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Renewable energy, resource efficiency, and globalization are recognized as pivotal factors in the pursuit of environmental sustainability. This study's novelty lies in examining the impact of resource efficiency, the influence of globalization, and the adoption of renewable energy sources on energy‐related CO2 emissions in the United States spanning the years from 1990 to 2020 is the focus of this study. Additionally, this investigation leveraged a diverse range of time‐frequency‐quantile techniques, encompassing quantile‐on‐quantile regressions, wavelet coherence, cross quantile coherence, wavelet‐based quantile correlation, and quantile regression, which is an additional novel aspect of this study. The findings of this research indicate that resource efficiency, the utilization of renewable energy sources, and the forces of globalization collectively contribute to enhancing ecological sustainability by reducing energy‐related CO2 in the United States across a range of quantiles, time periods, and temporal contexts. The study posits that these findings offer a comprehensive framework that can guide policymakers in the United States toward taking tangible measures to advance ecological sustainability while curbing energy‐related CO2 emissions. [ABSTRACT FROM AUTHOR]

The mechanistic-empirical road pavement design methods necessitate knowledge of the dynamic modulus master curve of asphalt mixtures. This requires the designer to accurately define both the load frequency and the temperature of the pavement structure. The parameter of load frequency is of great importance in laboratory fatigue tests. However, it presents a challenge since road structures are loaded in the time domain, not the frequency domain. This complexity has prompted researchers to develop timefrequency conversion methods to estimate the corresponding frequency from the duration of traffic-induced stress or deformation pulses. Recent findings indicate that the time-frequency equivalence factor is considerably lower than previously assumed. In this study, we introduce a new four-parameter empirical RAMBO material model, derived from the Ramberg-Osgood equation, which offers parameters to determine the time-frequency equivalence factor for asphalt mixtures. The relationship between the moving wheel load velocity and the load impulse time is analysed, primarily based on experimental data and computational methods from existing literature. A method for determining the wavelength of the load impulse is proposed. The theoretical correlation indicates that a wheel load travelling at 100 km/h results in a dynamic deformation frequency of 10 Hz. However, for a strain impulse, the same speed corresponds to a much lower frequency of 3-4 Hz. This study offers a refined understanding of the time-frequency equivalence factor, which is of crucial importance for more accurate road pavement design and fatigue testing. [ABSTRACT FROM AUTHOR]

A signal is primarily generated when two objects collide, with one falling from a specific height and the other disrupting its balance. This signal contains an important component that aids in the diagnosis of material damage. In this work, a piezoelectric thin film sensor made of polyvinylidene fluoride (PVDF) was employed to monitor the impact signal and process the recorded signal. By stacking a left and right piezoelectric film sensor, drop impact experiments were performed on rubber sheet material. The data-acquisition system captured the impact signal, and a signal processing tool was used to assess its performance. To determine the time-frequency domain visibility of the impact signal, the short-time Fourier transform (STFT) and continuous wavelet transform (CWT) were used. The detection of the elastic signal and impact force on the material is suitable for piezoelectric film. A steel impactor strikes a silicon rubber sheet at a speed of 3.43 m/s and exerts an average impact force of 72.6 kN. To make the impact signal more visible, the noise signal can be denoised using a bandpass filter. The continuous wavelet transform has a high time-frequency resolution compared to the short-time Fourier transform. Additionally, the analysis of non-stationary signals is improved by using STFT and CWT approaches. Thus, the elastic signal detection in the material may be recognized using the average total of the signals identified by the sensor. [ABSTRACT FROM AUTHOR]

Induction motors (IM) play a fundamental role in the industrial sector because they are robust, efficient, and low-cost machines. Changes in the environment, installation errors, or modifications to working conditions can generate faults in induction motors. The trend on IM fault detection is focused on the design techniques and sensors capable of evaluating multiple faults with various signals using non-invasive analysis. The methodology is based on processing electric current signals by applying the short-time Fourier transform (STFT). Additionally, the computation of the mean and standard deviation of infrared thermograms is proposed as main indicators. The proposed system combines both parameters by means of Support Vector Machine and k-nearest-neighbor classifiers. The development of the diagnostic system was done with digital hardware implementations using a Xilinx PYNQ Z2 card that integrates an FPGA with a microprocessor, thus taking advantage of the acquisition and processing of digital signals and images in hardware. The proposed method has proved to be effective for the classification of healthy (HLT), misalignment (MAMT), unbalance (UNB), damaged bearing (BDF), and broken rotor bar (BRB) faults with an accuracy close to 99%. [ABSTRACT FROM AUTHOR]

With the rapid development of national infrastructure projects, there has been a significant increase in intersecting lines in transportation construction. As a result, rotating bridges are increasingly used in engineering projects that span existing railway lines. In order to study the spatial response characteristics and vibration wave transmission mechanisms of the rotating bridge structure under the loading of existing railway trains, field experiments and numerical analyses were conducted. The response characteristics of these bridges were investigated under different types and speeds of adjacent existing lines. A comprehensive methodology has been proposed, integrating the time domain spectrum and the Hilbert–Huang Transform (HHT) energy spectrum for signal processing and vibration analysis. The analysis was carried out using MATLAB 2018a software. This methodology was applied to analyze the test data. The results show that significant resonance phenomenon occurs in the girders of the rotating bridge under the loading of trains on the existing line. The low-frequency component f1 (2–5 Hz) is the primary factor contributing to the amplification of the acceleration response in the rotating bridge, while f3 (10–13 Hz) plays a secondary role. The frequency distribution characteristics of vibration waves caused by train loads on the existing line have a significant influence on the acceleration response of the rotating bridge's girders. The predominant frequency of vibration waves at each measuring point along the transmission path shows a trend of decreasing → increasing → decreasing. The impact on the rotating bridge structure of vibration waves generated by low-speed freight trains on existing railways is greater. The research findings are of great importance for studying the dynamic response of rotating bridges adjacent to existing railway lines. [ABSTRACT FROM AUTHOR]

Several popular time-frequency techniques, including the Wigner-Ville distribution, smoothed pseudo-Wigner-Ville distribution, wavelet transform, synchrosqueezing transform, Hilbert-Huang transform, and Gabor-Wigner transform, are investigated to determine how well they can identify damage to structures. In this work, a synchroextracting transform (SET) based on the short-time Fourier transform is proposed for estimating post-earthquake structural damage. The performance of SET for artificially generated signals and actual earthquake signals is examined with existing methods. Amongst other tested techniques, SET improves frequency resolution to a great extent by lowering the influence of smearing along the time-frequency plane. Hence, interpretation and readability with the proposed method are improved, and small changes in the time-varying frequency characteristics of the damaged buildings are easily detected through the SET method. [ABSTRACT FROM AUTHOR]

