Decoding Of Signals Research Articles

Psychopathic and autistic traits differentially influence the neural mechanisms of social cognition from communication signals

Psychopathy is associated with severe deviations in social behavior and cognition. While previous research described such cognitive and neural alterations in the processing of rather specific social information from human expressions, some open questions remain concerning central and differential neurocognitive deficits underlying psychopathic behavior. Here we investigated three rather unexplored factors to explain these deficits, first, by assessing psychopathy subtypes in social cognition, second, by investigating the discrimination of social communication sounds (speech, non-speech) from other non-social sounds, and third, by determining the neural overlap in social cognition impairments with autistic traits, given potential common deficits in the processing of communicative voice signals. The study was exploratory with a focus on how psychopathic and autistic traits differentially influence the function of social cognitive and affective brain networks in response to social voice stimuli. We used a parametric data analysis approach from a sample of 113 participants (47 male, 66 female) with ages ranging between 18 and 40 years (mean 25.59, SD 4.79). Our data revealed four important findings. First, we found a phenotypical overlap between secondary but not primary psychopathy with autistic traits. Second, primary psychopathy showed various neural deficits in neural voice processing nodes (speech, non-speech voices) and in brain systems for social cognition (mirroring, mentalizing, empathy, emotional contagion). Primary psychopathy also showed deficits in the basal ganglia (BG) system that seems specific to the social decoding of communicative voice signals. Third, neural deviations in secondary psychopathy were restricted to social mirroring and mentalizing impairments, but with additional and so far undescribed deficits at the level of auditory sensory processing, potentially concerning deficits in ventral auditory stream mechanisms (auditory object identification). Fourth, high autistic traits also revealed neural deviations in sensory cortices, but rather in the dorsal auditory processing streams (communicative context encoding). Taken together, social cognition of voice signals shows considerable deviations in psychopathy, with differential and newly described deficits in the BG system in primary psychopathy and at the neural level of sensory processing in secondary psychopathy. These deficits seem especially triggered during the social cognition from vocal communication signals.

Introducing block-Toeplitz covariance matrices to remaster linear discriminant analysis for event-related potential brain–computer interfaces

Objective. Covariance matrices of noisy multichannel electroencephalogram (EEG) time series data provide essential information for the decoding of brain signals using machine learning methods. However, small datasets and high dimensionality make it hard to estimate these matrices. In brain–computer interfaces (BCI) based on event-related potentials (ERP) and a linear discriminant analysis (LDA) classifier, the state of the art covariance estimation uses shrinkage regularization. As this is a general covariance regularization approach, we aim at improving LDA further by better exploiting the domain-specific characteristics of the EEG to regularize the covariance estimates. Approach. We propose to enforce a block-Toeplitz structure for the covariance matrix of the LDA, which implements an assumption of signal stationarity in short time windows for each channel. Main results. An offline re-analysis of data collected from 213 subjects under 13 different event-related potential BCI protocols showed a significantly increased binary classification performance of this ‘ToeplitzLDA’ compared to shrinkage regularized LDA (up to 6 AUC points, p < 0.001) and Riemannian classification approaches (up to 2 AUC points, p < 0.001). In an unsupervised visual speller application, this improvement would translate to a relative reduction of spelling errors by 81% on average for 25 subjects. Additionally, aside from lower memory and reduced time complexity for LDA training, ToeplitzLDA proves to be robust against drastic increases of the number of temporal features. Significance. The proposed covariance estimation allows BCI researchers to improve classification rates and reduce calibration times of BCI protocols using event-related potentials and thus support the usability of corresponding applications. Its lower computational and memory needs could make it a valuable algorithm especially for mobile BCIs.

