom changes in rs-fMRI, CBF and SWI were observed were more likely to have experienced high G impacts on a daily basis. These findings are indicative of potential pathophysiological changes in brain integrity arising from only a single season of participation in the NCAA Football Bowl Subdivision, even in the absence of clinical symptoms or a diagnosis of concussion. Whether these changes reflect compensatory adaptation to cumulative head impacts or more lasting alteration of brain integrity remains to be further explored.
Brain registration to a stereotaxic atlas is an effective way to report anatomic locations of interest and to perform anatomic quantification. However, existing stereotaxic atlases lack comprehensive coordinate information about white matter structures. In this paper, white matter specific atlases in stereotaxic coordinates are introduced. As a reference template, the widely-used ICBM-152 was used. The atlas contains fiber orientation maps and hand-segmented white matter parcellation maps based on diffusion tensor imaging (DTI). Registration accuracy by linear and nonlinear transformation was measured, and automated template-based white matter parcellation was tested. The results showed high correlation between the manual ROI-based and the automated approaches for normal adult populations. The atlases are freely available and believed to be a useful resource as a target template and for automated parcellation methods.
In this paper, we deal with low-complexity near-optimal detection/equalization in large-dimension multiple-input multiple-output inter-symbol interference (MIMO-ISI) channels using message passing on graphical models. A key contribution in the paper is the demonstration that near-optimal performance in MIMO-ISI channels with large dimensions can be achieved at low complexities through simple yet effective simplifications/approximations, although the graphical models that represent MIMO-ISI channels are fully/densely connected (loopy graphs). These include 1) use of Markov random field (MRF)-based graphical model with pairwise interaction, in conjunction with message damping, and 2) use of factor graph (FG)-based graphical model with Gaussian approximation of interference (GAI). The per-symbol complexities are O(K2nt2) and O(Knt) for the MRF and the FG with GAI approaches, respectively, where K and nt denote the number of channel uses per frame, and number of transmit antennas, respectively. These low-complexities are quite attractive for large dimensions, i.e., for large Knt. From a performance perspective, these algorithms are even more interesting in large-dimensions since they achieve increasingly closer to optimum detection performance for increasing Knt. Also, we show that these message passing algorithms can be used in an iterative manner with local neighborhood search algorithms to improve the reliability/performance of M-QAM symbol detection.
eng_Latn
30,534
Objective Transcranial direct current stimulation (tDCS) is a neuromodulation scheme that delivers a small current via electrodes placed on the scalp. The target is generally assumed to be under the electrode, but deep brain regions could also be involved due to the large current spread between the electrodes. This study aims to computationally evaluate if group-level hotspots exist in deep brain regions for different electrode montages. Methods We computed the tDCS-generated electric fields (EFs) in a group of subjects using interindividual registration methods that permitted the projection of EFs from individual realistic head models (n = 18) to a standard deep brain region. Results The spatial distribution and peak values (standard deviation of 14%) of EFs varied significantly. Nevertheless, group-level EF hotspots appeared in deep brain regions. The caudate had the highest field peaks in particular for F3-F4 montage (70% of maximum cortical EF), while other regions reach field peaks of 50%. Conclusions tDCS at deeper regions may include not only modulation via underlying cortical or subcortical circuits but also modulation of deep brain regions. Significance The presented EF atlas in deep brain regions can be used to explain tDCS mechanism or select the most appropriate tDCS montage.
Electro-stimulation or modulation of deep brain regions is commonly used in clinical procedures for the treatment of several nervous system disorders. In particular, transcranial direct current stimulation (tDCS) is widely used as an affordable clinical application that is applied through electrodes attached to the scalp. However, it is difficult to determine the amount and distribution of the electric field (EF) in the different brain regions due to anatomical complexity and high inter-subject variability. Personalized tDCS is an emerging clinical procedure that is used to tolerate electrode montage for accurate targeting. This procedure is guided by computational head models generated from anatomical images such as MRI. Distribution of the EF in segmented head models can be calculated through simulation studies. Therefore, fast, accurate, and feasible segmentation of different brain structures would lead to a better adjustment for customized tDCS studies. In this study, a single-encoder multi-decoders convolutional neural network is proposed for deep brain segmentation. The proposed architecture is trained to segment seven deep brain structures using T1-weighted MRI. Network generated models are compared with a reference model constructed using a semi-automatic method, and it presents a high matching especially in Thalamus (Dice Coefficient (DC) = 94.70%), Caudate (DC = 91.98%) and Putamen (DC = 90.31%) structures. Electric field distribution during tDCS in generated and reference models matched well each other, suggesting its potential usefulness in clinical practice.
Electro-stimulation or modulation of deep brain regions is commonly used in clinical procedures for the treatment of several nervous system disorders. In particular, transcranial direct current stimulation (tDCS) is widely used as an affordable clinical application that is applied through electrodes attached to the scalp. However, it is difficult to determine the amount and distribution of the electric field (EF) in the different brain regions due to anatomical complexity and high inter-subject variability. Personalized tDCS is an emerging clinical procedure that is used to tolerate electrode montage for accurate targeting. This procedure is guided by computational head models generated from anatomical images such as MRI. Distribution of the EF in segmented head models can be calculated through simulation studies. Therefore, fast, accurate, and feasible segmentation of different brain structures would lead to a better adjustment for customized tDCS studies. In this study, a single-encoder multi-decoders convolutional neural network is proposed for deep brain segmentation. The proposed architecture is trained to segment seven deep brain structures using T1-weighted MRI. Network generated models are compared with a reference model constructed using a semi-automatic method, and it presents a high matching especially in Thalamus (Dice Coefficient (DC) = 94.70%), Caudate (DC = 91.98%) and Putamen (DC = 90.31%) structures. Electric field distribution during tDCS in generated and reference models matched well each other, suggesting its potential usefulness in clinical practice.
eng_Latn
30,535
Achieving secure vehicles equipped with accident prevention systems is one of the greatest passions of researchers in automotive laboratories. This article provides a detail description on the state of the art techniques for road situation monitoring and driver's behavior analysis (human factors). The focus would be to gain a practical multifaceted approach in order to simultaneous analysis of driver's distraction, and potential road hazards to make an emergency intervention in case of dangerous driving situations. In this regard, a diversity of information via Iris/Pupil status monitoring and EEG spectrum has been gathered and then fused with out-vehicle sensors such as RADAR, LIDAR, Ultrasonic and Vision. In order to cope with computational complexities due to multiple sensors, a heuristic method of multi sensory data fusion and fuzzy solutions has been developed. All discussions are based on real sensors. The method could be applied on various driver assistance systems such as cruise control, overtaking and lane keeping systems.