In this paper, we utilize the Chicago Board Option Exchange (CBOE) implied volatility indices to estimate the time-frequency information transmission among financial markets from 01.08.2008 to 31.10.2019. In doing so, we utilize the rolling window wavelet correlation (RWWC), Diebold & Yilmaz (The Economic Journal 119: 158–171, 2012), and Barunik & Krehlik (Journal of Financial Econometrics 16: 271–296, 2018). Our empirical findings suggest short-term and long-term dynamic connectedness between implied volatility indices of alternative assets. The long-term analysis findings suggest potential hedging and diversification opportunities that can be exploited by taking offsetting positions across volatility indices. The findings confirm heterogeneity between short-term and long-term connectedness results. Our findings also show superior out of sample hedging effectiveness of GVZ. The implications of the findings are further discussed in the paper. [ABSTRACT FROM AUTHOR]

We propose a two-step procedure for atomic decomposition of multichannel EEGs, based upon multivariate matching pursuit and dipolar inverse solution, from which atoms representing relevant EEG structures are selected according to prior knowledge. We detect sleep spindles in 147 polysomnographic recordings from the Montreal Archive of Sleep Studies. Detection is compared with human scorers and two state-of-the-art algorithms, which find only about a third of the structures conforming to the definition of sleep spindles and detected by the proposed method. We provide arguments supporting the thesis that the previously undetectable sleep spindles share the same properties as those marked by human experts and previously applied methods, and were previously omitted only because of unfavorable local signal-to-noise ratios, obscuring their visibility to both human experts and algorithms replicating their markings. All detected EEG structures are automatically parametrized by their time and frequency centers, width duration, phase, and spatial location of an equivalent dipolar source within the brain. It allowed us, for the first time, to estimate the spatial gradient of sleep spindles frequencies, which not only confirmed quantitatively the well-known prevalence of higher frequencies in posterior regions, but also revealed a significant gradient in the sagittal plane. The software used in this study is freely available. [ABSTRACT FROM AUTHOR]

Tau rhythms are largely defined by sound responsive alpha band (~8–13 Hz) oscillations generated largely within auditory areas of the superior temporal gyri. Studies of tau have mostly employed magnetoencephalography or intracranial recording because of tau's elusiveness in the electroencephalogram. Here, we demonstrate that independent component analysis (ICA) decomposition can be an effective way to identify tau sources and study tau source activities in EEG recordings. Subjects (N = 18) were passively exposed to complex acoustic stimuli while the EEG was recorded from 68 electrodes across the scalp. Subjects' data were split into 60 parallel processing pipelines entailing use of five levels of high‐pass filtering (passbands of 0.1, 0.5, 1, 2, and 4 Hz), three levels of low‐pass filtering (25, 50, and 100 Hz), and four different ICA algorithms (fastICA, infomax, adaptive mixture ICA [AMICA], and multi‐model AMICA [mAMICA]). Tau‐related independent component (IC) processes were identified from this data as being localized near the superior temporal gyri with a spectral peak in the 8–13 Hz alpha band. These "tau ICs" showed alpha suppression during sound presentations that was not seen for other commonly observed IC clusters with spectral peaks in the alpha range (e.g., those associated with somatomotor mu, and parietal or occipital alpha). The choice of analysis parameters impacted the likelihood of obtaining tau ICs from an ICA decomposition. Lower cutoff frequencies for high‐pass filtering resulted in significantly fewer subjects showing a tau IC than more aggressive high‐pass filtering. Decomposition using the fastICA algorithm performed the poorest in this regard, while mAMICA performed best. The best combination of filters and ICA model choice was able to identify at least one tau IC in the data of ~94% of the sample. Altogether, the data reveal close similarities between tau EEG IC dynamics and tau dynamics observed in MEG and intracranial data. Use of relatively aggressive high‐pass filters and mAMICA decomposition should allow researchers to identify and characterize tau rhythms in a majority of their subjects. We believe adopting the ICA decomposition approach to EEG analysis can increase the rate and range of discoveries related to auditory responsive tau rhythms. [ABSTRACT FROM AUTHOR]

Objective: Most studies in epileptic seizure detection and classification focused on classifying different types of epileptic seizures. However, localization of the epileptogenic zone in epilepsy patient brain's is paramount to assist the doctor in locating a focal region in patients screened for surgery. Therefore, this paper proposed robust models for the localization of epileptogenic areas for the success of epilepsy surgery. Method: Advanced feature extraction techniques were proposed as effective feature extraction techniques based on Electroencephalogram (EEG) rhythms extracted from Fourier Basel Series Expansion Multivariate Empirical Wavelet Transform (FBSE-MEWT). The proposed extracted EEG rhythms of δ , θ , α , β and γ features were used to obtain a joint instantaneous frequency and amplitude components using a sub-band alignment approach. The features are used in Sparse Autoencoder (SAE), Deep Belief Network (DBN), and Support Vector Machine (SVM) with the optimized capability to develop three new models: 1. FMEWT–SVM 2. FMEWT_SAE–SVM, and 3. FMEWT–DBN–SVM. The EEG signal was preprocessed using a proposed Multiscale Principal Component Analysis (mPCA) to denoise the noise embedded in the signal. Main results: The developed models show a significant performance improvement, with the SAE–SVM outperforming other proposed models and some recently reported works in literature with an accuracy of 99.7% using δ -rhythms in channels 1 and 2. Significance: This study validates the EEG rhythm as a means of discriminating the embedded features in epileptic EEG signals to locate the focal and non-focal regions in the epileptic patient's brain to increase the success of the surgery and reduce computational cost. [ABSTRACT FROM AUTHOR]

Problem‐solving often requires creativity and is critical in everyday life. However, the neurocognitive mechanisms underlying creative problem‐solving remain poorly understood. Two mechanisms have been highlighted: the formation of new connections among problem elements and insight solving, characterized by sudden realization of a solution. In this study, we investigated EEG activity during a modified version of the remote associates test, a classical insight problem task that requires finding a word connecting three unrelated words. This allowed us to explore the brain correlates associated with the semantic remoteness of connections (by varying the remoteness of the solution word across trials) and with insight solving (identified as a Eurêka moment reported by the participants). Semantic remoteness was associated with power increase in the alpha band (8–12 Hz) in a left parieto‐temporal cluster, the beta band (13–30 Hz) in a right fronto‐temporal cluster in the early phase of the task, and the theta band (3–7 Hz) in a bilateral frontal cluster just prior to participants' responses. Insight solving was associated with power increase preceding participants' responses in the alpha and gamma (31–60 Hz) bands in a left temporal cluster and the theta band in a frontal cluster. Source reconstructions revealed the brain regions associated with these clusters. Overall, our findings shed new light on some of the mechanisms involved in creative problem‐solving. [ABSTRACT FROM AUTHOR]

