Signal Correlation Research Articles

Electroencephalogram Signal Correlations between Default Mode Network and Attentional Functioning

Electroencephalogram Signal Correlations between Default Mode Network and Attentional Functioning

Stimulus-Informed Generalized Canonical Correlation Analysis for Group Analysis of Neural Responses to Natural Stimuli.

Various new brain-computer interface technologies or neuroscience applications require decoding stimulus-following neural responses to natural stimuli such as speech and video from, e.g., electroencephalography (EEG) signals. In this context, generalized canonical correlation analysis (GCCA) is often used as a group analysis technique, which allows the extraction of correlated signal components from the neural activity of multiple subjects attending to the same stimulus. GCCA can be used to improve the signal-to-noise ratio of the stimulus-following neural responses relative to all other irrelevant ( non-)neural activity, or to quantify the correlated neural activity across multiple subjects in a group- wise coherence metric. However, the traditional GCCA technique is stimulus-unaware: no information about the stimulus is used to estimate the correlated components from the neural data of several subjects. Therefore, the GCCA technique might fail to extract relevant correlated signal components in practical situations where the amount of information is limited, for example, because of a limited amount of training data or group size. This motivates a new stimulus-informed GCCA (SI-GCCA) framework that allows taking the stimulus into account to extract the correlated components. We show that SI-GCCA outperforms GCCA in various practical settings, for both auditory and visual stimuli. Moreover, we showcase how SI-GCCA can be used to steer the estimation of the components towards the stimulus. As such, SI-GCCA substantially improves upon GCCA for various purposes, ranging from preprocessing to quantifying attention.

Generalized optimal statistic for characterizing multiple correlated signals in pulsar timing arrays

Generalized optimal statistic for characterizing multiple correlated signals in pulsar timing arrays

Abnormal Local Brain Activity and Cognitive Impairments in Young Non-Disabled Patients With Intracerebral Hemorrhage: A Resting-State Functional MRI Study.

Resting-state functional MRI (rs-fMRI) has identified static changes of local brain activity among patients with intracerebral hemorrhage (ICH). However, the dynamic and concordance-related characteristics of brain activity remain unclear. To investigate static, dynamic, and concordance-related features of the regional brain activity of young non-disabled ICH patients. Prospective. Thirty-three ICH patients (modified Rankin Scale score ≤2, 21% female, 46.36 ± 6.53) and 33 matched healthy controls (HCs) (21% female, 47.64 ± 6.16). 3-T, rs-fMRI using gradient echo-planar imaging, T1-weighted imaging. Neuropsychological and rs-fMRI data were acquired from all participants. Amplitude of low-frequency fluctuations (ALFF), fractional ALFF (fALFF), regional homogeneity (ReHo), voxel-mirrored homotopic connectivity, global signal correlation (GSCorr) and degree centrality (DC), and their dynamic and concordance-related changes with sliding window analysis were calculated based on rs-fMRI data at a whole-brain level. The burden of cerebral small vascular diseases (cSVD) was assessed by cSVD scores. All hemorrhage lesions were delineated on normalized T1 images. Multiple regression models, a voxel-level uncorrected P < 0.001, a cluster-level false discovery rate (FDR) corrected q < 0.05, a re-corrected qFDR <0.05 were considered significant. Pearson or Spearman correlation analyses between fMRI data and neurocognitive performance were performed. Compared to HCs, ICH patients showed significant abnormal changes of ALFF, dynamic ALFF, fALFF, ReHo, dynamic ReHo, GSCorr, DC, and voxel-wise concordance in multiple brain regions mainly including the bilateral cerebellar hemispheres, ipsilesional thalamus, and bilateral middle cingulum gyrus. The ALFF in the cerebellar posterior lobe and thalamus were significantly associated with attention (r = -0.481) and executive function (rho = -0.521) in ICH patients. Young non-disabled ICH patients exhibit static, dynamic, and concordance-related alterations of local brain activity, part of which shows associations with cognitive functions.These findings may help comprehensively understand the mechanism of cognitive impairment after ICH. 2 TECHNICAL EFFICACY: Stage 3.

