Pair Of Channels Research Articles

A Class-Imbalance Aware and Explainable Spatio-Temporal Graph Attention Network for Neonatal Seizure Detection.

Seizures are the most prevalent clinical indication of neurological disorders in neonates. In this study, a class-imbalance aware and explainable deep learning approach based on Convolutional Neural Networks (CNNs) and Graph Attention Networks (GATs) is proposed for the accurate automated detection of neonatal seizures. The proposed model integrates the temporal information of EEG signals with the spatial information on the EEG channels through the graph representation of the multi-channel EEG segments. One-dimensional CNNs are used to automatically develop a feature set that accurately represents the differences between seizure and nonseizure epochs in the time domain. By employing GAT, the attention mechanism is utilized to emphasize the critical channel pairs and information flow among brain regions. GAT coefficients were then used to empirically visualize the important regions during the seizure and nonseizure epochs, which can provide valuable insight into the location of seizures in the neonatal brain. Additionally, to tackle the severe class imbalance in the neonatal seizure dataset using under-sampling and focal loss techniques are used. Overall, the final Spatio-Temporal Graph Attention Network (ST-GAT) outperformed previous benchmarked methods with a mean AUC of 96.6% and Kappa of 0.88, demonstrating its high accuracy and potential for clinical applications.

Investigating the Effect of Data Length on the Performance of Frequency-Domain fNIRS Functional Connectivity Measures.

Resting-state functional connectivity is a promising tool for understanding and characterizing brain network architecture. However, obtaining uninterrupted long recording of resting-state data is challenging in many clinically relevant populations. Moreover, the interpretation of connectivity results may heavily depend on the data length and functional connectivity measure used. We compared the performance of three frequency-domain connectivity measures: magnitude-squared, wavelet and multitaper coherence; and the effect of data length ranging from 3 to 9 minutes. Performance was characterized by distinguishing two groups of channel pairs with known different connectivity strengths. While all methods considered improved the ability to distinguish the two groups with increasing data lengths, wavelet coherence performed best for the shortest time window of 3 minutes. Knowledge of which measure is more reliably used when shorter fNIRS recordings are available could make the utility of functional connectivity biomarkers more feasible in clinical populations of interest.

Impact of averaging fNIRS regional coherence data when monitoring people with long term post-concussion symptoms.

Functional near-infrared spectroscopy (fNIRS), with its measure of delta hemoglobin concentration, has shown promise as a monitoring tool for the functional assessment of neurological disorders and brain injury. Analysis of fNIRS data often involves averaging data from several channel pairs in a region. Although this greatly reduces the processing time, it is uncertain how it affects the ability to detect changes post injury. We aimed to determine how averaging data within regions impacts the ability to differentiate between post-concussion and healthy controls. We compared interhemispheric coherence data from 16 channel pairs across the left and right dorsolateral prefrontal cortex during a task and a rest period. We compared the statistical power for differentiating groups that was obtained when undertaking no averaging, vs. averaging data from 2, 4, or 8 source detector pairs. Coherence was significantly reduced in the concussion group compared with controls when no averaging was undertaken. Averaging all 8 channel pairs before undertaking the coherence analysis resulted in no group differences. Averaging between fiber pairs may eliminate the ability to detect group differences. It is proposed that even adjacent fiber pairs may have unique information, so averaging must be done with caution when monitoring brain disorders or injury.

A Dual-Band Magnetoelectric Monopole Antenna Array Using Mode-Composite Waveguides

A novel substrate-integrated mode-composite waveguide owing two bands where only the quasi-TE10 mode is supported is presented. The waveguide geometry completely shared in the two bands is equivalent to one transmission channel at low frequencies and a pair of individual channels at high frequencies. With the aid of the waveguide, a dual-band magneto-electric (ME) monopole array with a relatively large frequency ratio is designed, fabricated and tested. Benefitting from the shared radiation structure that separately operates as 1 × 2 and 1 × 4 arrays in the low and high frequency bands, satisfying radiation features are obtained simultaneously in the two bands from 10.3 to 11.7 GHz and from 31.8 to 38.1 GHz. The proposed mode-composite waveguide and antenna array are valuable to emerging dual-band wireless applications operating at both microwave and millimeter-wave frequencies.