EEG spectral power has been shown to correlate with level of arousal and alertness in humans. In this paper, we assess its usefulness in the detection of behavioral microsleeps (BMs). Eight non-sleep-deprived normal subjects performed two 1-hour sessions of a continuous tracking task while EEG and facial video were recorded. BMs were identified independent of tracking performance by a human rater by viewing the video recordings. Spectral power, normalized spectral power, and power ratios in the standard EEG bands were calculated using the Burg method on 16 bipolar derivations to form an EEG feature matrix. PCA was used to reduce the dimensionality of the feature matrix and linear discriminant analysis used to form a classifier for each subject. The 8 classifiers were combined using stacked generalization to create an overall detection model and N-fold cross-validation used to determine its performance (�- = 0.30±0.05, mean±SE). While modest, the detection of BMs at such a high temporal resolution (1 s) has not been achieved previously other than by our group.
We prove that groups acting geometrically on delta-quasiconvex spaces contain no essential Baumslag-Solitar quotients as subgroups. This implies that they are translation discrete, meaning that the translation numbers of their nontorsion elements are bounded away from zero.
eng_Latn
30,536
Magnetoencephalography (MEG) is a functional modality to register magnetic brain activity with high spatiotemporal resolution. Since distortion of magnetic fields by the skin, skull and cerebrospinal fluids is negligible, the technique offers an almost undistorted view on brain activity. While MEG systems are still expensive and complex, the technique's characteristics offer promising possibilities for the investigation of epilepsy patients, for example, for focus localization and presurgical functional mapping. This review gives an overview of the method and discusses advantages and limitations in the clinical context of presurgical epilepsy diagnosis.
To increase the reliability for the non-invasive determination of the irritative zone in presurgical epilepsy diagnosis, we introduce here a new experimental and methodological source analysis pipeline that combines the complementary information in EEG and MEG, and apply it to data from a patient, suffering from refractory focal epilepsy. Skull conductivity parameters in a six compartment finite element head model with brain anisotropy, constructed from individual MRI data, are estimated in a calibration procedure using somatosensory evoked potential (SEP) and field (SEF) data. These data are measured in a single run before acquisition of further runs of spontaneous epileptic activity. Our results show that even for single interictal spikes, volume conduction effects dominate over noise and need to be taken into account for accurate source analysis. While cerebrospinal fluid and brain anisotropy influence both modalities, only EEG is sensitive to skull conductivity and conductivity calibration significantly reduces the difference in especially depth localization of both modalities, emphasizing its importance for combining EEG and MEG source analysis. On the other hand, localization differences which are due to the distinct sensitivity profiles of EEG and MEG persist. In case of a moderate error in skull conductivity, combined source analysis results can still profit from the different sensitivity profiles of EEG and MEG to accurately determine location, orientation and strength of the underlying sources. On the other side, significant errors in skull modeling are reflected in EEG reconstruction errors and could reduce the goodness of fit to combined datasets. For combined EEG and MEG source analysis, we therefore recommend calibrating skull conductivity using additionally acquired SEP/SEF data.
Using a mobile device in a social context should not cause embarrassment and disruption to the immediate environment. Interaction with mobile and wearable devices needs to be subtle, discreet and unobtrusive. Therefore, we promote the idea of "intimate interfaces": discrete interfaces that allow control of mobile devices through subtle gestures in order to gain social acceptance. To achieve this goal, we present an electromyogram (EMG) based wearable input device which recognizes isometric muscular activity: activity related to very subtle or no movement at all. In the online experiment reported, the EMG device, worn on an armband around the bicep, was able to reliably recognize a motionless gesture without calibration or training across users with different muscle volumes. Hence, EMG-based input devices can provide an effective solution for designing mobile interfaces that are subtle and intimate, and therefore socially acceptable.
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Background. Eye trackers are widely used among people with amyotrophic lateral sclerosis, and their benefits to quality of life have been previously shown. On the contrary, Brain-computer interfaces (BCIs) are still quite a novel technology, which also serves as an access technology for people with severe motor impairment. Objective. To compare a visual P300-based BCI and an eye tracker in terms of information transfer rate (ITR), usability, and cognitive workload in users with motor impairments. Methods. Each participant performed 3 spelling tasks, over 4 total sessions, using an Internet browser, which was controlled by a spelling interface that was suitable for use with either the BCI or the eye tracker. At the end of each session, participants evaluated usability and cognitive workload of the system. Results. ITR and System Usability Scale (SUS) score were higher for the eye tracker (Wilcoxon signed-rank test: ITR T = 9, P = .016; SUS T = 12.50, P = .035). Cognitive workload was higher for the BCI (T = 4; P = .003). Conclusions. Although BCIs could be potentially useful for people with severe physical disabilities, we showed that the usability of BCIs based on the visual P300 remains inferior to eye tracking. We suggest that future research on visual BCIs should use eye tracking-based control as a comparison to evaluate performance or focus on nonvisual paradigms for persons who have lost gaze control.
Increasing number of research activities and different types of studies in brain-computer interface (BCI) systems show potential in this young research area. Research teams have studied features of different data acquisition techniques, brain activity patterns, feature extraction techniques, methods of classifications, and many other aspects of a BCI system. However, conventional BCIs have not become totally applicable, due to the lack of high accuracy, reliability, low information transfer rate, and user acceptability. A new approach to create a more reliable BCI that takes advantage of each system is to combine two or more BCI systems with different brain activity patterns or different input signal sources. This type of BCI, called hybrid BCI, may reduce disadvantages of each conventional BCI system. In addition, hybrid BCIs may create more applications and possibly increase the accuracy and the information transfer rate. However, the type of BCIs and their combinations should be considered carefully. In this paper, after introducing several types of BCIs and their combinations, we review and discuss hybrid BCIs, different possibilities to combine them, and their advantages and disadvantages.