A review of zirconia oxygen, NOx, and mixed potential gas sensors – History and current trends

Zirconia-based electrochemical sensors have revolutionized oxygen monitoring and combustion control. These robust, solid-state devices are found in virtually every internal combustion engine today providing feedback information for tight air to fuel ratio control thus minimizing unwanted emissions and improving engine efficiency. Variations on the sensor design and operational mode enable the measurement of nitrogen oxides to part per million levels, providing control information for exhaust after-treatment systems. Emerging designs include the development of "gas analyzers on a chip" capable of monitoring oxygen, carbon monoxide, nitrogen oxides, ammonia and hydrocarbons. • Zirconia based electrochemical gas sensors can detect wide variety of pollutants. • Potentiometric, amperometric, impedance-based, and pulsed polarization methods are reviewed. • Modeling of sensor behavior can predict sensor response. • Machine learning techniques can be applied for sensor signal decoding.

BVP: Balanced Vehicular Pairing for Fair Resource Distribution in Downlink NOMA

In this paper, a vehicular pairing scheme is proposed to balance the resource distribution in non-orthogonal multiple access (NOMA) based vehicular networks. The proposed BVP: balanced vehicular pairing scheme chooses pairs based on fair inter-vehicle distances through the base station (BS) for mobile NOMA users with high mobility. BVP reduces the biased pairing and the disparity of resource utilization by each pair, resulting in fewer signal decoding errors than the existing state-of-the-art solutions. The exhaustive evaluation in Matlab-based network simulator, using real vehicular traffic traces from OpenStreetMap (OSM) and simulation of urban mobility (SUMO) traffic simulator shows that the proposed BVP outperforms existing schemes. The proposed solution reduces throughput and pair sum rate standard deviation by 2 ~ 3 b/s/Hz for every device and every resource, while also significantly reducing the decoding bit error rate.

Movement decoding using spatio-spectral features of cortical and subcortical local field potentials

The first commercially sensing enabled deep brain stimulation (DBS) devices for the treatment of movement disorders have recently become available. In the future, such devices could leverage machine learning based brain signal decoding strategies to individualize and adapt therapy in real-time. As multi-channel recordings become available, spatial information may provide an additional advantage for informing machine learning models. To investigate this concept, we compared decoding performances from single channels vs. spatial filtering techniques using intracerebral multitarget electrophysiology in Parkinson's disease patients undergoing DBS implantation. We investigated the feasibility of spatial filtering in invasive neurophysiology and the putative utility of combined cortical ECoG and subthalamic local field potential signals for decoding grip-force, a well-defined and continuous motor readout. We found that adding spatial information to the model can improve decoding (6% gain in decoding), but the spatial patterns and additional benefit was highly individual. Beyond decoding performance results, spatial filters and patterns can be used to obtain meaningful neurophysiological information about the brain networks involved in target behavior. Our results highlight the importance of individualized approaches for brain signal decoding, for which multielectrode recordings and spatial filtering can improve precision medicine approaches for clinical brain computer interfaces.

Neurostimulation artifact removal for implantable sensors improves signal clarity and decoding of motor volition.

As the demand for prosthetic limbs with reliable and multi-functional control increases, recent advances in myoelectric pattern recognition and implanted sensors have proven considerably advantageous. Additionally, sensory feedback from the prosthesis can be achieved via stimulation of the residual nerves, enabling closed-loop control over the prosthesis. However, this stimulation can cause interfering artifacts in the electromyographic (EMG) signals which deteriorate the reliability and function of the prosthesis. Here, we implement two real-time stimulation artifact removal algorithms, Template Subtraction (TS) and ε-Normalized Least Mean Squares (ε-NLMS), and investigate their performance in offline and real-time myoelectric pattern recognition in two transhumeral amputees implanted with nerve cuff and EMG electrodes. We show that both algorithms are capable of significantly improving signal-to-noise ratio (SNR) and offline pattern recognition accuracy of artifact-corrupted EMG signals. Furthermore, both algorithms improved real-time decoding of motor intention during active neurostimulation. Although these outcomes are dependent on the user-specific sensor locations and neurostimulation settings, they nonetheless represent progress toward bi-directional neuromusculoskeletal prostheses capable of multifunction control and simultaneous sensory feedback.