Performance Analysis of an Efficient Router using X Y Algorithm

As more and more functions are expected to be performed by a single electronic device (such as a smartphone, smart television, etc.), the need to have more and more components on SoC is increasing, posing new difficulties for NoC. The majority of NoC designs utilise mesh, torus, or other topologies to ensure a robust router. Most solutions, however, fall short when it comes to addressing key issues like throughput, area overhead, and latency, as well as QoS and congestion. The current paper proposes a concept for a reconfigurable router that can be used in No C settings. For the suggested router's design, we use Verilog, formal language for describing hardware (Verilog HDL). The four-channel router presented here has an east-west-north-south orientation and a crossbar switch connecting the two pairs of channels. Each channel consists of a multiplexer and a FIFO buffer. The input and output are handled by multiplexers, and the data is stored in FIFO buffers. The FIFO and multiplexer architectures for the south channel are developed initially. Afterwards the remaining three channels and the crossbar switch are made. Routers use channels, FIFO buffers, multiplexers, and crossbar switches in their overall design. Simulating the proposed design in Modelsim and obtaining the RTL view in Xilinx ISE 14.0 are the two primary methods of approaching this problem. The suggested reconfigurable router's power consumption is greatly reduced by employing the Power gating technique.. The XPower Analyzer application is used to determine the total power. As demonstrated by the findings obtained, the proposed design uses less energy than conventional reconfigurable routers.

Design and Fabrication of a High Performance Microfluidic Chip for Blood Plasma Separation: Modelling and Prediction of System Behaviour via CFD Method.

This paper presents a single-step microfluidic system designed for passive separation of human fresh blood plasma using direct capillary forces. Our microfluidic system is composed of a cylindrical well between upper and lower channel pairs produced by soft photolithography. The microchip was fabricated based on hydrophobicity differences upon suitable cylindrical surfaces using gravitational and capillary forces and lateral migration of plasma and red blood cells. The plasma radiation was applied to attach the polymeric segment (polydimethylsiloxane (PDMS)) to the glass. Meanwhile, Tween 80 was used as a surfactant to increase the hydrophobicity of the lateral channel surfaces. This led to the higher movement of whole blood, including plasma. Fick's law of diffusion was validated for this diffusion transfer, the Navier-Stokes equation was used for the momentum balance, and the Laplace equation was utilized for the dynamics of the mesh. A model with high accuracy using the COMSOL Multiphysics software was created to predict the capillary forces and chip model validation. RBCs (red blood cells) were measured by the H3 cell counter instrument, by which 99% plasma purity was achieved. Practically, 58.3% of the plasma was separated from the blood within 12 min. Correlation between plasma separation results obtained from software and experimental data showed a coefficient of determination equal to 0.9732. This simple, rapid, stable, and reliable microchip can be considered as a promising candidate for providing plasma in point-of-care diagnostics.

An on-chip generator for multi-pattern periodic dynamic flow based on multiple synchronous sources

Periodic dynamic flow, including pulsatile flow and oscillatory flow, has many applications at the microscopic scale in chemistry, biology, and pharmacy. Conventional devices for periodic flow are either bulky, complicated, or inflexible. Here, we propose a novel on-chip flow generator with multi-pattern outputs possessing the obvious advantages of small size, simple structure, and being easy to be implemented. The fluid flow is synergistically driven by the constant pressure and the alternating pressure in a pair of conical channels. We design and fabricate a simple on-chip prototype following this conical-channel-based constant–alternating (C3A) driving method, and validate its performance through both numerical simulation and flow experiment. The results demonstrate the synergistic effects of the constant and alternating pressures on the fluid flow, and can implement four flow modes, i.e., steady flow, oscillatory flow, pulsatile flow, and complex flow, simply by modulating the input voltage. The proposed flow generator with multi-pattern outputs, simple structure and small footprint can be flexibly integrated with other on-chip devices for various biomedical applications, and the proposed strategy utilizing multiple synchronous sources can also inspire more delicate manipulation in the context of microfluidics.