Heavy trucks are mostly used for international transportations, with longer highways and long driving hours contributing to corresponding increases in the driver’s fatigue that is related to accide...
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PURPOSE ::: To determine the intracranial EEG features responsible for producing the various ictal scalp rhythms, which we previously identified in a new EEG classification for temporal lobe seizures. ::: ::: ::: METHODS ::: In 24 patients, we analyzed simultaneous intracranial and surface ictal EEG recordings (64 total channels) obtained from a combination of intracerebral depth, subdural strip, and scalp electrodes. ::: ::: ::: RESULTS ::: Four of four patients with Type 1 scalp seizure patterns had mesial temporal seizure onsets. However, discharges confined to the hippocampus produced no scalp EEG rhythms. The regular 5- to 9-Hz subtemporal and temporal EEG pattern of Type 1a seizures required the synchronous recruitment of adjacent inferolateral temporal neocortex. Seizure discharges confined to the mesiobasal temporal cortex produced a vertex dominant rhythm (Type 1c) due to the net vertical orientation of dipolar sources located there. Ten of 13 patients with Type 2 seizures had inferolateral or lateral, temporal neocortical seizure onsets. Initial cerebral ictal activity was typically a focal or regional, low voltage, fast rhythm (20-40 Hz) that was often associated with widespread background flattening. Only an attenuation of normal rhythms was reflected in scalp electrodes. Irregular 2- to 4-Hz cortical ictal rhythms that commonly followed resulted in a comparably slow and irregular scalp EEG pattern (Type 2a). Type 2C seizures showed regional, periodic, 1- to 4-Hz sharp waves following intracranial seizure onset. Seven patients had Type 3 scalp seizures, which were characterized by diffuse slowing or attenuation of background scalp EEG activity. This resulted when seizure activity was confined to the hippocampus, when there was rapid seizure propagation to the contralateral temporal lobe, or when cortical ictal activity failed to achieve widespread synchrony. ::: ::: ::: CONCLUSIONS ::: Type 1, 2, and 3 scalp EEG patterns of temporal lobe seizures are not a reflection of cortical activity at seizure onset. Differences in the subsequent development, propagation, and synchrony of cortical ictal discharges produce the characteristic scalp EEG rhythms.
Objectives : The ability to analyze patterns of recorded seizure activity is important in the localization and classification of seizures. Ictal evolution is typically a dynamic process with signals composed of multiple frequencies; this can limit or complicate methods of analysis. The recently-developed matching pursuit algorithm permits continuous time–frequency analyses, making it particularly appealing for application to these signals. The studies here represent the initial applications of this method to intracranial ictal recordings. Methods : Mesial temporal onset partial seizures were recorded from 9 patients. The data were analyzed by the matching pursuit algorithm were continuous digitized single channel recordings from the depth electrode contact nearest the region of seizure onset. Time–frequency energy distributions were plotted for each seizure and correlated with the intracranial EEG recordings. Results : Periods of seizure initiation, transitional rhythmic bursting activity, organized rhythmic bursting activity and intermittent bursting activity were identified. During periods of organized rhythmic bursting activity, all mesial temporal onset seizures analyzed had a maximum predominant frequency of 5.3–8.4 Hz with a monotonic decline in frequency over a period of less than 60 s. The matching pursuit method allowed for time–frequency decomposition of entire seizures. Conclusions : The matching pursuit method is a valuable tool for time–frequency analyses of dynamic seizure activity. It is well suited for application to the non-stationary activity that typically characterizes seizure evolution. Time–frequency patterns of seizures originating from different brain regions can be compared using the matching pursuit method.
We prove that groups acting geometrically on delta-quasiconvex spaces contain no essential Baumslag-Solitar quotients as subgroups. This implies that they are translation discrete, meaning that the translation numbers of their nontorsion elements are bounded away from zero.
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Michael Mace, et al, 'An automated approach towards detecting complex behaviours in deep brain oscillations', Journal of Neuroscience Methods, Vol. 224: 66-78, December 2013, doi: https://doi.org/10.1016/j.jneumeth.2013.11.019. Published by Elsevier. Copyright © 2013 Elsevier B.V.
Objective. Correlating electrical activity within the human brain to movement is essential for developing and refining interventions (e.g. deep brain stimulation (DBS)) to treat central nervous system disorders. It also serves as a basis for next generation brain–machine interfaces (BMIs). This study highlights a new decoding strategy for capturing movement and its corresponding laterality from deep brain local field potentials (LFPs). Approach. LFPs were recorded with surgically implanted electrodes from the subthalamic nucleus or globus pallidus interna in twelve patients with Parkinson's disease or dystonia during a visually cued finger-clicking task. We introduce a method to extract frequency dependent neural synchronization and inter-hemispheric connectivity features based upon wavelet packet transform (WPT) and Granger causality approaches. A novel weighted sequential feature selection algorithm has been developed to select optimal feature subsets through a feature contribution measure. This is particularly useful when faced with limited trials of high dimensionality data as it enables estimation of feature importance during the decoding process. Main results. This novel approach was able to accurately and informatively decode movement related behaviours from the recorded LFP activity. An average accuracy of 99.8% was achieved for movement identification, whilst subsequent laterality classification was 81.5%. Feature contribution analysis highlighted stronger contralateral causal driving between the basal ganglia hemispheres compared to ipsilateral driving, with causality measures considerably improving laterality discrimination. Significance. These findings demonstrate optimally selected neural synchronization alongside causality measures related to inter-hemispheric connectivity can provide an effective control signal for augmenting adaptive BMIs. In the case of DBS patients, acquiring such signals requires no additional surgery whilst providing a relatively stable and computationally inexpensive control signal. This has the potential to extend invasive BMI, based on recordings within the motor cortex, by providing additional information from subcortical regions.
Berzelius failed to make use of Faraday's electrochemical laws in his laborious determination of equivalent weights.