Sexually objectified women are perceived as dehumanized. This may affect the behavioral and neural responses underlying the observer's empathic reactions for their physical pain, although this hypothesis still lacks empirical support. In the present study, we measured the electrophysiological activity of 30 participants (14 females and 16 males), in an empathy for physical pain paradigm in which pictures of sexualized and non‐sexualized women were presented in painful and non‐painful situations. The behavioral results revealed that sexualized women were evaluated as experiencing less pain than non‐sexualized women. Neural evidence corroborated this finding showing that the perception of vicarious physical pain is lacking for sexualized women in both event‐related potentials (ERPs) and brain oscillation domains. Specifically, the P2 component and the event‐related synchronization/desynchronization (ERS/ERD) on the mu frequency band differed between painful and non‐painful stimulation exclusively when women were not sexualized. Our results provide the first evidence that the neurophysiological responses to the vicarious experience of physical pain are dampened or even absent for sexualized women. These findings expand our understanding of the neurophysiological signatures of empathic processes and highlight the detrimental effect of a sexual‐objectification bias in everyday contexts. The influence of the sexualization bias on the empathy for physical pain has been only tested in hypothetical scenarios, although it has possible implication in the gender‐based violence. In this EEG study, we provide the first empirical evidences that the vicarious experience of physical pain is dampened or even absent for sexualized women. These findings point to the detrimental effect of a sexual‐objectification bias, deepening our knowledge of the neurophysiological signatures of empathetic processes. [ABSTRACT FROM AUTHOR]

Visually induced motion sickness (VIMS) is a common phenomenon when using visual devices such as smartphones and virtual reality applications, with symptoms including nausea, fatigue, and headache. To date, the neuro-cognitive processes underlying VIMS are not fully understood. Previous studies using electroencephalography (EEG) delivered mixed findings, with some reporting an increase in delta and theta power, and others reporting increases in alpha and beta frequencies. The goal of the study was to gain further insight into EEG correlates for VIMS. Participants viewed a VIMS-inducing visual stimulus, composed of moving black-and-white vertical bars presented on an array of three adjacent monitors. The EEG was recorded during visual stimulation and VIMS ratings were recorded after each trial using the Fast Motion Sickness Scale. Time–frequency analyses were conducted comparing neural activity of participants reporting minimal VIMS (n = 21) and mild–moderate VIMS (n = 12). Results suggested a potential increase in delta power in the centro-parietal regions (CP2) and a decrease in alpha power in the central regions (Cz) for participants experiencing mild–moderate VIMS compared to those with minimal VIMS. Event-related spectral perturbations (ERSPs) suggested that group differences in EEG activity developed with increasing duration of a trial. These results support the hypothesis that the EEG might be sensitive to differences in information processing in VIMS and minimal VIMS contexts, and indicate that it may be possible to identify neurophysiological correlate of VIMS. Differences in EEG activity related to VIMS may reflect differential processing of conflicting visual and vestibular sensory information. [ABSTRACT FROM AUTHOR]

Semantic memory remains relatively stable with normal cognitive aging and declines in early stages of neurodegenerative disease. We measured electroencephalography (EEG) oscillatory correlates of semantic memory retrieval to examine the effects of normal and pathological aging. Twenty‐nine cognitively healthy young adults (YA), 22 cognitively healthy aging adults (HA) and 20 patients with mild cognitive impairment (MCI) completed a semantic memory retrieval task with concurrent EEG recording in which they judged whether two words (features of objects) led to retrieval of an object (retrieval) or not (non‐retrieval). Event‐related power changes contrasting the two conditions (retrieval vs. non‐retrieval) within theta, alpha, low‐beta and high‐beta EEG frequency bands were examined for normal aging (YA vs. HA) and pathological aging effects (HA vs. MCI). With no behavioural differences between the two normal age groups, we found later theta and alpha event‐related power differences between conditions only in YA and a high‐beta event‐related power difference only in HA. For pathological aging effects, with reduced accuracy in MCI, we found different EEG patterns of early event‐related beta power differences between conditions in MCI compared with HA and an event‐related low‐beta power difference only in HA. Beta oscillations were correlated with behavioural performance only in HA. We conclude that the aging brain relies on faster (beta) oscillations during the semantic memory task. With pathological aging, retrieval accuracy declines and pattern of beta oscillation changes. The findings provide insights about age‐related neural mechanisms underlying semantic memory and have implications for early detection of pathological aging. [ABSTRACT FROM AUTHOR]

Time-frequency signal processing offers a significant advantage over temporal or frequency-only methods, but representations require optimisation for a given signal. Standard practice includes choosing the appropriate time-frequency distribution and fine-tuning its parameters, usually via visual inspection and various measures—the most commonly used ones are based on the Rényi entropies or energy concentration by Stanković. However, a discrepancy between the observed representation quality and reported numerical value may arise when the filter kernel has greater adaptability. Herein, a performance measure derived from the Abbot–Firestone curve similar to the volume parameters in surface metrology is proposed as the objective function to be minimised by the proposed minimalistic differential evolution variant that is parameter-free and uses a population of five members. Tests were conducted on two synthetic signals of different frequency modulations and one real-life signal. The multiform tiltable exponential kernel was optimised according to the Rényi entropy, Stanković's energy concentration and the proposed measure. The resulting distributions were mutually evaluated using the same measures and visual inspection. The optimiser demonstrated a reliable convergence for all considered measures and signals, while the proposed measure showed consistent alignment of reported numerical values and visual assessments. [ABSTRACT FROM AUTHOR]

In this study, elastic waves generated from a SKD11 (HV670) steel specimen under various corrosive solutions were investigated. The frequency characteristics of the waves, as well as how hydrogen aggregation and crack propagation affected these characteristics, were studied using time-frequency analysis conducted in LabVIEW. The waves detected were either in a low frequency band, below 40 kHz, or in a high frequency band, above 60 kHz. The low frequency waves below 40 kHz were caused by hydrogen aggregation and corrosion, while the high frequency waves above 60 kHz were caused by crack initiation and propagation due to HAC. Inspection by SEM confirmed that the grains and cracks were caused by HAC via hydrogen embrittlement. [ABSTRACT FROM AUTHOR]