Plasmonic Based Fibre Optic Sensors as an in-Situ Battery Diagnostic Technique

Plasmonic based fibre optic sensing is a technique that enables measurements of the refractive index of an analyte via a metal coated fibre optic sensor. Fibre optics are well suited to take measurements from difficult to access or remote locations, and the technique of plasmonic based sensing has been used widely in biotechnology research. In this research we demonstrate the use of the plasmonic sensing technology as a diagnostics tool in lithium-ion batteries, a novel use of this sensing technology.Plasmonic based fibre optic sensors are placed inside li-ion pouch cells, allowing access to information from directly inside the system. Our research has shown multiple promising features, including a correlation of the optical signal with cell charge and discharge, detection of cell events such as phase transitions, and crucially that the presence of the sensor has negligible impact on the cell performance. This opens up the possibility of a new battery diagnostic technique, with the potential to aid cell research and characterisation, battery usage optimisation and early detection of cell failure modes such as lithium plating. Figure 1

Decreased long-range temporal correlations in the resting-state functional magnetic resonance imaging blood-oxygen-level-dependent signal reflect motor sequence learning up to 2 weeks following training.

Decreased long-range temporal correlations (LRTC) in brain signals can be used to measure cognitive effort during task execution. Here, we examined how learning a motor sequence affects long-range temporal memory within resting-state functional magnetic resonance imaging signal. Using the Hurst exponent (HE), we estimated voxel-wise LRTC and assessed changes over 5 consecutive days of training, followed by a retention scan 12 days later. The experimental group learned a complex visuomotor sequence while a complementary control group performed tightly matched movements. An interaction analysis revealed that HE decreases were specific to the complex sequence and occurred in well-known motor sequence learning associated regions including left supplementary motor area, left premotor cortex, left M1, left pars opercularis, bilateral thalamus, and right striatum. Five regions exhibited moderate to strong negative correlations with overall behavioral performance improvements. Following learning, HE values returned to pretraining levels in some regions, whereas in others, they remained decreased even 2 weeks after training. Our study presents new evidence of HE's possible relevance for functional plasticity during the resting-state and suggests that a cortical subset of sequence-specific regions may continue to represent a functional signature of learning reflected in decreased long-range temporal dependence after a period of inactivity.

Evaluation of the Influence of the Spatial Smoothing Procedure of the Correlation Matrix on the Efficiency of the 2D-MUSIC Method

The occurrence of correlations between radio signals arriving at the antenna array of the base station reduces the effectiveness of subspace methods for positioning user devices in wireless communication networks. For the purpose of radiation sources decorrelation, a spatial smoothing algorithm can be applied dividing the base station antenna array into sub-arrays and then averaging the signal correlation matrices obtained for these sub-arrays. In this paper, we investigate the impact of various ways of dividing a flat equidistant antenna array into subarrays on the performance of the 2D-MUSIC multiple signal classification method in the scenario of ultra-dense networks operating in the FR2 band.

Altitude measurement method of VHF radar based on spatial smoothing of correlation matrix

For very high frequency (VHF) phased array radar, the key problem to be solved in altitude measurement is the super-resolution spatial spectrum estimation under the condition of coherent sources. The spatial smoothing algorithm is a kind of decorrelation algorithm with excellent properties, but the decorrelation process is at the expense of the effective array aperture. Because it only uses the autocorrelation information of the subspace, its performance is significantly reduced, when the positions of the coherent sources are very close. In order to solve the above problems, this paper proposes an altitude measurement method of VHF radar based on the space smoothing of autocorrelation and cross-correlation matrix, which is used to realize the correlation and super-resolution processing of echo signals and multipath signals. The proposed method does not need to construct a weighting matrix, and can make full use of the received data, enhance the signal components in the equivalent spatial smoothing matrix, reduce the impact of noise, and improve the resolution of coherent sources. The simulation results show that the weighted spatial smoothing method proposed in this paper is correct and effective.