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ABSTRACT Geospatial Intelligence Analysts are currently faced with an enor mous volume of imagery, only a fraction of which can be processed or reviewed in a timely operational manner. Computer-based target detection efforts have failed to yield the speed, flexibility and accuracy of the human visual system. Rather than focus solely on artificial systems, we hypothesize that the human visual system is still the best target detection apparatus currently in use, and with the addition of neuroscience-based measurement capabilities it can surpass the throughput of the unaided human several-fold. Using electroencephalography (EEG), Thorpe et al 1 described a fast signal in the brain associated with the early detection of targets in static imagery using a Rapid Serial Visual Presentation (RSVP) paradigm. This finding suggests that it may be possible to extract target detection signals from complex imagery in real time utilizing non-invasive neurophysiological assessment tools. To transform this phenomenon into a capability for defense applications, the Defense Advanced Research Projects Agency (DARPA) currently is sponsoring an effort titled Neurotechnology for Intelligence Analysts (NIA). The vision of the NIA program is to revolutionize the way that analysts handle intelligence imagery, increasing both the throughput of imag ery to the analyst and overall accu racy of the assessments. Successful development of a neurobiolog ically-based image triage system w ill enable image analysts to train more effectively and process imagery w ith greater speed and precision. Keywords: Neuroscience, satellite imagery, electroencephalography, near-infrared spectroscopy, independent component analysis, linear discriminant analysis, rapid serial visual presentation, classifier, change detection, expert
Averaged event-related potential (ERP) data recorded from the human scalp reveal electroencephalographic (EEG) activity that is reliably time-locked and phase-locked to experimental events. We report here the application of a method based on information theory that decomposes one or more ERPs recorded at multiple scalp sensors into a sum of components with fixed scalp distributions and sparsely activated, maximally independent time courses. Independent component analysis (ICA) decomposes ERP data into a number of components equal to the number of sensors. The derived components have distinct but not necessarily orthogonal scalp projections. Unlike dipole-fitting methods, the algorithm does not model the locations of their generators in the head. Unlike methods that remove second-order correlations, such as principal component analysis (PCA), ICA also minimizes higher-order dependencies. Applied to detected—and undetected—target ERPs from an auditory vigilance experiment, the algorithm derived ten components that decomposed each of the major response peaks into one or more ICA components with relatively simple scalp distributions. Three of these components were active only when the subject detected the targets, three other components only when the target went undetected, and one in both cases. Three additional components accounted for the steady-state brain response to a 39-Hz background click train. Major features of the decomposition proved robust across sessions and changes in sensor number and placement. This method of ERP analysis can be used to compare responses from multiple stimuli, task conditions, and subject states.
Berzelius failed to make use of Faraday's electrochemical laws in his laborious determination of equivalent weights.
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EEG spectrogram classification employing ANN for IQ application
The term intelligence is associated in many areas such as linguistic, mathematical, music and art. In this paper, Intelligence Quotient (IQ) is measured using Electroencephalogram (EEG) from the human brain. The EEG signals are then used to form the spectrogram images, from which a large data of Gray Level Co-occurrence Matrix (GLCM) texture features were extracted. Then, Principal Component Analysis (PCA) is used to reduce the big matrix, and is followed with the classification of the EEG spectrogram image in IQ application using ANN algorithm. The results are then validated based on the concept of Raven's Standard Progressive Matrices (RPM) IQ test. The results showed that the ANN is able to classify the EEG spectrogram image with 88.89% accuracy and 0.0633 MSE.
A finite element model was built up based on the 3-D model of the large and high effective milling machine of CNC.The finite element analysis software-ANSYS was applied to the modal analysis of the machine tool.The preceding nine step natural vibration frequencies and its corresponding vibration modes were obtained.The deficiency of the machine tool was pointed out.Four different kinds of models were established towards the deficiency and their calculations were solved.A reasonable scheme was provided through the comparison.
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Stimulation of the Deep Brain for the Treatment of Chronic Pain
Deep brain stimulation is a therapy that has been used for more than half a century to treat chronic pain. The first use of these treatments occurred in the 1950s when neurosurgeons stimulated the septal region nuclei in patients with psychiatric diseases who also suffered from chronic pain. Over the next twenty years, the therapy evolved to include the sensory thalamic nuclei to treat pain of neuropathic origin. Other targets have included the periaqueductal gray and periventricular gray, and several new targets are under current investigation. Outcomes for both facial and extremity pain have been positive and the use of this modality in the neuromodulation algorithm is increasingly helpful to those who have severe pain.
Background: Cardiac resynchronization therapy (CRT) has significant nonresponse rates. We assessed whether machine learning (ML) could predict CRT response beyond current guidelines. Methods: We an...
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Neural representation of different 3D architectural images: An EEG study
This work has been supported in part by the Spanish National Research Program (MAT2015-69967-C3-1), by a research grant of the Spanish Blind Organization (ONCE) and by Programa de Ayudas a Grupos de Excelencia de la Region de Murcia, from Fundacion Seneca, Agencia de ciencia y Tecnologia de la Region de Murcia.
The continuous increment in the power of modern high-performance computing (HPC) platforms has further stimulated the interest of the electronic structure calculations community for more computationally challenging studies.
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Electromyographic power spectral changes associated with the sleep-awake cycle and with diazepam treatment in the rat
Abstract Power spectral analysis was used to study temporalis muscle EMG activities during the sleep-awake cycle in the rat. EMG spectra derived from EMG during the states of slow-wave sleep. REM sleep and wakefulness demonstrated qualitative and quantitative differences. Diazepam treatment produced reductions in EMG spectral power during wakefulness. Thus, our experimental model allows qualitative and quantitative delineation of EMG activity associated with behavioral changes or drug treatments.
In this paper we propose a Gaussian decomposition approach to compute the SPECT imaging system matrix. The flexibility of our method allows the implementation of a wide range of imaging systems. With the system matrix, we predict the variance in reconstructed images using the Fisher information matrix and local block circulant approximation. We present results of noise prediction for 3 multi-pinhole systems and 3 multi-slit slit-slat systems, each of them being designed to be inserted in an MRI system. Results show that for a particular phantom 2×2 multi-pinhole and 2 slits slit-slat systems achieve lowest variance.