Adaptation of Signal with NOMA and Polar Codes to the Rayleigh Channel

Polar codes are one of the most effective methods of error correction coding. Currently, these codes are used in 5G communication systems and are the main candidate for 6G. Symmetry plays an important role in coding and decoding algorithms for polar codes. Modulation and multiple access methods are the basis for a physical layer along with error correction codes. Non-Orthogonal Multiple Access (NOMA) methods are a promising technology for future telecommunication systems. They allow the increase of both spectral efficiency and the quantity of subscribers of a communication system. In this paper, we consider a communication system with polar codes, traditional orthogonal multiple access (OMA), and NOMA. The channel with multipath propagation, which can be defined by the Rayleigh channel model, is especially difficult to transmit. We propose a method for adapting signals with polar codes to a channel state based on the analysis of channel matrix and permutation of logical subchannels. The results obtained demonstrate efficiency compared to classical solutions and do not really increase the computational complexity of signal processing and decoding.

CANClassify: Automated Decoding and Labeling of CAN Bus Signals

CANClassify: Automated Decoding and Labeling of CAN Bus Signals by Paul Ngo 1,* , Jonathan Sprinkle 2 , Rahul Bhadani 3 1 University of California, Berkeley, Berkeley, California, USA, 947042 Vanderbilt University, Nashville, Tennessee, USA, 372123 The University of Arizona, Tucson, Arizona, USA, 85721 * Author to whom correspondence should be addressed. Journal of Engineering Research

Improving classification and reconstruction of imagined images from EEG signals.

Decoding brain activity related to specific tasks, such as imagining something, is important for brain computer interface (BCI) control. While decoding of brain signals, such as functional magnetic resonance imaging (fMRI) signals and electroencephalography (EEG) signals, during observing visual images and while imagining images has been previously reported, further development of methods for improving training, performance, and interpretation of brain data was the goal of this study. We applied a Sinc-EEGNet to decode brain activity during perception and imagination of visual stimuli, and added an attention module to extract the importance of each electrode or frequency band. We also reconstructed images from brain activity by using a generative adversarial network (GAN). By combining the EEG recorded during a visual task (perception) and an imagination task, we have successfully boosted the accuracy of classifying EEG data in the imagination task and improved the quality of reconstruction by GAN. Our result indicates that the brain activity evoked during the visual task is present in the imagination task and can be used for better classification of the imagined image. By using the attention module, we can derive the spatial weights in each frequency band and contrast spatial or frequency importance between tasks from our model. Imagination tasks are classified by low frequency EEG signals over temporal cortex, while perception tasks are classified by high frequency EEG signals over occipital and frontal cortex. Combining data sets in training results in a balanced model improving classification of the imagination task without significantly changing performance in the visual task. Our approach not only improves performance and interpretability but also potentially reduces the burden on training since we can improve the accuracy of classifying a relatively hard task with high variability (imagination) by combining with the data of the relatively easy task, observing visual images.

On the Convenience of Perfect Channel Knowledge for Spatial Equalization of Correlated MIMO-VLC Links: Is it Really Worth it?

Visible Light Communication has risen as a promising technology to support indoor wireless connectivity and provide illumination services as well. Moreover, high communication performance can be achieved by implementing Multiple-Input Multiple-Output architectures. However, the crosstalk characterizing spatially correlated channels scenarios may significantly impact on reliability. Such issue is commonly addressed by means of spatial equalization, with zero forcing and minimum mean square error representing the most widespread approaches. These methods allow the mitigation of crosstalk, even though they may cause undesired noise amplification as side effect, inducing errors during signal detection and decoding. In this regard, by focusing on a more realistic approach with respect to the literature, this work overturns the belief that having perfect channel knowledge is always better. Hence, we show that, when applying spatial equalization, imperfect channel knowledge is sometimes preferable to ideal channel state information. As a result, noise amplification is reduced at the expense of a reasonably less accurate crosstalk mitigation, with better communication performance being achieved.