An agent-based model of supply chain financing: agents’ strategies, performance, and risk contagions

ABSTRACT Supply chain finance (SCF), which has gained prominent attention from academicians, is a complex process involving various participants. However, previous studies have not provided a comprehensive framework integrating these participants or a consensus on how participants’ strategies influence SCF. Here, a comprehensive framework is proposed to investigate SCF through an agent-based approach. We chose the Chinese market to proxy key model parameters. We find that although banks prefer large businesses, trade credit, and partial credit guarantee still play a pair of main complementary financing channels for small and medium enterprises (SMEs). The cautious strategies of agents indeed significantly decrease the risk contagions in the supply chain. However, marketisation negatively moderates this effect for SMEs. From the banks’ perspective, although longer-term credit policies bring lower risk contagions, they increase the uncertainty of the banks’ risk. In addition, our extended framework also shows that the market-oriented financial system benefits either large business or SMEs as well as smooths the risk fluctuation for banks. This study not only verifies some economic phenomena but also provides a new systematic angle for understanding SCF. We also discuss in detail this study’ important practical insights and theoretical contribution.

Constraints on Gaussian Error Channels and Measurements for Quantum Communication.

Joint Gaussian measurements of two quantum systems are important for quantum communication between remote parties and are often used in continuous-variable teleportation or entanglement-swapping protocols. Many of the errors in real-world implementations can be modeled by independent Gaussian error channels acting prior to measurement. In this work we study independent single-mode Gaussian error channels on two modes A and B that take place prior to a joint Gaussian measurement. We determine the set of pairs of such channels that render all Gaussian measurements separable, and therefore unsuitable for entanglement swapping or teleportation of arbitrary input states. For example, if the error channels are loss with parameters followed by added noise with parameters then all Gaussian measurements are separable if and only if .

The machine learning pipeline to unravel the functional effects of interactions between sodium channels.

Recent reports obtained using patch clamp approach in Na+ channel (NaV) pairs have suggested cooperative gating of two NaVs, which has been linked to their homodimerization. Yet, the existing methods of the cooperative gating analysis are not effective in unraveling alterations in kinetics of individual NaVs result from their interactions in dimers. To address this issue, we developed a machine learning pipeline to remove capacitance drift and noise within the signal, and detect channel activity in order to infer Markov models parameters from single and multi-channel recordings.

Connections between spatially distant primary language regions strengthen with age during infancy, as revealed by resting-state fNIRS

Objective. Hearing is an important sensory function that plays a key role in how children learn to speak and develop language skills. Although previous neuroimaging studies have established that much of brain network maturation happens in early childhood, our understanding of the developmental trajectory of language areas is still very limited. We hypothesized that typical development trajectory of language areas in early childhood could be established by analyzing the changes of functional connectivity in normal hearing infants at different ages using functional near-infrared spectroscopy. Approach. Resting-state data were recorded from two bilateral temporal and prefrontal regions associated with language processing by measuring the relative changes of oxy-hemoglobin (HbO) and deoxy-hemoglobin (HbR) concentrations. Connectivity was calculated using magnitude-squared coherence of channel pairs located in (a) inter-hemispheric homologous and (b) intra-hemispheric brain regions to assess connectivity between homologous regions across hemispheres and two regions of interest in the same hemisphere, respectively. Main results. A linear regression model fitted to the age vs coherence of inter-hemispheric homologous test group revealed a significant coefficient of determination for both HbO (R 2 = 0.216, p = 0.0169) and HbR (R 2 = 0.206, p = 0.0198). A significant coefficient of determination was also found for intra-hemispheric test group for HbO (R 2 = 0.237, p = 0.0117) but not for HbR (R 2 = 0.111, p = 0.0956). Significance. The findings from HbO data suggest that both inter-hemispheric homologous and intra-hemispheric connectivity between primary language regions significantly strengthen with age in the first year of life. Mapping out the developmental trajectory of primary language areas of normal hearing infants as measured by functional connectivity could potentially allow us to better understand the altered connectivity and its effects on language delays in infants with hearing impairments.

PSIs of EEG with Refined Frequency Decomposition Could Prognose Motor Recovery Before Rehabilitation Intervention.

