A novel speckle suppression method for medical ultrasound images is presented. First, the logarithmic transform of original image analyzed into multiscale wavelet domain. The authors show that subband decompositions have significantly non-Gaussian statistics are best described by families heavy-tailed distributions such as alpha-stable. Then, design a Bayesian estimator exploits these statistics. They use alpha-stable model to develop blind noise-removal processor performs nonlinear...

Synthetic aperture radar (SAR) images are inherently affected by multiplicative speckle noise, which is due to the coherent nature of scattering phenomenon. This paper proposes a novel Bayesian-based algorithm within framework wavelet analysis, reduces in SAR while preserving structural features and textural information scene. First, we show that subband decompositions logarithmically transformed accurately modeled alpha-stable distributions, family heavy-tailed densities. Consequently,...

We have identified the occurrence of chimera states for various coupling schemes in networks two-dimensional and three-dimensional Hindmarsh–Rose oscillators, which represent realistic models neuronal ensembles. This result, together with recent studies on multiple nonlocally coupled FitzHugh–Nagumo provide strong evidence that phenomenon chimeras may indeed be relevant neuroscience applications. Moreover, our work verifies existence bistable elements, whereas to date were known arise...

We consider the problem of experimental detection directionality weak coupling between two self-sustained oscillators from bivariate data. further develop method introduced by Rosenblum and Pikovsky [Phys. Rev. E 64, 045202 (2001)], suggesting an alternative approach. Next, we another framework for identification directionality, based on idea mutual predictability. Our algorithms provide index that shows whether is unidirectional or bidirectional, quantifies asymmetry bidirectional coupling....

Despite the apparent importance of mental fatigue detection, a reliable application is hindered due to incomprehensive understanding neural mechanisms fatigue. In this paper, we investigated topological alterations functional brain networks in theta band (4 - 7 Hz) electroencephalography (EEG) data from 40 male subjects undergoing two distinct fatigue-inducing tasks: low-intensity one-hour simulated driving and high-demanding half-hour sustained attention task [psychomotor vigilance (PVT)]....

Efficient classification of mental workload, an important issue in neuroscience, is limited, so far to single task, while cross-task remains a challenge. Furthermore, network approaches have emerged as promising direction for studying the complex organization brain, enabling easier interpretation various states. In this paper, using two tasks (N-back and arithmetic), we present framework crossas well within-task workload discrimination by utilizing multiband electroencephalography (EEG)...

Although anomalies in the topological architecture of whole-brain connectivity have been found to be associated with Alzheimer’s disease (AD), our understanding about progression AD a functional (FC) perspective is still rudimentary and few study has explored function-structure relations brain networks patients. By using resting-state MRI (fMRI), this firstly investigated organizational alternations FC 12 patients, 15 amnestic mild cognitive impairment (aMCI) 14 age-matched healthy aging...

Despite the apparent usefulness of efficient mental workload assessment in various real-world situations, underlying neural mechanism remains largely unknown, and studies are limited to well-controlled cognitive tasks using a 2D computer screen. In this paper, we investigated functional brain network alterations simulated flight experiment with three levels compared reorganization pattern between screen (2D) virtual reality (3D) interfaces. We constructed multiband networks...

Despite convergent evidence indicating a variety of regional abnormalities hemispheric asymmetry in schizophrenia, patterns wider neural network remain to be determined. In this study, we investigated alterations white matter topology schizophrenia and their association with clinical manifestations the illness. Weighted brain anatomical networks were constructed for each 116 right-handed patients 66 matched healthy participants. Graph theoretical approaches then employed estimate topological...

Sexual dimorphism in the brain maturation during childhood and adolescence has been repeatedly documented, which may underlie differences behaviors cognitive performance. However, our understanding of how gender modulates development structural connectome healthy adults is still not entirely clear. Here we utilized graph theoretical analysis longitudinal diffusion tensor imaging data over a five-year period to investigate progressive network topology. The networks both genders showed...

Maintaining sustained attention during a prolonged cognitive task often comes at cost: high levels of mental fatigue. Heuristically, fatigue refers to feeling tiredness or exhaustion, and disengagement from the hand; it manifests as impaired behavioral performance. In order effectively reduce undesirable yet preventable consequences in many real-world workspaces, better understanding underlying neural mechanisms is needed, continuous efforts have been devoted this topic. comparison with...

Numerous studies have revealed various working memory (WM)-related brain activities that originate from cortical regions and oscillate at different frequencies. However, multi-frequency band analysis of the network in WM space remains largely unexplored. In this study, we employed a graph theoretical framework to characterize topological properties functional theta alpha frequency bands during tasks. Twenty-eight subjects performed visual n-back tasks two difficulty levels, i.e., 0-back...

Accumulating evidences showed that the optimal brain network topology was altered with progression of fatigue during car driving. However, extent discriminative power functional connectivity contributes to driving detection is still unclear. In this article, we extracted two types features (network properties and critical connections) explore their usefulness in detection. EEG data were recorded twice from twenty healthy subjects a simulated experiment. Multiband matrices established using...

The rising number of traffic accidents has become a major issue in our daily life, which attracted the concern society and governments. To deal with this issue, previous study, we have designed real-time driving fatigue detection system using power spectrum density sample entropy. By wireless technology dry electrodes for EEG collection, further integrated virtual reality simulated environment, made study more applicable to realistic settings. However, high accuracy classification not been...

Frequency-domain (FD) features reveal the activated patterns of individual local brain regions responding to different emotions, whereas connectivity (BC) involve coordination multiple for generating emotional responses; these two types are complementary each other. To date, fusion electroencephalography (EEG)-based cross-subject emotion recognition remains be fully investigated due intersubject variability in EEG signals. In this article, we first attempt investigate fused from...

Deep neural networks have recently been successfully extended to EEG-based driving fatigue detection. Nevertheless, most existing models fail reveal the intrinsic inter-channel relations that are known be beneficial for classification. Additionally, these require substantial data training, which is often impractical due high cost of collection. To simultaneously address two issues, we propose a Self-Attentive Channel-Connectivity Capsule Network (SACC-CapsNet) detection in this paper....

Electroencephalography (EEG) signals have been proven to be one of the most predictive and reliable indicators for estimating driving fatigue state. However, how make full use EEG data detection remains a challenge. Many existing methods include time-consuming manual process or tedious parameter tunings feature extraction, which is inconvenient train implement. On other hand, models ignore manually determine connectivity features between different channels, thus failing thoroughly exploit...

Working memory (WM) is a distributed cognitive process that employs communication between prefrontal cortex and posterior brain regions in the form of cross-frequency coupling theta (θ) high-alpha (α2) waves. A novel method for deriving causal interactions waves different frequencies essential better understanding neural dynamics such complex process. Here, we proposed to estimate transfer entropy (TE) through symbolization scheme, which based on neural-gas algorithm (NG) encodes bivariate...

The effectiveness of workload identification is one the critical aspects in a monitoring instrument mental state. In this field, usually recognized as binary classes. There are scarce studies toward multiclass because challenge success much tough, even though more class added. Besides, most existing only utilized spectral power features from individual channels but ignoring abundant interchannel that represent interactions between brain regions. study, we representing intrachannel...