Aeroengine Stall Warning by Multicorrelation Analysis

The present work aims to develop a stall warning method that is reliable even when the compressor encounters inlet distortion. For this purpose, multicorrelation analysis (MCA) is proposed to provide an effective indicator for the stall warning method. The relationship between MCA and correlation measures was discussed. It was demonstrated that MCA can provide an overall evaluation of both temporal and spatial correlations of multiple signals collected by different sensors. An experimental study of MCA was carried out on a low-speed axial compressor. Sensors were mounted on the casing to sample the dynamic pressure signals used for stall warning. The correlation of the signals was found to be influenced by the steady blade loading and the disturbance energy. Autocorrelation and cross-correlation were adequate when the inlet flow was uniform, but the accuracy of these methods could be affected by distortion. In contrast, MCA was robust under uniform and distorted inlet conditions.

Association between electroencephalogram-based sleep characteristics and physical health in the general adult population

We examined the associations between electroencephalogram (EEG)-based sleep characteristics and physical health parameters in general adults via a cross-sectional study recruiting 100 volunteers aged 30–59 years. Sleep characteristics were measured at home using a portable multichannel electroencephalography recorder. Using the k-means + + clustering method, according to 10 EEG-based parameters, participants were grouped into better (n = 39), middle (n = 46), and worse (n = 15) sleep groups. Comparing 50 physical health parameters among the groups, we identified four signals of difference (P < 0.05), including systolic (sBP) and diastolic blood pressure (dBP), γ-glutamyl transpeptidase (γ-GTP), and serum creatinine, where sBP reached a Bonferroni-corrected threshold (P < 0.001). The sBP was higher by 7.9 (95% confidence interval 1.9–13.9) and 15.7 (7.3–24.0) mmHg before adjustment and 5.4 (− 0.1–10.9) and 8.7 (1.1–16.3) mmHg after adjustment for age, sex, body mass index, smoking, drinking habits, and 3% oxygen desaturation index in the middle and worse sleep groups, respectively, than in the better group. As another approach, among 500 combinations of EEG-based and physical health parameters, there were 45 signals of correlation, of which 4 (N1% and sBP, dBP, γ-GTP, and triglycerides) reached a Bonferroni-corrected threshold (P < 0.0001). Thus, EEG-based sleep characteristics are associated with several physical health parameters, particularly sBP.

Do underwriting profit factors affect general insurance firms’ profitability in South Africa?

This research paper examines the correlation between underwriting profit factors and the overall profitability of publicly traded general insurance companies operating in South Africa. The study analyzed a sample of 36 insurers, considering their quantifiable markets and accessible financial data from 2008 to 2019. Employing signal correlation analysis, the investigation explored the associations between various financial indicators and Return on Assets (ROA). The results revealed negative correlations between ROA and the logarithms of total investment (TI), shareholder funds (SF), and underwriting profits (UWP), with correlation coefficients of –0.4500, –0.3365, and –0.4050, respectively. These findings indicate that as TI, SF, and UWP increase, there is a tendency for ROA to decrease for general insurance companies in South Africa. Furthermore, a positive relationship was observed between the earning-asset ratio and ROA. This suggests that as the earning-asset ratio rises, the ROA of general insurance firms in South Africa tends to improve, indicating a potentially favorable impact on profitability. The significant findings of this study emphasize the importance of prioritizing effective risk management practices within insurance firms. By implementing these measures, such as minimizing the likelihood of claims and ensuring accurate reflection of assumed risks in premium charges, insurance companies can maintain positive underwriting profit. This, in turn, has the potential to enhance their overall profitability.