Joint Decoding in Downlink NOMA Systems With Finite Blocklength Transmissions for Ultrareliable Low-Latency Tasks

Future ultrareliable low-latency tasks in the Internet of Things require finite blocklength transmissions. The spectral efficiency of finite blocklength transmissions, by incorporating nonorthogonal multiple access (NOMA), can be significantly improved. In conventional NOMA systems, the successive interference cancelation (SIC) is employed for signal decoding, which is optimal for sufficient long blocklength transmissions. However, for finite blocklength transmissions, the joint decoding instead of SIC is optimal. Considering the joint decoding, we study the decoding error probability and power allocation factor optimization problem, which aims at maximizing the effective throughput at the central user under the minimum-required effective throughput constraint at the cell-edge user. We put forward a 2-D search method to find the globally optimal solution and a low-complexity alternating optimization method to find the locally optimal solution. It is illustrated that our proposed joint decoding scheme has the higher effective throughput than the conventional SIC scheme.

Direction decoding of imagined hand movements using subject-specific features from parietal EEG

Objective. Research on the decoding of brain signals to control external devices is rapidly emerging due to its versatile potential applications, including neuroprosthetic control and neurorehabilitation. Electroencephalogram (EEG)-based non-invasive brain–computer interface (BCI) systems decode brain signals to establish an augmented communication and control pathway between the brain and the computer. The development of an efficient BCI system requires accurate decoding of neural activity underlying the user’s intentions. This study investigates the directional tuning of EEG characteristics from the posterior parietal region, associated with bidirectional hand movement imagination or motor imagery (MI) in left and right directions. Approach. The imagined movement directions of the chosen hand were decoded using a combination of envelope and phase features derived from parietal EEGs of both hemispheres. The proposed algorithm uses wavelets for spectral decomposition, and discriminative subject-specific subband levels are identified based on Fisher analysis of envelope and phase features. The selected features from the discriminative subband levels are used to classify left and right MI directions of the hand using a support vector machine classifier. Furthermore, the performance of the proposed algorithm is evaluated by incorporating a maximum-variance-based EEG time bin selection algorithm. Main results. With the time bin selection approach using subject-specific features, the proposed algorithm yielded an average left vs right MI direction decoding accuracy of 73.33% across 15 healthy subjects. In addition, the decoding accuracy offered by the phase features was higher than that of the envelope features, indicating the importance of phase features in MI kinematics decoding. Significance. The results reveal the significance of the parietal EEG in decoding of imagined kinematics and open new possibilities for future BCI research.

TOUCH 3 and CALMODULIN 1/4/6 cooperate with calcium-dependent protein kinases to trigger calcium-dependent activation of CAM-BINDING PROTEIN 60-LIKE G and regulate fungal resistance in plants.

Plants utilize localized cell-surface and intracellular receptors to sense microbes and activate the influx of calcium, which serves as an important second messenger in eukaryotes to regulate cellular responses. However, the mechanisms through which plants decipher calcium influx to activate immune responses remain largely unknown. Here, we show that pathogen-associated molecular patterns (PAMPs) trigger calcium-dependent phosphorylation of CAM-BINDING PROTEIN 60-LIKE G (CBP60g) in Arabidopsis (Arabidopsis thaliana). CALCIUM-DEPENDENT PROTEIN KINASE5 (CPK5) phosphorylates CBP60g directly, thereby enhancing its transcription factor activity. TOUCH 3 (TCH3) and its homologs CALMODULIN (CAM) 1/4/6 and CPK4/5/6/11 are required for PAMP-induced CBP60g phosphorylation. TCH3 interferes with the auto-inhibitory region of CPK5 and promotes CPK5-mediated CBP60g phosphorylation. Furthermore, CPKs-mediated CBP60g phosphorylation positively regulates plant resistance to soil-borne fungal pathogens. These lines of evidence uncover a novel calcium signal decoding mechanism during plant immunity through which TCH3 relieves auto-inhibition of CPK5 to phosphorylate and activate CBP60g. The findings reveal cooperative interconnections between different types of calcium sensors in eukaryotes.