Stroke often leads to permanent impairment in motor function. Accurate and quantitative prognosis of potential motor recovery before rehabilitation intervention can help healthcare centers improve resources organization and enable individualized intervention. The context of this paper investigated the potential of using electroencephalography (EEG) functional connectivity (FC) measures as biomarkers for assessing and prognosing improvement of Fugl-Meyer Assessment in upper extremity motor function (ΔFMU) among participants with chronic stroke. EEG data from resting and motor imagery task were recorded from 13 participants with chronic stroke. Three functional connectivity methods, which were Pearson correlation measure (PCM), weighted Phase Lag Index (wPLI) and phase synchronization index (PSI), were investigated, under three regions of interest (inter-hemispheric, intra-hemispheric, and whole-brain), in two statues (resting and motor imagery), with 15 refined center frequencies. We applied correlation analysis to identify the optimal center frequencies and pairs of synchronized channels that were consistently associated with ΔFMU. Predictive models were generated using regression analysis algorithms based on optimized center frequencies and channel pairs identified from the proposed analysis method, with leave-one-out cross-validation. We found that PSI in the Alpha band (with center frequency of 9Hz) was the most sensitive FC measures for prognosing motor recovery. Strong and significant correlations were identified between the predictions and actual ΔFMU scores both in the resting state (R2=0.79, P<0.001, N=13) and motor imagery (R2=0.65, P<0.001, N=13). Our results suggested that EEG connectivity measured with PSI in resting state could be a promising biomarker for quantifying motor recovery before motor rehabilitation intervention.

Dense Modality Interaction Network for Audio-Visual Event Localization

Human perception systems can integrate audio and visual information automatically to obtain a profound understanding of real-world events. Accordingly, fusing audio and visual contents is important to solve the audio-visual event (AVE) localization problem. Although most existing works have fused audio and visual modalities to explore their relationship with attention-based networks, we can delve into their relationship more deeply to improve the fusion capability of the two modalities. In this paper, we propose a dense modality interaction network (DMIN) to elegantly leverage audio and visual information by integrating two novel modules, namely, the audio-guided triplet attention (AGTA) module and the dense inter-modality attention (DIMA) module. The AGTA module enables audio information to guide the network to pay more attention to event-relevant visual regions. This guidance is conducted in the channel, temporal, and spatial dimensions, which emphasize informative features, temporal relationships and spatial regions, to boost the capacity of representations. Furthermore, the DIMA module establishes the dense-relationship between audio and visual modalities. Specifically, the DIMA module leverages the information of all channel pairs of audio and visual features to formulate the cross-modality attention weight, which is superior to the multi-head attention module that uses limited information. Moreover, a novel unimodal discrimination loss (UDL) is introduced to exploit the unimodal and fused features together for more exact AVE localization. The experimental results show that our method is remarkably superior to the state-of-the-art methods in fully- and weakly-supervised AVE settings. To further evaluate the model's ability to build audio-visual connections, we design a dense cross modality relation network (DCMR) to solve the cross-modality localization task. DCMR is a simple deformation of a DMIN, and the experimental results further illustrate that DIMA can explore denser relationships between the two modalities. Code is available at https://github.com/weizequan/DMIN.git.

A Feature Extraction Method for Seizure Detection Based on Multi-Site Synchronous Changes and Edge Detection Algorithm.

Automatic detection of epileptic seizures is important in epilepsy control and treatment, and specific feature extraction assists in accurate detection. We developed a feature extraction method for seizure detection based on multi-site synchronous changes and an edge detection algorithm. We investigated five chronic temporal lobe epilepsy rats with 8- and 12-channel detection sites in the hippocampus and limbic system. Multi-site synchronous changes were selected as a specific feature and implemented as a seizure detection method. For preprocessing, we used magnitude-squared coherence maps and Canny edge detection algorithm to find the frequency band with the most significant change in synchronization and the important channel pairs. In detection, we used the maximal cross-correlation coefficient as an indicator of synchronization and the correlation coefficient curves' average value and standard deviation as two detection features. The method achieved high performance, with an average 96.60% detection rate, 2.63/h false alarm rate, and 1.25 s detection delay. The experimental results show that synchronization is an appropriate feature for seizure detection. The magnitude-squared coherence map can assist in selecting a specific frequency band and channel pairs to enhance the detection result. We found that individuals have a specific frequency band that reflects the most significant synchronization changes, and our method can individually adjust parameters and has good detection performance.