Photonic distributed compressive sampling of multi-node wideband sparse radio frequency signals.

A photonic distributed compressive sampling (PDCS) approach for identifying the spectra of multi-node wideband sparse signals is proposed. The scheme utilizes wavelength division multiplexing (WDM) technology to transmit multi-node signals to a central station, where distributed compressive sampling (DCS) based on the random demodulator (RD) model is employed to simultaneously identify the signal spectrum. By exploiting signal correlations among nodes, DCS achieves a higher compression ratio of the sampling rate than single-node compressive sampling (CS). In a semi-physical simulation experiment, we demonstrate the feasibility of the approach by recovering the spectra of two wideband sparse signals from nodes located 20 km and 10 km away. The spectra of two signals with a mixed support-set sparsity of 2 and 4 are recovered with a compression ratio of 8 and 4, respectively. We further investigate the impact of common parts and the number of nodes on PDCS performance through numerical simulation. The proposed system takes advantage of the ultra-high bandwidth of photonic technology and the low loss of optical fiber transmission, making it suitable for long-distance, multi-node, and large-coverage electromagnetic spectrum identification.

A Volume‐Based Alternative for classifying ATN: Data from the Tau Propagation over Time (T‐POT) cohort

AbstractBackgroundAlzheimer’s disease (AD) is characterized by the cerebral accumulation of amyloid‐beta (A), tau (T), and progressive neurodegeneration (N). The widely used ATN system, with regard to positron emission tomography (PET) biomarkers, categorizes AD based on the mean signal in specific regions of interest (ROI). However, this procedure disregards the spatial extent of pathology and neurodegeneration. Here, we propose an alternative quantification of the volume, i.e., fill states, of A, T and N in (pre)‐clinical AD.MethodWe analyzed data from the Tau Propagation over Time (T‐POT) study, including cognitively unimpaired individuals (CU, n = 58), and patients with mild cognitive impairment (MCI, n = 20) or AD dementia (n = 4). C11‐PIB‐PET (A), 18F‐AV1451 (T) and perfusion‐phase 18F‐AV1451 scans (N) were spatially and intensity‐normalized (reference: cerebellum). To quantify the volume of A, T and N, we z‐standardized and subsequently binarized all scans within–modality using a z‐score threshold. Fill states were then computed as the sum of abnormal voxels relative to a whole‐brain mask. Finally, mean fill states were compared across groups of clinical status (CU, MCI, AD) and partial correlations of either fill states or mean PET signal in established, tracer‐specific ROIs with cognitive performance (MMSE) were computed, adjusting for age, sex and education.ResultMean fill states reflected clinical status, as they increased with disease progression (CU: A = 4%, T = 4%, N = 3%; MCI: A = 15%, T = 11%, N = 4%; AD dementia: A = 20%, T = 23%, N = 5%). Moreover, A and T fill states were negatively associated with MMSE (rhoA = ‐.299, p &lt; .001; rhoT = ‐.318, p &lt; .01; rhoN = ‐.147, p = .20), while associations of mean PET signal and MMSE tended to be weaker (rhoA(global) = ‐.255, p = .03; rhoT(temporalmetaROI) = ‐.275, p = .01; rhoN(metaROI) = .179, p = .12).ConclusionWe present a competitive quantification scheme for ATN that is associated with both, clinical status and cognitive performance. These results, while currently validated in a larger sample, suggest that the spatiotemporal dynamics of pathology and neurodegeneration in the AD continuum are well captured by our multi‐parametric approach, which is possibly superior compared to classification from mean PET signal intensity.