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Neuroimaging in Neonatal Encephalopathy
Investigators at St Francis Medical Center, Cape Girardeau, MO, and multiple centers in the US, using a database from the Vermont Oxford Network Registry, studied the pattern of use and findings of computed tomography, MRI, and intracranial ultrasound in the evaluation of infants with neonatal encephalopathy.
We have performed near-infrared spectroscopy on the forehead of human subjects during all-night sleep. The evolution of the sleep stages during the night has been identified by electroencephalography.
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Automatic detection and quantification of brain midline shift using anatomical marker model
Moderate and severe traumatic brain injury in adults
Clinical trials in head injury.
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Magnetic resonance imaging in pediatric appendicitis: a systematic review
Diagnostic performance of contrast-enhanced MR for acute appendicitis and alternative causes of abdominal pain in children
A Novel Multi-Class EEG-Based Sleep Stage Classification System
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EEG-Based Brain-Computer Interfaces: A Thorough Literature Survey
Brain–computer interface (BCI) technology has been studied with the fundamental goal of helping disabled people communicate with the outside world using brain signals. In particular, a large body of research has been reported in the electroencephalography (EEG)-based BCI research field during recent years. To provide a thorough summary of recent research trends in EEG-based BCIs, the present study reviewed BCI research articles published from 2007 to 2011 and investigated (a) the number of published BCI articles, (b) BCI paradigms, (c) aims of the articles, (d) target applications, (e) feature types, (f) classification algorithms, (g) BCI system types, and (h) nationalities of the author. The detailed survey results are presented and discussed one by one. [Supplemental materials are available for this article. Go to the publisher's online edition of International Journal of Human-Computer Interaction to view the free supplemental file: Supplementary Tables.pdf.]
Background: Cardiac resynchronization therapy (CRT) has significant nonresponse rates. We assessed whether machine learning (ML) could predict CRT response beyond current guidelines. Methods: We an...
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A Theory of Excitons in Strained Molecular Crystals
Abstract This paper examines theoretically the effects of strain on the exciton properties of some molecular crystals. It is found that the parameters of exciton theory are relatively sensitive to changes in lattice parameters of the crystal, and thus strain can result in significant changes in the crystal spectrum. The method proceeds through strain derivatives of the exciton parameters, which are evaluated for the unstressed crystal geometries. In this way, the influence of pressure and temperature on the spectrum is examined. The strain derivatives are evaluated for anthracene and naphthalene for light incident on the ab-face, and the theory used to re-examine some of the features of the first singlet of anthracene. Good agreement with experiment is obtained.
Removing artifacts and background EEG from multichannel interictal and ictal EEG has become a major research topic in EEG signal processing in recent years. We applied for this purpose a recently developed subspace-based method for modelling the common dynamics in multichannel signals. When the epileptiform activity is common in the majority of channels and the artifacts appear only in a few channels the proposed method can be used to remove the latter. The performance of the method was tested on simulated data for different noise levels. For high noise levels the method was still able to identify the common dynamics. In addition, the method was applied to a real life EEG recording. Also in this case the muscle artifacts were removed successfully. For both the synthetic data and the analyzed real life data the results were compared with the results obtained with principal component analysis (PCA). In both cases the proposed method performed better than PCA
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A Serious Game to Build a Database for ErrP Signal Recognition
Brain wave signals allow communication between user and computer in a system called Brain-Computer Interface. Signal processing can detect attention, engagement, and errors in a task. Error-Related Potentials (ErrP) can be extracted from brain signals with noise, however, it is quite complicated to be recognized and accurate. This paper presents a new database, using gaming and a humanoid robot to induce the occurrence of user errors and methods to extract signal features. A Haar wavelet was used to feature extraction, and a MultiLayer Perceptron (MLP) and a Convolutional Neural Network (CNN) to signal classification. Several experiments are presented demonstrating that wavelet extraction outperformed Fourier transform to extract the error and MLP performed a consistent accuracy.
Aiming at the problems of great demand of samples, applicability and simulation precision of the routine empirical statistical soil erosion models, based on the reasonable extension of the high-order grey dispersed array dynamic model )
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A novel eye surgery simulator for exercising operation task of inner limiting membrane peeling
We established a brand-new eye surgery simulator for training of peeling the inner limited membrane (ILM) which is superficial layer of retina. The artificial ILM could be peeled under water like as actual surgery. An artificial eye consisting of a fundus and eye ball parts was fabricated. The artificial eye could be installed in the simulator. The fundus part was mounted in the eyeball, which consisted of an artificial sclera, retina, and ILM. The artificial ILM was fabricated by chemically crosslinked PVA hydrogel. Thickness measurement and sensory evaluation of the fabricated ILM were done. We installed the eye model on an ocular surgery simulator, which could perform a sequence of operations like ILM peeling. Then, we succeeded in developing a novel ocular surgery simulator for peeling task of ILM and peeling under water like as the actual surgery.
A large number of Brain-Computer Interfaces (BCIs) are currently under development, or being proposed, for both medical and non-medical applications. These applications include advertising, market surveys, focus groups and gaming. For example, in 2008, the Nielsen Company acquired Neurofocus, for the development of neural engineering technologies aimed at better understanding customer needs and preferences [1]. In May 2013, Samsung, in collaboration with the University of Texas, demonstrated how BCIs could be used to control mobile devices [2]. In the same month, the first neurogaming conference gathered more than 50 involved companies [3]. In September 2013, Neuroware presented Neurocam, a wearable EEG system equipped with a camera. The system is set to automatically start recording moments of interest based on inferred information from users? neural signals [4].
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The electro-optical device and electronic equipment
The present invention provides a liquid crystal display and an electro-optical device of electronic apparatus or the like, which can display high-quality color images. Electro-optical device on a substrate (10) comprises: three sub-pixels (70R, 70G, 70B) and the red, green, and blue respectively corresponding to each color; 3 sampling switches (71R, 71G, 71B); 3 are electrically connected to each other three data lines sub-pixels and three sampling switch (6R, 6G, 6B); and the three sampling switches corresponding to each of three image signal lines (500); are electrically connected to each three sampling switches and three image signals the three lead-out wiring lines (72R, 72G, 72B), 3 sampling switch compared to the sampling switches corresponding to green in the other sampling switch 2, 3 closer to the image signal lines arranged.