Increased error‐related negativity (ERN), a measure of error monitoring, has been suggested as a biomarker of obsessive–compulsive disorder (OCD). Additional insight into error monitoring is possible using time‐frequency decomposition of electroencephalographic (EEG) data, as it allows disentangling the brain's parallel processing of information. Greater error‐related theta is thought to reflect an error detection signal, while delta activity may reflect more elaborative post‐detection processes (i.e., strategic adjustments). Recent investigations show that decreased error‐related alpha may index attentional engagement following errors; additionally, increases and decreases in error‐related beta could reflect motor inhibition and motor preparation, respectively. However, time‐frequency dynamics of error monitoring in OCD are largely unknown. The present study examined time‐frequency theta, delta, alpha and beta power in early adolescents with OCD using a data‐driven, cluster‐based approach. The aim was to explore electrocortical measures of error monitoring in early adolescents with (n = 27, 15 females) and without OCD (n = 27, 14 females) during an arrowhead version of the flanker task while EEG activity was recorded. Results indicated that the OCD group was characterized by increased ERN and error‐related theta, as well as reduced error‐related beta power decrease (i.e., greater power) compared to participants without OCD. Greater error‐related beta explained variance in OCD over and above the ERN and error‐related theta. By examining separate time‐frequency measures, the present study provides novel insights into the dynamics of error monitoring, suggesting that pediatric OCD may be characterized by enhanced error monitoring (i.e., greater theta power) and post‐error inhibition (i.e., reduced beta power decrease). The present study provided novel insights into the dynamics of error monitoring by capitalizing on both event‐related potentials and time‐frequency measures. The results suggest that pediatric OCD may be characterized by enhanced error monitoring (i.e., greater ERN and theta power) and post‐error inhibition, and reduced motor preparation (i.e., reduced beta power decrease). Overall, these results are promising as examining error‐related time‐frequency measures could enhance clinical utility. [ABSTRACT FROM AUTHOR]

EEG methods offer a promising approach to study the development of attention or attention‐related processes such as change‐detection and attentional capture. However, the development of these attention processes from early to middle childhood is not well understood. In the current study, we utilized a passive three‐stimulus oddball paradigm to examine age‐related changes in auditory change‐detection and attentional capture in a large sample of children across childhood (N = 475; 249 female, 226 male; Mage = 6.71; SDage = 2.22; Rangeage = 4.01–11.5 years). Conventional ERP analyses revealed no age‐related changes in change detection (mismatch negativity) and attentional capture (P3a) components, but we observed age‐related reductions in late automatic processing of auditory change (late discriminative negativity). However, when utilizing time‐frequency analyses, we observed developmental increases in frontocentral signal strength (power) and consistency (inter‐trial phase synchrony) in delta and theta bands in response to novel sounds. Such frontocentral delta/theta responses have been linked in prior work to cognitive control. To further examine this possibility, we examined relations with inhibitory control. Results revealed that increased consistency in theta in response to novel sounds was related to improved inhibitory control. Together, our results advance our understanding of the development of attention in childhood. Moreover, they demonstrate the contributions of time‐frequency approaches to studying neurocognitive development. Finally, our results highlight the utility of neuroimaging paradigms that have low cognitive and motor demands to study the development of psychological processes. We examined age‐related differences in the auditory attention processes of change‐detection and orienting across childhood. ERP analyses revealed few age‐related changes. In contrast, time‐frequency analyses showed increases in frontocentral signal strength and consistency in delta and theta bands. Moreover, time‐frequency measures, but not ERPs, were related to improved inhibitory control, suggesting that these measures of auditory attention might be related to top‐down processes. Our findings demonstrate the contributions of time‐frequency approaches to studying neurocognitive development. [ABSTRACT FROM AUTHOR]

The causes of ventricular fibrillation (VF) are not yet elucidated, and it has been proposed that different mechanisms might exist. Moreover, conventional analysis methods do not seem to provide time or frequency domain features that allow for recognition of different VF patterns in electrode-recorded biopotentials. The present work aims to determine whether low-dimensional latent spaces could exhibit discriminative features for different mechanisms or conditions during VF episodes. For this purpose, manifold learning using autoencoder neural networks was analyzed based on surface ECG recordings. The recordings covered the onset of the VF episode as well as the next 6 min, and comprised an experimental database based on an animal model with five situations, including control, drug intervention (amiodarone, diltiazem, and flecainide), and autonomic nervous system blockade. The results show that latent spaces from unsupervised and supervised learning schemes yielded moderate though quite noticeable separability among the different types of VF according to their type or intervention. In particular, unsupervised schemes reached a multi-class classification accuracy of 66%, while supervised schemes improved the separability of the generated latent spaces, providing a classification accuracy of up to 74%. Thus, we conclude that manifold learning schemes can provide a valuable tool for studying different types of VF while working in low-dimensional latent spaces, as the machine-learning generated features exhibit separability among different VF types. This study confirms that latent variables are better VF descriptors than conventional time or domain features, making this technique useful in current VF research on elucidation of the underlying VF mechanisms. [ABSTRACT FROM AUTHOR]

Using the frequency-domain spillover index method, we investigate time-frequency spillovers and their underlying drivers among fossil energy, clean energy and metal markets. We find that short-term spillovers are stronger than long-term spillovers. Global clean energy markets are powerful spillover transmitters that can have strong impacts on fossil energy and metal markets. Rare earth metals are most vulnerable to spillover effects from clean energy and base metal markets, particularly in the long term. Different clean energy sources and metal markets have heterogeneous connectedness, e.g., the impact of wind energy on rare earth market is greater than that of solar energy. The short-term spillovers are mainly driven by policy changes, while the long-term spillovers are mainly affected by stock market uncertainty and economic fundamentals. Our findings have important implications for the construction of optimal diversification strategies and the design of policy incentives to promote clean energy investments across different time horizons. [ABSTRACT FROM AUTHOR]

The error-related negativity (ERN) reflects individual differences in error monitoring. However, findings on the ERN in adult and adolescent depression have been inconsistent. Analyzing electroencephalographic (EEG) data in both the time- and time-frequency domain can be useful to better quantify neural response to errors. The present study aimed at examining electrocortical measures of error monitoring in early adolescents with and without depression. EEG activity was collected during an arrowhead version of the flanker task in 29 (25 females) early adolescents with depression and 34 without MDD (29 females). The depression group showed reduced ERN amplitude, reduced error-related theta power and increased error-related beta power compared to the control group. When all variables that related to MDD diagnosis were considered simultaneously, both theta and beta power, but not the ERN, were independently related to an increased likelihood of being diagnosed with depression. By examining both time-domain and separate time-frequency measures, the present study provided novel evidence on error monitoring alterations in youth depression, suggesting that depression during adolescence may be characterized by reduced error monitoring (i.e., reduced ERN and error-related theta) and post-error inhibition (i.e., greater error-related beta power). These results support that time-frequency measures might be better suited for examining error-related neural activity in MDD relative to time-domain measures. • Findings on the ERN in adult and adolescent depression have been inconsistent. • EEG was recorded during a flanker task in early adolescents with and without MDD. • EEG data were analyzed in the time- and time-frequency domains. • MDD showed reduced ERN amplitude and error-related theta power and increased error-related beta power compared to the control group. • MDD is characterized by reduced error monitoring and post-error inhibition. [ABSTRACT FROM AUTHOR]