Towards data driven quality monitoring: alignment and correlation of photodiode-based co-axial melt pool monitoring signals to part quality in laser powder bed fusion

Online quality control of advanced manufacturing processes often utilize process monitoring as a qualification method to reduce or eliminate time consuming and costly destructive and non-destructive tests. The need for process monitoring based qualification becomes more apparent with the advent of laser based additive manufacturing for complex geometries and lot-size-one production. However, the usability of the process monitoring solution heavily relies on the accuracy of the established alignment and assignment of process signals to specific part and defects herein. This research proposes a data fusion strategy for data pre-processing to structure and align multi-source sensor data for improving correlation accuracy in process monitoring. A laser powder bed fusion system equipped with a photodiode-based co-axial melt pool monitoring is used for the study. First, the monitoring system collects photon emission data from the melt pool using photodiodes with high frequency (>100 kHz) which are aligned spatially and temporally using position data (xy-coordinates) of the galvanometer scanner and the laser ON/OFF signals. Next, a clustering approach is used to assign each photodiode signal with the individual parts on the build platform. Finally, correlation between melt pool monitoring signals and part level porosity is studied using micro-computed X-ray tomography (μCT) from a build job containing parts produced under varying process conditions.

Performance comparison of conventional and striation-based beamformers for underwater bearing detection of pulse sources.

The multi-path and dispersion properties of shallow water waveguides make conventional beamforming (CBF) face issues such as beam shift, broadening, splitting, output distortion, and array gain reduction. In this paper, the striation-based beamforming (SBF) is investigated to address these issues. SBF differs from CBF by utilizing frequency-shift processing along interference striations. The performance difference between CBF and SBF is compared. It demonstrates that under ideal waveguide modeling with pulse sources, SBF can achieve a beam output response that is close to the plane wave condition. The speed term of SBF's response is approximately independent of modal indexes, which equips SBF to form a unique beam output and guarantee the beam resolution. The processing of consistent signals along the striation maintains the optimal signal correlation, which makes SBF ensure the output fidelity and array gain. To shift the mainlobe of SBF to the source azimuth, the time delay related to the waveform truncation point can be introduced to pre-compensate the array signals. There exist two theoretical accuracy limits to using the truncation. First, truncation time corresponds to the waveform point at r0/c (r0 is the source range), and the mainlobe of SBF is directed to the source azimuth. Second, truncation corresponds to the pulse peak point, and the azimuth estimation accuracy of SBF gets close to CBF. Simulations and experimental results are given as illustrations.

Статистическая модель высоконаправленной цифровой антенной решетки пространственно-корреляционным методом обработки сигналов

The article presents a statistical model of a digital antenna array with a spatial correlation method of signal processing. The model is obtained on the basis of the Neumann-Pearson criterion for the conditions of a stationary Gaussian distribution of the output process in spatial correlation signal processing. This model makes it possible to numerically evaluate the probabilistic characteristics of radar signal detection for various orders of nonlinear processing under the influence of natural radio interference and can be recommended for the study and optimization of the construction of the antenna array aperture with spatial correlation signal processing, providing a given spatial noise immunity of radio engineering systems.

The Effects of the Local Environment on a Compact Radio Interferometer I: Cross-Coupling in the Tianlai Dish Pathfinder Array

The visibilities measured by radio astronomical interferometers include non-astronomical correlated signals that arise from the local environment of the array. These correlated signals are especially important in compact arrays such as those under development for 21[Formula: see text]cm intensity mapping. The amplitudes of the contaminated visibilities can exceed the expected 21[Formula: see text]cm signal and represent a significant systematic effect. We study the receiver noise radiated by antennas in compact arrays and develop a model for how it couples to other antennas. We apply the model to the Tianlai Dish Pathfinder Array (TDPA), a compact array of 16, 6-m dish antennas. The coupling model includes electromagnetic simulations, measurements with a network analyzer, and measurements of the noise of the receivers. We compare the model to drift-scan observations with the array and set requirements on the level of antenna cross-coupling for 21[Formula: see text]cm intensity mapping instruments. We find that for the TDPA, cross-coupling would have to be reduced by three orders of magnitude in order to contribute negligibly to the visibilities.