Abstract In this paper we present a new correction method of inner filter effects that occurs when measuring fluorescence Excitation–Emission Matrices (EEM) of concentrated solutions. While traditional method requires absorption measurement or sample dilution(s), the Mirrored Cell Approach (MCA) only requires two different EEM of the considered sample: a first one using a traditional cell and a second one using a mirrored cell. The mathematical relationship between both models is originally exploited to obtain a simple numerical correction. Method is validated using a set of known mixtures. In addition we show that advanced multilinear analysis can be efficiently applied on to the corrected EEM.
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Applications of T1WI-3D-MP RAGE in the brain
Three-dimensional magnetization prepared rapid acquisition gradient echo sequences (T1WI-3D-MP RAGE) is a small-flip-angle, gradient-recalled-echo sequence with a 3D Fourier transform acquisition technique that has been implemented with 180° inversion recovery preparation pulse. This sequence considerably improved delineation of grey and white matter and small anatomical structures of brain due to its relatively temporal and spatial resolution, a relatively high signal-to-noise ratio (SNR), three-dimensional data acquired and post-processing capabilities. It has very important role not only in diagnosis of central nerves system but also a vital methods to get the digitized human brain atlas.
This paper firstly introduces the mathematical model of stepped frequency and MMW one dimensional range imaging technique.Secondly,the mathematical model of one dimensional range imaging for high range resolution MMW seeker when there is relative motion between the missile and the target is given.Using this mathematical model,the influence of missile-target relative motion on one dimensional range imaging is investigated by computer simulation.Finally,the value of the compensating error of this relative motion between the missile and the target is derived when one dimensional range imaging technique is used for target identification.
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Multiscale Simulation of Blood Flow in Brain Arteries with an Aneurysm
Multi-scale modeling of arterial blood flow can shed light on the interaction between events happening at micro- and meso-scales (i.e., adhesion of red blood cells to the arterial wall, clot formation) and at macro-scales (i.e., change in flow patterns due to the clot). Coupled numerical simulations of such multi-scale flow require state-of-the-art computers and algorithms, along with techniques for multi-scale visualizations. This animation presents results of studies used in the development of a multi-scale visualization methodology. First we use streamlines to show the path the flow is taking as it moves through the system, including the aneurysm. Next we investigate the process of thrombus (blood clot) formation, which may be responsible for the rupture of aneurysms, by concentrating on the platelet blood cells, observing as they aggregate on the wall of the aneurysm
A large number of Brain-Computer Interfaces (BCIs) are currently under development, or being proposed, for both medical and non-medical applications. These applications include advertising, market surveys, focus groups and gaming. For example, in 2008, the Nielsen Company acquired Neurofocus, for the development of neural engineering technologies aimed at better understanding customer needs and preferences [1]. In May 2013, Samsung, in collaboration with the University of Texas, demonstrated how BCIs could be used to control mobile devices [2]. In the same month, the first neurogaming conference gathered more than 50 involved companies [3]. In September 2013, Neuroware presented Neurocam, a wearable EEG system equipped with a camera. The system is set to automatically start recording moments of interest based on inferred information from users? neural signals [4].
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The use of parametric models in the detection of awareness during general anaesthesia [EEG processing]
The aim is to construct a method, based upon statistical pattern recognition techniques, including neural networks, whereby awareness during general anaesthesia may be detected. The data source for this system would be a single channel of the electroencephalogram (EEG). Pre-processing of data prior to input into the network is a critical component of the work, and it is here that parametric models have been utilised. A spectral representation has been extracted from the EEG based upon 1 second of data, using a lattice filter as the primary model; and a bispectral representation based upon 5 seconds of data has also been constructed, this time using a transversal filter as the underlying model. (5 pages)
According to background of real-time single IR target scene simulation system based on multi-DSP,an improved parallel render structure is putted forward.For this structure needs large memory which is limited in embedded DSP and high transfer bandwidth,a method which can reduce the memory and bandwidth in parallel raster is also putted forward.For the IR target in real simulation system never fill the entire imaging plane,this method estimates the maximal projection region which is the real imaging region.For the real imaging region is small than the entire imaging plane,the memory and transfer bandwidth needed is reduced.The experiment result is provided at the end.
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Early-Stage Pilot Study on Using Fractional-Order Calculus-Based Filtering for the Purpose of Analysis of Electroencephalography Signals
Abstract Analysis of Electroencephalography (EEG) signals has recently awoken the increased interest of numerous researchers all around the world with regard to rapid development of Brain-Computer Interaction-related research areas and because EEG signals are implemented in most of the non-invasive BCI systems, as they provide necessary information regarding activity of the brain. In this paper, a very early stage pilot study on implementation of filtering based on fractional-order calculus (Bi-Fractional Filters – BFF) for the purpose of EEG signal classification is presented in brief.
One may represent polynomials not only by their coefficients but also by arithmetic circuits which evaluate them. This idea allowed in the past fifteen years considerable complexity progress in effective polynomial equation solving. We present a circuit based computation model which captures all known symbolic elimination algorithms in effective Algebraic Geometry and exhibit a class of simple elimination problems which require exponential size circuits to be solved in this model. This implies that the known, circuit based elimination algorithms are already optimal.
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On the Simulation of the Brain Activity: A Brief Survey
This article represents the brief introduction into the issues of simulation of brain activity. Firstly, there is shown a physiological description of the human brain, which summarizes current knowledge and also points out its complexity. These facts were obtained through the technologies, which are intended for observing electrical activity of the brain; for example invasive methods, electroencephalography (EEG) and functional magnetic resonance imaging (fMRI). Then, there are described approaches to simulate the brain activity. First of them is a standard model, which is the basis of most current methods. Second model is based on simulation of brain rhythm changes. Finally, there is discussed possible utilization of complex networks to create a biological neural network.
Background: Cardiac resynchronization therapy (CRT) has significant nonresponse rates. We assessed whether machine learning (ML) could predict CRT response beyond current guidelines. Methods: We an...