Attention deficit hyperactivity disorder recognition based on intrinsic time-scale decomposition of EEG signals

Attention deficit hyperactivity disorder (ADHD), a neuro-developmental condition, is characterized by various degrees of impulsivity, hyperactivity, and inattention. Treatment of this condition and minimizing its negative impact on learning, working, forming relationships, and quality of life depends heavily on the early identification. The Electroencephalography (EEG) is a useful neuroimaging technique for understanding ADHD. This study examines the brain activity of children with ADHD by analyzing the EEG signals using the intrinsic time-scale decomposition (ITD). Different combinations of the modes, known as Proper Rotation Components (PRCs), produced by ITD, are used to extract a variety of connectivity-based features (magnitude square coherence, cross power spectral density, correlation coefficient, covariance, cohentropy coefficient, correntropy coefficient). EEG signals of 15 ADHD children and 18 age-matched health children are recorded while resting with the eyes closed. Mentioned features are calculated using different channel pairs chosen from longitudinal and transversal planes. Through various machine learning approaches and a 10-fold cross-validation method, the proposed approach is evaluated to distinguish between ADHD patients and healthy controls. Classification accuracies are obtained for the longitudinal and transverse planes, between 92.90% to 99.90% and 91.70% to 100.00%, respectively. Our results support the remarkable performance of the proposed approach, and represent a substantial advance over similar studies in terms of recognizing and classifying ADHD.

Effects of Viewing Geometry on Multispectral Lidar-Based Needle-Leaved Tree Species Identification

Identifying tree species with remote sensing techniques, such as lidar, can improve forest management decision-making, but differences in scan angle may influence classification accuracy. The multispectral Titan lidar (Teledyne Optech Inc., Vaughan, ON, Canada) has three integrated lasers with different wavelengths (1550, 1064 and 532 nm), and with different scan angle planes (respectively tilted at 3.5°, 0° and 7° relative to a vertical plane). The use of multispectral lidar improved tree species separation, compared to mono-spectral lidar, by providing classification features that were computed from intensities in each channel, or from pairs of channels as ratios and normalized indices (NDVIs). The objective of the present study was to evaluate whether scan angle (up to 20°) influences 3D and intensity feature values and if this influence affected species classification accuracy. In Ontario (Canada), six needle-leaf species were sampled to train classifiers with different feature selection. We found the correlation between feature values and scan angle to be poor (mainly below |±0.2|), which led to changes in tree species classification accuracy of 1% (all features) and 8% (3D features only). Intensity normalization for range improved accuracies by 8% for classifications using only single-channel intensities, and 2–4% when features that were unaffected by normalization were added, such as 3D features or NDVIs.

Tackling the challenges of group network inference from intracranial EEG data.

Intracranial EEG (iEEG) data is a powerful way to map brain function, characterized by high temporal and spatial resolution, allowing the study of interactions among neuronal populations that orchestrate cognitive processing. However, the statistical inference and analysis of brain networks using iEEG data faces many challenges related to its sparse brain coverage, and its inhomogeneity across patients. We review these challenges and develop a methodological pipeline for estimation of network structure not obtainable from any single patient, illustrated on the inference of the interaction among visual streams using a dataset of 27 human iEEG recordings from a visual experiment employing visual scene stimuli. 100 ms sliding window and multiple band-pass filtered signals are used to provide temporal and spectral resolution. For the connectivity analysis we showcase two connectivity measures reflecting different types of interaction between regions of interest (ROI): Phase Locking Value as a symmetric measure of synchrony, and Directed Transfer Function-asymmetric measure describing causal interaction. For each two channels, initial uncorrected significance testing at p < 0.05 for every time-frequency point is carried out by comparison of the data-derived connectivity to a baseline surrogate-based null distribution, providing a binary time-frequency connectivity map. For each ROI pair, a connectivity density map is obtained by averaging across all pairs of channels spanning them, effectively agglomerating data across relevant channels and subjects. Finally, the difference of the mean map value after and before the stimulation is compared to the same statistic in surrogate data to assess link significance. The analysis confirmed the function of the parieto-medial temporal pathway, mediating visuospatial information between dorsal and ventral visual streams during visual scene analysis. Moreover, we observed the anterior hippocampal connectivity with more posterior areas in the medial temporal lobe, and found the reciprocal information flow between early processing areas and medial place area. To summarize, we developed an approach for estimating network connectivity, dealing with the challenge of sparse individual coverage of intracranial EEG electrodes. Its application provided new insights into the interaction between the dorsal and ventral visual streams, one of the iconic dualities in human cognition.