Vibration monitoring of CNC high-speed machining (HSM) centers under non-stationary conditions, characterized by varying operating parameters and uncertainties affected by the change of speed and load during operation currently presents a particular challenge. Therefore, bearing condition monitoring is important. Indeed, this variation has a considerable impact on the vibratory response delivered by the accelerometers and therefore can mask any fault. The change in speed causes considerable changes in the spectrum of the vibration such that defect signatures become almost undetectable with conventional tools. The order tracking method based on time–frequency representation is regarded as an effective tool for fault detection of bearings with varying rotating speeds. This study aims to propose non-stationary tools based on tachometer order tracking to detect bearing faults in high-speed milling centers during run-up and coast-down conditions. Developed tools are compared to stationary technics in this study, remaining limited to detect faults. Indeed, the speed variation would cause spectrum smearing if classic tools are used in non-stationary conditions. These latter methods are based on constant rotating speed and would fail to detect faults of bearings with variable spindle rotating speeds. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of disentangling nonoverlapping multicomponent signals from their observation being possibly contaminated by external additive noise. We aim to extract and to retrieve the elementary components (also called modes) present in an observed nonstationary mixture signal. To this end, we propose a new pseudo-Bayesian algorithm to perform the estimation of the instantaneous frequency of the signal modes from their time-frequency representation. In a second time, a detection algorithm is developed to restrict the time region where each signal component behaves, to enhance quality of the reconstructed signal. We finally deal with the presence of noise in the vicinity of the estimated instantaneous frequency by introducing a new reconstruction approach relying on nonbinary band-pass synthesis filters. We validate our methods by comparing their reconstruction performance to state-of-the-art approaches through several experiments involving both synthetic and real-world data under different experimental conditions. [ABSTRACT FROM AUTHOR]

This paper explores the utilization of time–frequency representations in seismic signal analysis to extract valuable insights from seismic signals. The study highlights the importance of high-resolution time–frequency representations in extracting crucial information from seismic data. Specifically, the research focuses on the low-frequency shadow as a seismic attribute, aiding in the identification of gas reservoirs. The low-frequency shadow is characterized by a decrease in frequency and an increase in amplitude near the base of a gas layer in a seismic section. Acknowledging the limitations of conventional methods such as short-time Fourier transform and high-resolution techniques such as synchrosqueezing transform in accurately representing non-stationary seismic signals in time–frequency representation, this paper introduces a novel high-resolution method named as modular high-resolution time–frequency representation. This innovative approach excels in analyzing non-stationary signals, accurately detecting the low-frequency shadow, and signal reconstruction. To demonstrate the effectiveness of the proposed method, the research outlines its algorithm and applies it for comparison with other methods using synthetic seismic data. The results showcase its ability to generate sparse time–frequency maps compared to conventional methods. Furthermore, the study evaluates the effectiveness of the proposed method for low-frequency shadow detection in real seismic data by applying it to 2D seismic data from a gas field in southwestern Iran. The findings emphasize its significant potential for accurately detecting low-frequency shadow locations. [ABSTRACT FROM AUTHOR]

Time–frequency distributions (TFD) can provide a great set of tools for studying transient signals. Although TFD can overcome constraints on signal representation, the most widely used TFDs produce artifacts known as cross terms, which could pose a challenge when used on real-world signals. The paper proposes a sparse Time–Frequency Distribution (TFD) reconstruction method employing a gradient slime Renyi entropy shrinkage model. Each time–frequency slice within the TFD undergoes shrinkage utilizing distinct algorithms based on Renyi entropy, which accounts for both short-term and narrowband characteristics. Renyi entropy quantifies data presence in the time–frequency plane. By integrating the Renyi entropy-based shrinkage operator, the traditional hard threshold operator in the shrinkage process is replaced, enhancing TFD resolution. The reconstruction model parameters are fine-tuned using a gradient slime shape optimizer, employing a concentration minimization function and Mean Squared Error (MSE) metrics between initial and reconstructed TFD modules for optimization. The simulation results prove that the proposed method achieved a reduced MSE value of 1.95 as compared with other existing methods. [ABSTRACT FROM AUTHOR]

Hoarding disorder (HD) and obsessive-compulsive disorder (OCD) are highly comorbid and genetically related, but their similarities and differences at the neural level are not well characterized. The present study examined the time-frequency information contained in stimulus-related EEG data as participants worked on a visual flanker task. Three groups were included: participants diagnosed with HD (N = 33), OCD (N = 26), and healthy controls (N = 35). Permutation-controlled mass-univariate analyses found no differences between groups in terms of the magnitude of the oscillatory responses. Differences between groups were found selectively for phase-based measures (phase-locking across trials and across sensors) in time ranges well after those consistent with initial visuocortical processes, in the alpha (10 Hz) as well as theta and beta frequency bands, centered around 6 Hz and 15 Hz, respectively. Specifically, HD showed attenuated phase locking in theta and alpha compared to OCD and HC, while OCD showed heightened inter-site phase locking in alpha/beta. Including age as a covariate attenuated, but did not eliminate, the group differences. These findings point to signatures of cortical dynamics and cortical communication task processing that are unique to HD, and which are specifically present during higher-order visual cognition such as stimulus-response mapping, response selection, and action monitoring. • Neural characteristics of hoarding disorder and obsessive-compulsive disorder can be assessed using electroencephalography. • Phase locking (PLI) assesses temporal similarity of responses within a population of neurons. • Intersite phase locking (ISPL) assesses synchrony of neural communication between populations of neurons. • Hoarding disorder participants show attenuated PLI and ISPL compared to obsessive compulsive disorder and controls. • These findings suggest a lack of temporal organization of the neural activity in hoarding disorder. [ABSTRACT FROM AUTHOR]

Music training was shown to induce changes in auditory processing in older adults. However, most findings stem from correlational studies and fewer examine long-term sustainable benefits. Moreover, research shows small and variable changes in auditory event-related potential (ERP) amplitudes and/or latencies in older adults. Conventional time domain analysis methods, however, are susceptible to latency jitter in evoked responses and may miss important information of brain processing. Here, we used time-frequency analyses to examine training-related changes in auditory-evoked oscillatory activity in healthy older adults (N = 50) assigned to a music training (n = 16), visual art training (n = 17), or a no-treatment control (n = 17) group. All three groups were presented with oddball auditory paradigms with synthesized piano tones or vowels during the acquisition of high-density EEG. Neurophysiological measures were collected at three-time points: pre-training, post-training, and at a three-month follow-up. Training programs were administered for 12-weeks. Increased theta power was found pre and post- training for the music (p = 0.010) and visual art group (p = 0.010) as compared to controls (p = 0.776) and maintained at the three-month follow-up. Results showed training-related plasticity on auditory processing in aging adults. Neuroplastic changes were maintained three months post-training, suggesting music and visual art programs yield lasting benefits that might facilitate encoding, retention, and memory retrieval. [ABSTRACT FROM AUTHOR]