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Removing Artifacts and Background Activity in Multichannel Electroencephalograms by Enhancing Common Activity
Removing artifacts and background EEG from multichannel interictal and ictal EEG has become a major research topic in EEG signal processing in recent years. We applied for this purpose a recently developed subspace-based method for modelling the common dynamics in multichannel signals. When the epileptiform activity is common in the majority of channels and the artifacts appear only in a few channels the proposed method can be used to remove the latter. The performance of the method was tested on simulated data for different noise levels. For high noise levels the method was still able to identify the common dynamics. In addition, the method was applied to a real life EEG recording. Also in this case the muscle artifacts were removed successfully. For both the synthetic data and the analyzed real life data the results were compared with the results obtained with principal component analysis (PCA). In both cases the proposed method performed better than PCA
This book chapter is largely a narrative version of a plenary keynote address I delivered on 2 November 2013 titled “Facing Academic Minders, the Instruments of Institutional Interference in Higher Education” at the Decolonizing Global Citizenship Education International Conference organized through the Centre for Global Citizenship Education and Research at the University of Alberta (Edmonton, Alberta, Canada).
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An EEG-based brain computer interface for rehabilitation and restoration of hand control following stroke using ipsilateral cortical physiology
The loss of motor control severely impedes activities of daily life. Brain computer interfaces (BCIs) offer new possibilities to treat nervous system injuries, but conventional BCIs use signals from primary motor cortex, the same sites most likely damaged in a stroke causing paralysis. Recent studies found distinct cortical physiology associated with contralesional limb movements in regions distinct from primary motor cortex. To capitalize on these findings, we designed and implemented a BCI that localizes and acquires these brain signals to drive a powered, hand orthotic which opens and closes a patient's hand.
This work presents a method based on Petri nets for estimating the number of registers needed for hardware implementation of behavioral descriptions. The proposed method considers the data-dependency graph represented by a Petri net model. Such a model along with the control flow, also represented by a Petri net model, describes behavioral specifications. This work is inserted in the context of hardware/software codesign systems.
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Regulator, Voltage generator, Semiconductor memory device and Voltage generating method
A regulator comprises: an input voltage control unit configured to adjust and output an inputted pumping voltage in response to a control signal changed according to a set target voltage; and a regulation portion configured to output the target voltage by regulating the adjusted pumping voltage. The regulator can reduce the current consumption by adjusting the pumping voltage inputted according to the size of the target voltage.
Abstract This study demonstrates how the rigid body registration parameters for good registration of serially acquired 3-D magnetic resonance images vary systematically when the registration routine is presented with a series of cropped data sets that are systematically positioned throughout the entire volume. The results of the registration of these subcubes are compared with the results of a single registration of the complete volume for two consecutive 3-D scans of the brain of a normal volunteer, with one scan having optimized shim coil currents and the other having all second-order shim coil currents set to zero. The technique is sensitive and able to reveal subvoxel misregistrations.
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A New Perimeter for Electroperimetry
The author represents a new perimeter based on the evaluation of the visual evoked cortical potentials (VECP). The equipment provides for stimulus diameter, duration, intensity, colour, and other parameters. The VECP signal is amplified, modulated, and is printed according to connected programs.
This chapter contains sections titled: Introduction Identification of the Best Linear Approximation Using Random Excitations Generation of Uncertainty Bounds? Identification of the Best Linear Approximation Using Periodic Excitations Advises and Conclusions ]]>
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PSF estimation using sharp edge prediction
Removing Non-Uniform Motion Blur from Images
Generating Natural Language Descriptions for Semantic Representations of Human Brain Activity
eng_Latn
30,563
Deep Reinforcement Learning Using Neurophysiological Signatures of Interest
Brain state decoding for rapid image retrieval
Multi-output power supply with series voltage compensation capability for Magnetic Resonance Imaging system
eng_Latn
30,564
Brain-computer interface in paralysis.
Brain–machine interfaces: past, present and future
Functional MRI for neurofeedback: feasibility study on a hand motor task
eng_Latn
30,565
Feature extraction and classification of EEG signals for mapping motor area of the brain
Brain Computer Interface Design and Applications: Challenges and Future
Transgenic Plants as Biofactories for the Production of Biopharmaceuticals: A Case Study of Human Placental Lactogen
eng_Latn
30,566
PixelGAN Autoencoders
Operator Variational Inference
Resting state EEG correlates of memory consolidation
glg_Latn
30,567
Noninvasive Brain-Computer Interface: Decoding Arm Movement Kinematics and Motor Control
BCI2000: a general-purpose brain-computer interface (BCI) system
An EMG-controlled exoskeleton for hand rehabilitation
eng_Latn
30,568
DREAMER: A Database for Emotion Recognition Through EEG and ECG Signals From Wireless Low-cost Off-the-Shelf Devices
EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis
The ectonucleotidases CD39 and CD73: Novel checkpoint inhibitor targets
yue_Hant
30,569
Control of a 9-DoF Wheelchair-mounted robotic arm system using a P300 Brain Computer Interface: Initial experiments
Evoked-potential correlates of stimulus uncertainty.
On effective classification of strings with wavelets
eng_Latn
30,570
Automated Diagnosis of Epilepsy Using Key-Point-Based Local Binary Pattern of EEG Signals
EEG signal classification using PCA, ICA, LDA and support vector machines
Formal Concept Analysis
eng_Latn
30,571
Training deep neural-networks based on unreliable labels
Learning to Label Aerial Images from Noisy Data
Interindividual variability of the modulatory effects of repetitive transcranial magnetic stimulation on cortical excitability
eng_Latn
30,572
Smartwatch-Based Wearable EEG System for Driver Drowsiness Detection
A Real-Time Wireless Brain–Computer Interface System for Drowsiness Detection
Decoding the motion aftereffect in human visual cortex.
kor_Hang
30,573
Combinatorial Testing for Deep Learning Systems
Towards Proving the Adversarial Robustness of Deep Neural Networks
EEG based emotion recognition using SVM and PSO
eng_Latn
30,574
deep learning in the eeg diagnosis of alzheimer ' s disease .