Surgical face masks do not impair the decoding of facial expressions of negative affect more severely in older than in younger adults

Surgical face masks reduce the spread of airborne pathogens but also disturb the flow of information between individuals. The risk of getting seriously ill after infection with SARS-COV-2 during the present COVID-19 pandemic amplifies with age, suggesting that face masks should be worn especially during face-to-face contact with and between older people. However, the ability to accurately perceive and understand communication signals decreases with age, and it is currently unknown whether face masks impair facial communication more severely in older people. We compared the impact of surgical face masks on dynamic facial emotion recognition in younger (18–30 years) and older (65–85 years) adults (N = 96) in an online study. Participants watched short video clips of young women who facially expressed anger, fear, contempt or sadness. Faces of half of the women were covered by a digitally added surgical face mask. As expected, emotion recognition accuracy declined with age, and face masks reduced emotion recognition accuracy in both younger and older participants. Unexpectedly, the effect of face masks did not differ between age groups. Further analyses showed that masks also reduced the participants’ overall confidence in their emotion judgements, but not their performance awareness (the difference between their confidence ratings for correct and incorrect responses). Again, there were no mask-by-age interactions. Finally, data obtained with a newly developed questionnaire (attitudes towards face masks, atom) suggest that younger and older people do not differ in how much they feel impaired in their understanding of other people’s emotions by face masks or how useful they find face masks in confining the COVID-19 pandemic. In sum, these findings do not provide evidence that the impact of face masks on the decoding of facial signals is disproportionally larger in older people.

Molecular Mechanisms of Plant Responses to Salt Stress.

Saline-alkali soils pose an increasingly serious global threat to plant growth and productivity. Much progress has been made in elucidating how plants adapt to salt stress by modulating ion homeostasis. Understanding the molecular mechanisms that affect salt tolerance and devising strategies to develop/breed salt-resilient crops have been the primary goals of plant salt stress signaling research over the past few decades. In this review, we reflect on recent major advances in our understanding of the cellular and physiological mechanisms underlying plant responses to salt stress, especially those involving temporally and spatially defined changes in signal perception, decoding, and transduction in specific organelles or cells.

A New 6D Chaotic Generator: Computer Modelling and Circuit Design

The objective of this study aims at using the Matlab-Simulink environment and the LabVIEW software environment to build computer models of a six-dimensional (6D) chaotic dynamic system. For the fixed system’s parameters, the spectrum of Lyapunov exponents and the Kaplan-York dimension are calculated. The presence of two positive Lyapunov exponents demonstrates the hyperchaotic behavior of the system. The fractional Kaplan-York dimension indicates the fractal structure of strange attractors. An active control method is extended to achieve global chaotic synchronization of two identical novel 6D chaotic systems with unknown system parameters. Based on the results obtained in Matlab-Simulink and LabVIEW models, a chaotic signal generator for the 6D chaotic system is implemented in the MultiSim environment. The experimental results show that the chaotic behavior simulation in the MultiSim environment is similar to those in the Matlab-Simulink and LabVIEW models. The simulation results demonstrate that the Pecora-Carroll method is a simple way of chaotic masking and signal decoding.

Multi-Classification of Motor Imagery EEG Signals Using Bayesian Optimization-Based Average Ensemble Approach

Motor Imagery (MI) classification using electroencephalography (EEG) has been extensively applied in healthcare scenarios for rehabilitation aims. EEG signal decoding is a difficult process due to its complexity and poor signal-to-noise ratio. Convolutional neural networks (CNN) have demonstrated their ability to extract time–space characteristics from EEG signals for better classification results. However, to discover dynamic correlations in these signals, CNN models must be improved. Hyperparameter choice strongly affects the robustness of CNNs. It is still challenging since the manual tuning performed by domain experts lacks the high performance needed for real-life applications. To overcome these limitations, we presented a fusion of three optimum CNN models using the Average Ensemble strategy, a method that is utilized for the first time for MI movement classification. Moreover, we adopted the Bayesian Optimization (BO) algorithm to reach the optimal hyperparameters’ values. The experimental results demonstrate that without data augmentation, our approach reached 92% accuracy, whereas Linear Discriminate Analysis, Support Vector Machine, Random Forest, Multi-Layer Perceptron, and Gaussian Naive Bayes achieved 68%, 70%, 58%, 64%, and 40% accuracy, respectively. Further, we surpassed state-of-the-art strategies on the BCI competition IV-2a multiclass MI database by a wide margin, proving the benefit of combining the output of CNN models with automated hyperparameter tuning.