• Complex interlimb movements are most affected in older adults. • Elevated beta desynchronization in older adults seems to reflect a compensatory mechanism. • Large-scale alpha functional connectivity is lower in older versus younger adults. • Large-scale theta connectivity increases with task complexity, irrespective of age. Interlimb coordination deteriorates as a result of aging. Due to its ubiquity in daily life, a greater understanding of the underlying neurophysiological changes is required. Here, we combined electroencephalography time–frequency spectral power and functional connectivity analyses to provide a comprehensive overview of the neural dynamics underlying the age-related deterioration of interlimb coordination involving all four limbs. Theta, alpha and beta oscillations in the frontal, central and parietal regions were analyzed in twenty younger (18–30 years) and nineteen older adults (65–78 years) during a complex interlimb reaction time task. Reaction time was significantly higher in older adults across all conditions, and the discrepancy between both age groups was largest in the most complex movement condition. Older adults demonstrated enhanced beta event-related desynchronization (i.e., the attenuation of beta power), which further increased along with task complexity and was positively linked to behavioral performance. Theta functional connectivity between frontal, central and parietal regions generally increased with movement complexity, irrespective of age group. In general, frontoparietal alpha band functional connectivity tended to be reduced in older versus younger adults, although these contrasts did not survive multiple comparison corrections. Overall, spectral results suggest that enhanced beta desynchronization in older adults reflects a successful compensatory mechanism to cope with increased difficulty during complex interlimb coordination. Functional connectivity results suggest that theta and alpha band connectivity are prone to respectively task- and age-related modulations. Future work could target these spectral and functional connectivity dynamics through noninvasive brain stimulation to potentially improve interlimb coordination in older adults. [ABSTRACT FROM AUTHOR]

In this paper, time-frequency distributions (TFDs) and their hardware implementation on FPGA are presented. TFDs are evolved due to disadvantage of Fourier Transform (FT), which cannot provide time information in spectrum representation. Time-Frequency Representations (TFRs) are helpful in providing simultaneous information about spectral contents of a signal with respect to time period axis. The major problem associated with hardware implementation of TFDs is limited on-board memory. Forward and backward register allocation method (FBRA) is employed to obtain the optimum register occupation. A register of length 32-bit is considered for the input signal representation. The stored register values are applied to the proposed TFDs and computed using real-time hardware. FBRA is implemented during the computation of FFT in all TFDs. All the transforms are modeled using Verilog code and implemented on SPARTA-6 FPGA. A real-time ECG, earthquake and a quad chirp signals are taken as input to test the designed TFDs. Finally, a comparison of different hardware resources utilized on FPGA with earlier conventional methods for better real-time applications was made. [ABSTRACT FROM AUTHOR]

Rotor fault is a condition that may occur in induction motors due to high forces and stresses on rotor bars or end rings. When this occurs, the machine may lead to a forced outage in production lines. Several methods have been proposed in the literature for the detection of rotor faults; however, they cannot work under variable frequency conditions, and they are very sensitive to mechanical load oscillations. This paper presents a novel frequency normalization methodology for reliable rotor defect diagnosis in variable-frequency drive-fed systems. The normalization is implemented by demodulating the stator current signal with a time-variant factor. As a result, the fundamental component of the stator current is normalized in a constant frequency value, whereas avoiding spectral leakage and frequency smearing. This enables the transient stator current signature analysis to accurately locate targeted fault signatures. The effectiveness of the proposed frequency normalization approach is demonstrated by analyzing the stator current of motors under non-stationary conditions. Experimental results show that the proposed methodology can detect and distinguish between load oscillations and rotor fault signatures for avoiding false rotor fault alarms. [ABSTRACT FROM AUTHOR]

As an extension of Gabor signal processing, the covariant Weyl-Heisenberg integral quantization is implemented to transform functions on the eight-dimensional phase space x , k into Hilbertian operators. The x = x μ values are space-time variables, and the k = k μ values are their conjugate frequency-wave vector variables. The procedure is first applied to the variables x , k and produces essentially canonically conjugate self-adjoint operators. It is next applied to the metric field g μ ν (x) of general relativity and yields regularized semi-classical phase space portraits g ˇ μ ν (x) . The latter give rise to modified tensor energy density. Examples are given with the uniformly accelerated reference system and the Schwarzschild metric. Interesting probabilistic aspects are discussed. [ABSTRACT FROM AUTHOR]

Previous studies have associated attention-deficit/hyperactivity disorder (ADHD) with several alterations in electroencephalographic (EEG) activity. Time-frequency analyses capturing event-related power modulations are becoming an increasingly popular approach, but a systematic synthesis of the time-frequency literature in ADHD is currently lacking. We conducted the first systematic review and meta-analysis of time-frequency studies of children and adults with ADHD in comparison to neurotypical controls. Searches via Medline, Embase, and Web of Science, as well as reference lists, identified 28 eligible articles published until March 2021. Of these, 13 articles with relevant data were included in a multi-level meta-analysis. Most studies examined power modulations of alpha, theta and/or beta frequencies (N = 21/28), and focused on children (N = 17/28). Meta-analyses showed significantly weaker theta increases (Cohen's d = −0.25, p = 0.039; N ADHD = 346, N CONTROL = 327), alpha decreases (d = 0.44, p < 0.001; N ADHD = 564, N CONTROL = 450), and beta increases (Cohen's d = −0.33, p < 0.001; N ADHD = 222, N CONTROL = 263) in individuals with ADHD relative to controls. These patterns indicate broad brain-oscillatory alterations in individuals with ADHD with small (theta) and small-to-moderate (alpha and beta) effect sizes. These group differences were partly consistent when repeating analyses by age group (<18 and 18+ years) and task type (cognitive control, working memory, and simple attention tasks). Overall, our findings identify widespread event-related brain-oscillatory alterations in individuals with ADHD during a range of neurocognitive functions. Future research requires larger samples, a broader range of frequency bands (including delta and gamma) during a wider type of cognitive-affective processes, and should clarify whether atypical event-related power profiles are ADHD-specific or shared with other neuropsychiatric conditions. • First systematic review and meta-analysis of time-frequency EEG studies in ADHD • Individuals with ADHD showed weaker alpha power decreases than controls. • Individuals with ADHD showed weaker theta and beta power increases than controls. • Effects sizes were small (theta) and small-to-moderate (alpha and beta). • Findings show widespread event-related EEG power alterations in ADHD. [ABSTRACT FROM AUTHOR]