Directional Sensitivity of DAS and Its Effect on Rayleigh-Wave Tomography: A Case Study in Oxnard, California

AbstractDistributed acoustic sensing (DAS) provides dense arrays ideal for seismic tomography. However, DAS only records average axial strain change along the cable, which can complicate the interpretation of surface-wave observations. With a rectangular DAS array located in the City of Oxnard, California, we compare phase velocity dispersion at the same location illuminated by differently oriented virtual sources. The dispersion curves are consistent for colinear and noncolinear virtual sources, suggesting that surface-wave observations in most of the cross-correlations are dominated by Rayleigh waves. Our measurements confirm that colinear channel pairs provide higher Rayleigh-wave signal-to-noise ratio (SNR). For cross-correlations of noncolinear channel pairs, the travel time of each connecting ray path can still be obtained despite the lower SNR of Rayleigh wave signals. The inverted Rayleigh-wave dispersion map reveals an ancient river channel consistent with the local geologic map. Our results demonstrate the potential of DAS-based 2D surface-wave tomography without special treatment of directional sensitivity in areas where one type of wave is dominating or can be identified.

A scheme to measure velocity vector of a meteor by meteor radar

A scheme is proposed to measure velocity vector of a meteoroid with phase information of echoes from meteor radar. It is revealed that the phase variation of interfere signal of the multi-channel signals has a certain relationship with the lateral velocity component by mathematical analysis. Simulation results demonstrate that the velocity vector of a meteor can be inversed with the radial velocity and two lateral velocities using the proposed scheme. Results of two field experimental examples show both the vertical deceleration and acceleration, together with horizontal direction changing abruptly. • Two lateral velocities of meteoroid can be inversed by calculating phase difference of echoes from two pairs of channels. • A simulation was conducted to obtain velocity vector of a meteoroid by the common meteor radar of SKiYMET radar. • The error of inversed velocity vector is only about 2% in simulation results, suggesting the proposed scheme is feasible. • Using proposed scheme, velocity vector of meteoroid was deduced, which could help us better understand meteoroid's movement.

Identifying depression disorder using multi-view high-order brain function network derived from electroencephalography signal.

Brain function networks (BFN) are widely used in the diagnosis of electroencephalography (EEG)-based major depressive disorder (MDD). Typically, a BFN is constructed by calculating the functional connectivity (FC) between each pair of channels. However, it ignores high-order relationships (e.g., relationships among multiple channels), making it a low-order network. To address this issue, a novel classification framework, based on matrix variate normal distribution (MVND), is proposed in this study. The framework can simultaneously generate high-and low-order BFN and has a distinct mathematical interpretation. Specifically, the entire time series is first divided into multiple epochs. For each epoch, a BFN is constructed by calculating the phase lag index (PLI) between different EEG channels. The BFNs are then used as samples, maximizing the likelihood of MVND to simultaneously estimate its low-order BFN (Lo-BFN) and high-order BFN (Ho-BFN). In addition, to solve the problem of the excessively high dimensionality of Ho-BFN, Kronecker product decomposition is used for dimensionality reduction while retaining the original high-order information. The experimental results verified the effectiveness of Ho-BFN for MDD diagnosis in 24 patients and 24 normal controls. We further investigated the selected discriminative Lo-BFN and Ho-BFN features and revealed that those extracted from different networks can provide complementary information, which is beneficial for MDD diagnosis.