Entropy analysis of the EEG background activity in Alzheimer's disease patients
Windows on the world: 2D windows for 3D augmented reality
eng_Latn
30,575
A fully automated correction method of EOG artifacts in EEG recordings
Spatial filtering of multichannel electroencephalographic recordings through principal component analysis by singular value decomposition
Guided-wave and leakage characteristics of substrate integrated waveguide
eng_Latn
30,576
Real-Time Mental Arithmetic Task Recognition From EEG Signals
LIBSVM: A library for support vector machines
A novel magic LSB substitution method (M-LSB-SM) using multi-level encryption and achromatic component of an image
kor_Hang
30,577
EEG-Based Brain–Computer Interfaces for Communication and Rehabilitation of People with Motor Impairment: A Novel Approach of the 21st Century
Demonstration of a Semi-Autonomous Hybrid Brain–Machine Interface Using Human Intracranial EEG, Eye Tracking, and Computer Vision to Control a Robotic Upper Limb Prosthetic
ContentFlow: Mapping Content to Flows in Software Defined Networks
eng_Latn
30,578
Cu Pillar and μ-bump electromigration reliability and comparison with high pb, SnPb, and SnAg bumps
Analysis of electromigration for Cu pillar bump in flip chip package
FUZZY ARTMAP CLASSIFICATION FOR MOTOR IMAGINARY BASED BRAIN COMPUTER INTERFACE
eng_Latn
30,579
The Berlin brain-computer interface: EEG-based communication without subject training
Brain-computer communication: unlocking the locked in.
Event-related EEG/MEG synchronization and desynchronization: basic principles
eng_Latn
30,580
Assistive technology and robotic control using motor cortex ensemble-based neural interface systems in humans with tetraplegia
A high-performance brain–computer interface
Modeling human faces with multi-image photogrammetry
eng_Latn
30,581
Hacking the brain: brain–computer interfacing technology and the ethics of neurosecurity
DARPA-funded efforts in the development of novel brain–computer interface technologies
Dry and Noncontact EEG Sensors for Mobile Brain–Computer Interfaces
eng_Latn
30,582
The signature of robot action success in EEG signals of a human observer: Decoding and visualization using deep convolutional neural networks
Deep learning with convolutional neural networks for decoding and visualization of EEG pathology
it ' s written on your face : detecting affective states from facial expressions while learning computer programming .
eng_Latn
30,583
Feature selection of EEG-signal data for cognitive load
An Introduction to Variable and Feature Selection
Orchestrating Caching, Transcoding and Request Routing for Adaptive Video Streaming Over ICN
eng_Latn
30,584
Robust extraction of P300 using constrained ICA for BCI applications
an efficient p300 - based brain – computer interface for disabled subjects .
Motion graphs
eng_Latn
30,585
Classification of multi-class motor imagery with a novel hierarchical SVM algorithm for brain–computer interfaces
an eeg - based brain - computer interface for cursor control .
Towards domain-independent information extraction from web tables
eng_Latn
30,586
Optimizing EEG energy-based seizure detection using genetic algorithms
Application of Machine Learning To Epileptic Seizure Detection
4 . use of genetically modified stem cells in experimental gene therapies .
eng_Latn
30,587
Learning to Decode Cognitive States from Brain Images
Analysis of fMRI Data by Blind Separation Into Independent Spatial Components
Impact of anti-vaccine movements on pertussis control: the untold story
eng_Latn
30,588
Improving fairness, efficiency, and stability in HTTP-based adaptive video streaming with FESTIVE
YouTube everywhere: impact of device and infrastructure synergies on user experience
Evaluation of a Dry EEG System for Application of Passive Brain-Computer Interfaces in Autonomous Driving
eng_Latn
30,589
Deep Gaussian Mixture-Hidden Markov Model for Classification of EEG Signals
a gentle tutorial of the em algorithm and its application to parameter estimation for gaussian mixture and hidden markov models .
Ecological speciation along an elevational gradient in a tropical passerine bird?
kor_Hang
30,590
A new EEG recording system for passive dry electrodes
FUNDAMENTALS OF EEG MEASUREMENT
The ten-twenty electrode system of the International Federation. The International Federation of Clinical Neurophysiology.
eng_Latn
30,591
Exploring miniaturized EEG electrodes for brain-computer interfaces. An EEG you do not see?
A Study of Evoked Potentials From Ear-EEG
brain - computer interface technology : a review of the first international meeting .
eng_Latn
30,592
Classification and discrimination of focal and non-focal EEG signals based on deep neural network
Discrimination of focal and non-focal EEG signals using entropy-based features in EEMD and CEEMDAN domains
Discrimination between ictal and seizure-free EEG signals using empirical mode decomposition
eng_Latn
30,593
Computer-Aided Diagnosis of Parkinson’s Disease Using Enhanced Probabilistic Neural Network
Enhanced probabilistic neural network with local decision circles: A robust classifier
Dependence of Grain Size on the Performance of a Polysilicon Channel TFT for 3D NAND Flash Memory.
eng_Latn
30,594
BCI competition 2003-data set IIb: enhancing P300 wave detection using ICA-based subspace projections for BCI applications
Natural Gradient Works Efficiently in Learning
On the Trivariate Non-Central Chi-Squared Distribution
eng_Latn
30,595
Blending of brain-machine interface and vision-guided autonomous robotics improves neuroprosthetic arm performance during grasping
control of a humanoid robot by a noninvasive brain - computer interface in humans .
Term Weighting Schemes for Question Categorization
eng_Latn
30,596
Classification of Four-Class Motor Imagery Employing Single-Channel Electroencephalography
Brain-Computer Interfaces in Medicine
The MITC3+ shell element and its performance
yue_Hant
30,597
Cognitive Analysis of Working Memory Load from Eeg, by a Deep Recurrent Neural Network
Speech recognition with deep recurrent neural networks
the magical number seven plus or minus two : some limits on our capacity for processing information .
eng_Latn
30,598
A Novel Incremental Covariance-Guided One-Class Support Vector Machine
One-Class Novelty Detection for Seizure Analysis from Intracranial EEG
New Support Vector Algorithms
eng_Latn
30,599