Instantaneous frequency in multi-sensor recordings is an important parameter for estimation of direction of arrival estimation, source separation, and sparse reconstruction. The instantaneous frequency estimation problem becomes challenging when signal components have close or overlapping signatures and the number of sensors is less than the number of sources. In this study, we develop a computationally efficient method that exploits the direction of the IF curve in addition to the angle of arrival as additional features for the accurate tracking of IF curves. Experimental results show that the proposed scheme achieves better accuracy compared to the-state-of-art method in terms of mean square error (MSE) with a slight increase in the computational cost, i.e., the proposed method achieves MSE of −50 dB at the signal to noise ratio of 0 dB whereas the existing method achieves the MSE of −38 dB. [ABSTRACT FROM AUTHOR]

Speech perception involves segmenting a continuous stream of speech into its word components. This can be challenging in the case of homophonous utterances only differing in non-contrastive subphonemic features. Yet, the speech perception system seems able to discriminate subphonemic deviation in homophonous utterances, since it has been shown to elicit a mismatch response (MMN). Here, we focused on the oscillatory correlates, namely phase resetting and power, of non-contrastive subphonemic deviation processing in language. An oddball task that considered natural intraspeaker variability was used. Subphonemic deviance elicited intertrial phase coherence (ITC) differences in the theta band at Fz during the time window of the MMN. No differences in power were found. This suggests that the processing of subphonemic deviation in speech signals, reflected by the MMN, might rely on mechanisms of phase resetting. ITC might facilitate the synchronous firing of functional networks involved in the processing of subphonemic deviance. [ABSTRACT FROM AUTHOR]

Purpose: This study aims to establish the friction vibration model. Design/methodology/approach: The friction vibration experiment was carried out on a pin disk friction tester. The causes of friction vibration are discussed, and the friction vibration model is established based on the energy method. Findings: The experimental and simulation results show that the main cause of friction vibration is the nonlinear change of friction coefficient; degree of the friction vibration has a positive relationship with the friction relative velocity and normal contact positive pressure; the proposed friction vibration model is highly consistent in chaotic attractor and time-frequency distribution map and can well predict friction vibration. Originality/value: The proposed friction vibration model is highly consistent in chaotic attractor and time-frequency distribution map and can well predict friction vibration. [ABSTRACT FROM AUTHOR]

Chatter will cause a series of changes in machining state during the boring process. Due to the difference in the sensitivity and ability of various sensors to resist external interference, the change of machining environment can lead to variation in response and make difference to the characteristics of signal. Therefore, it is essential to extract the features related to chatter from the machining signals. In this study, the dynamic model considering the regenerative effect is constructed, based on which the relationship between the limited cutting width and the spindle speed is determined. In addition, eddy current displacement sensor, vibration acceleration sensor, and acoustic pressure sensor are applied to propose a feasible boring chatter monitoring scheme and collect the machining signals. The time and frequency domain of the collected signals are analyzed to determine the pattern of time–frequency variation for three kinds of sensor signals generated with chatter. [ABSTRACT FROM AUTHOR]

• Human participants with SYNGAP1 mutations expressed less synchronization when processing an auditory stimulus. • The Down syndrome group showed maintained synchronization. • More gamma and beta power was found in subjects with SYNGAP1 mutations. Altered sensory processing is common in intellectual disability (ID). Here, we study electroencephalographic responses to auditory stimulation in human subjects presenting a rare condition (mutations in SYNGAP1) which causes ID, epilepsy and autism. Auditory evoked potentials, time-frequency and inter-trial coherence analyses were used to compare subjects with SYNGAP1 mutations with Down syndrome (DS) and neurotypical (NT) participants (N = 61 ranging from three to 19 years of age). Altered synchronization in the brain responses to sound were found in both ID groups. The SYNGAP1 mutations group showed less phase-locking in early time windows and lower frequency bands compared to NT, and in later time windows compared to NT and DS. Time-frequency analysis showed more power in beta-gamma in the SYNGAP1 group compared to NT participants. This study indicated reduced synchronization as well as more high frequencies power in SYNGAP1 mutations, while maintained synchronization was found in the DS group. These results might reflect dysfunctional sensory information processing caused by excitation/inhibition imbalance, or an imperfect compensatory mechanism in SYNGAP1 mutations individuals. Our study is the first to reveal brain response abnormalities in auditory sensory processing in SYNGAP1 mutations individuals, that are distinct from DS, another ID condition. [ABSTRACT FROM AUTHOR]

Impaired cognitive control has been associated with the occurrence of attentional errors in those with schizophrenia. However, the extent of altered proactive or reactive control underlying such errors is still unknown. Twenty-two patients with schizophrenia and 21 healthy matched controls performed a detection task (i.e., the continuous temporal expectancy task). Electrophysiological measures of proactive and reactive control were based on two periods of interest: during the target presentation (the critical window) and four trials before the critical window. Regarding the proactive mode, patients with schizophrenia exhibited a specific decrease in frontal midline theta power during the critical window before a miss compared to a correct detection. In contrast, the contingent negative variation amplitude was altered regardless of the response type, four trials before the critical window. Regarding the reactive mode, a reduced P3 amplitude was revealed later before a miss than a correct detection with differences apparent only two trials before the critical window in patients with schizophrenia, whereas it was observable up to four trials prior in healthy controls. Moreover, only the P3 amplitude reduction in patients with schizophrenia predicted the miss rate and was anti-correlated with the clinical symptoms. Thus, our results revealed a specific impairment of the proactive goal-updating process before an error and an altered implementation of the endogenous proactive mode engagement regardless of the response type. The results also highlighted the strong relationship between the disrupted reactive mode and the increased rate of attentional errors and severity of the clinical symptoms of schizophrenia. [ABSTRACT FROM AUTHOR]

A widely held though debatable claim is that the picture of an object like a frying pan automatically elicits features of a left/right-handed grasp action even in perceptual tasks that make no demands on the observer to consider the graspable properties of the depicted object. Here, we sought to further elucidate this claim by relying on a methodology that allowed us to distinguish between the influence of motor versus spatial codes on the selection of a left/right-handed response while electroencephalographic data were recorded. In our experiment, participants classified images of frying pans as upright or inverted using a left/right key press or by making a left/right-handed reach-and-grasp action towards a centrally located response element while we recorded electroencephalographic (EEG) data. In line with previous evidence (Bub, Masson, & van Noordenne, Journal of Experiment Psychology: Human Perception and Performance, 47(1), 53–80, 2021), these two modes of responding generated distinct correspondence effects on performance induced by the same set of images. In terms of our EEG data, we found that neither motor (the lateralized readiness potential) nor visual (N100 and P100) potentials were sensitive to handle-response hand correspondence. However, an exploratory theta analysis revealed that changes in frontal theta power mirrored the different correspondence effects evoked by the image on key press responses versus reach and grasp actions. Importantly, our results provide a link between these disparate effects and the engagement of cognitive control, highlighting a possible role of top-down control processes in separating motor features from the task-irrelevant features of an object, and thus in claims regarding object affordances more generally. [ABSTRACT FROM AUTHOR]