An exploratory study of transfer learning frameworks in the context of few available shots of neurophysiological signals

Neurophysiological signals are relevant to human motions and can be used for exploring underlying neural mechanisms of human locomotion. Deep learning has been popular in the field of human–machine interaction in recent years due to its superior capacity in decoding of neurophysiological signals, such as electroencephalography (EEG) and electromyography (EMG). However, deep learning usually requires a large amount of data to be driven. In practice, it is always difficult to collect required data due to insufficient amount, poor quality, and missing data labels. In this study, we proposed two transfer learning frameworks to improve the decoding performance of deep learning in the context of only few available shots. EEG and EMG data were collected in an exoskeleton-aided walking experiment with four conditions. The results showed that classification accuracy was improved by up to 10.02% after using the proposed transfer learning frameworks. In addition, we compared different transfer learning strategies and found that convolution neural network based (CNN-based) transfer learning for EEG and EMG signals requires transferring only in convolution layers, while avoiding transferring in the fully connected (FC) layer. Based on the results, the proposed transfer framework constitutes practical tool to improve the decoding performance of neurophysiological signal for deep learning models in the context of few available shots, and our study provides a high reference value for the selection of transfer learning strategies.

Performance analysis of cognitive radio‐assisted clustering car‐following V2X communication system

SummaryIn this paper, we study high spectrum efficiency transmission in vehicle to everything (V2X) system to allow the roadside unit (RSU) broadcasts its safety information to vehicles which belong to a dedicated group. However, low latency and high reliability are tightly required in related applications of V2X; we explore advantages of cognitive radio (CR) and non‐orthogonal multiple access (NOMA) to form system, namely, CR NOMA‐V2X. Such considered system provides transmissions from the RSU to vehicles with respect to lower access latency and to enhance the packet reception probability. In the proposed scheme, the vehicles need channel state information (CSI) for signal decoding order, but we face with imperfect CSI occurs in practical scenarios. To evaluate such degraded system performance of the involved CR NOMA‐V2X, we introduce the approximate closed‐form expressions of outage probability. Numerical results indicate the validness of our derivations.

Message-Passing Receiver Design for Multiuser Multi-Backscatter-Device Symbiotic Radio Communications

Symbiotic radio (SR) has emerged as a spectrum and energy-efficient communication paradigm for future passive Internet-of-Things (IoT). In this paper, we consider a multiuser multi- backscatter-device (BD) SR communication system to enhance the spectrum efficiency, by sharing a common time-frequency resource block. Due to the presence of inter-user and inter-BD interference, multiuser and multi-BD detection in the receiver design become much more challenging. Concretely, the detection problem involves several key components: direct-link channel estimation, backscatter-link channel estimation, user signal decoding, and BD symbol detection. A conventional way is to realise these components in two separate phases, in which a channel estimation phase is followed by a data decoding phase. However, channel state information (CSI) acquisition is very difficult for the multiuser multi-BD SR communication, as compared to the case of orthogonal multiple access. In addition, the backscatter-link is relatively weak, which further increases the difficulty of CSI acquisition. To address these issues, we propose a novel receiver design to perform joint channel estimation, user data decoding, and BD symbol detection. Based on the factor graph representation of the joint estimation problem, we design a message-passing receiver for the multiuser multi-BD SR system to iteratively refine the estimation outputs. Extensive simulation results demonstrate the effectiveness of the proposed receiver design.