Aging seeds of weedy broomrapes and witchweeds lose sensitivity to strigolactones as DNA demethylates

Societal Impact StatementBroomrapes and witchweeds have devastating effects on crops in parts of Europe, Africa, and Asia. The key to their success is the production of copious, long‐lived seeds, which germinate in response to the perception of chemicals released by their hosts' roots. Here, it is proposed that the success of a parasite population will decrease as its seed bank ages due to the gradual decrease in sensitivity to the host signals in correlation with alterations of the seeds' epigenetic landscape, which is the set of non‐hard‐coded genetic information that influences gene function. This research provides important information toward a better understanding of parasite seed bank dynamics, which must be accounted for in future control strategies.Summary Broomrapes (Phelipanche and Orobanche spp.) and witchweeds (Striga and Alectra spp.) are obligate root parasitic weeds responsible for major crop yield losses worldwide. Their success in agricultural landscapes is attributable to their ability to produce thousands of long‐lived minute seeds that coordinate their germination with the presence of nearby hosts by perceiving host‐derived strigolactones. The processes underlying the alleged decade(s)‐long persistence in the field are understudied. Here, we used an accelerated seed aging method coupled with germination bioassays and an enzyme‐linked immunosorbent assay (ELISA), which allowed studying seed aging in Orobanchaceae in a comparative manner. We show that the losses of seed viability and germinability associated with seed aging are accompanied by a decrease in both strigolactone sensitivity and global DNA methylation. Our results also suggest that seeds of broomrapes are longer‐lived than those of witchweeds. Overall, this emphasizes the need for further research into how epigenetic mechanisms contribute to alterations in seed viability in parasitic weeds and how seed aging influences seed responses to their environment.

Bayesian variational transformer: A generalizable model for rotating machinery fault diagnosis

Transformer has been widely applied in the research of rotating machinery fault diagnosis due to its ability to explore the internal correlation of vibration signals. However, challenges still exist despite the countless efforts. Generally, Transformer is more prone to overfitting than CNN on small-scale datasets. In practical engineering, collecting sufficient fault samples for training is difficult, resulting in poor generalization of Transformer. In addition, the measured signals are often accompanied with severe noise, further reducing the generalization performance of the model. Meanwhile, the collected signals often follow different distributions due to the changing operating conditions, which places higher demands on the generalizability of Transformer. This paper proposes a Bayesian variational Transformer (Bayesformer) to cope with the abovementioned problems. In Bayesformer, all the attention weights are treated as latent random variables, rather than determined values as the previous studies. This allows to train an ensemble of networks, instead of a single one, enhancing the generalizability of the model. Three experimental studies are conducted to illustrate the developed model and superior diagnostic performance is showed throughout the experiments.

A derivative, integral, and proportional features extractor for fault detection in dynamic processes

Detecting faults in dynamic systems is challenging due to temporal dependencies and signal correlations. Feature extraction from time-series data is a common step in fault detection, which is usually performed according two main approaches: knowledge-based and statistical. Knowledge-based methods provide interpretable features but require significant design time; also, they usually lack generality. Statistical methods are faster but lead to dimensionality issues and lack of interpretability. To address these challenges, we propose an interpretable and automated feature extraction method. It combines the benefits of knowledge-based and statistical methods, offering a fast solution for extracting effective and interpretable indicators without requiring prior domain knowledge. Also, this method consistently computes a fixed set of interpretable features describing the process’s dynamic behavior.We extensively compare our approach with state-of-the-art methods considering nine open-source datasets spanning various domains. Our results show that classifiers trained on features extracted with the proposed method achieve performance comparable to those provided by state-of-the-art automatic features extractors, according to F1-score, true positive rate, and false positive rate. In addition, our approach proves to be more robust to dimensionality issues and enhances interpretability, extracting a reduced set of features effective at providing insight into the detected anomalies’ characteristics. Additionally, we demonstrate that the selected features maintain consistent performance across different classifiers, showcasing their versatility.