Finding the best K-sparse approximation of a signal in a redundant dictionary is an NP-hard problem. Suboptimal greedy matching pursuit algorithms are generally used for this task. In this work, we present an acceleration technique and an implementation of the matching pursuit algorithm acting on a multi-Gabor dictionary, i.e., a concatenation of several Gabor-type time-frequency dictionaries, each of which consists of translations and modulations of a possibly different window and time and frequency shift parameters. The technique is based on pre-computing and thresholding inner products between atoms and on updating the residual directly in the coefficient domain, i.e., without the round-trip to the signal domain. Since the proposed acceleration technique involves an approximate update step, we provide theoretical and experimental results illustrating the convergence of the resulting algorithm. The implementation is written in C (compatible with C99 and C++11), and we also provide Matlab and GNU Octave interfaces. For some settings, the implementation is up to 70 times faster than the standard Matching Pursuit Toolkit. [ABSTRACT FROM AUTHOR]

Relatively little is known about the relation between subthreshold error corrections and post‐error behavioral compensations. The present study utilized lateralized beta power, which has been shown to index response preparation, to examine subthreshold error corrections in a task known to produce response conflict, the Simon task. We found that even when an overt correction is not made, greater activation of the corrective response, indexed by beta suppression ipsilateral to the initial responding hand, predicted post‐error speeding, and enhanced post‐error accuracy at the single‐trial level. This provides support for the notion that response conflict associated with errors can be adaptive, and suggests that subthreshold corrections should be taken into account to fully understand error‐monitoring processes. Furthermore, we expand on previous findings that demonstrate that post‐error slowing and post‐error accuracy can be dissociated, as well as findings that suggest that frontal midline theta oscillations and the error‐related negativity (ERN) are dissociable neurocognitive processes. Impact Statement: A lack of consensus on whether error‐monitoring processes facilitate adaptive post‐error behavior might be attributable to the possibility that most investigations do not consider subthreshold error corrections. Here, we provide evidence—using a novel neural index—that even in the absence of an overt correction, subthreshold error corrections are adaptive. [ABSTRACT FROM AUTHOR]

The ideal time-frequency (TF) representation which distributes the total energy along the instantaneous frequency (IF) of a signal is essentially sparse. Motivated by the weighted sparse representation of the signal, we propose the ridge-aware weighted sparse TF representation (RWSTF) which involves some properties an ideal TF representation should satisfy, such as, highly concentrated TF representation, the signal reconstruction and acceptable computational cost. Based on a basic sparse TF model, we firstly use a weighted strategy to effectively highlight the TF ridges even for the weak components, then fast iterative shrinkage thresholding algorithm (FISTA) is applied to obtain an efficient numerical approximation for solving the model. Furthermore, we introduce a k-sparsity strategy for the adaptive selection of the regularization parameter. A simulation study shows that the proposed method not only has higher energy concentration, but also performs better on signal denoising than other standard TF approaches, especially for the signals with fast varying IF. Two real life examples confirm the potential of the proposed method. [ABSTRACT FROM AUTHOR]

The decomposition of multi‐component micro‐Doppler signals overlapping in the time‐frequency (T‐F) domain is critical and challenging, especially in the case of irregular instantaneous Doppler frequency. The authors propose a novel time‐based decomposition method called the short‐time variational mode decomposition (STVMD) to analyse the irregular (FM) micro‐Doppler signals, and present an optimal model combined with T‐F transformation. Then, considering the STVMD may fail to extract the instantaneous frequency (IF) of overlapped components, an improved STVMD algorithm is put forward. Since the dependence of the STVMD algorithm on the initial value, they adopt the Kalman filtering to implement IF tracking and regrouping under the global constraint, further accelerating the convergence of the algorithm. Furthermore, due to the mode aliasing at the intersection point, they adopt a degenerate STVMD model to decompose the signals with known centre frequencies, which can be viewed as a Wiener filter. With the two steps, the improved STVMD algorithm can effectively solve the decomposition of T‐F overlapping irregular FM micro‐Doppler signals. Compared with the peak ridge technique and the ridge path regrouping and intrinsic chirp component decomposition (RPRG + ICCD), the proposed method shows the effectiveness and adaptability even for irregular FM signals with large T‐F spectrum amplitude fluctuation in the low signal‐to‐noise ratio environment. [ABSTRACT FROM AUTHOR]

• Speech characteristics are consistent with the auditory properties of the human ear. • Time-frequency characteristics reflect the variation of the signal in the time–frequency domain. • Deep residual networks are capable of learning features. • Multi-feature decision fusion methods for accurate target identification. Deep learning methods can recognize the various underwater acoustic targets in complex marine environments. In this study, we apply deep learning methods in decision recognition of underwater acoustic targets. However, decision recognition methods rely on a complex evidence theory that combines the probabilities of various scenarios as evidence sources, and in the process of combination produces conflicting evidence. For this reason, we propose an underwater acoustic target recognition method based on weighted average Dempster -Shafer (WA-DS) decision fusion, which replaces anomalous evidence with weighted average evidence, and solves the problem of synthesizing anomalous evidence. Firstly, the underwater acoustic dataset is preprocessed with downsampling, denoising, and audio segmentation. Then, the Mel-frequency cepstral coefficients (MFCC), Gammatone frequency cepstral coefficients (GFCC), and time–frequency (TF) features are extracted, and the category probabilities are output using the ResNet18 network to learn and train the multi-feature and then output the category probabilities. Finally, the category probabilities of multi-feature are computed as evidence using the Basic Probability Assignment (BPA) function, and the various types of evidence are fused using our decision fusion algorithm. Experimental results show that our decision fusion method is more capable of synthesizing anomalous evidence than traditional decision fusion methods. Our work achieves recognition accuracy of up to 98.34% on the ShipsEar dataset, which is a significant improvement in recognition rate compared to single features. [ABSTRACT FROM AUTHOR]

The maintenance of an intention in memory (Prospective Memory, PM) while performing a task is associated with a cost in terms of both performance (longer response times and lower accuracy) and neurophysiological modulations, which extent depends on several features of the stimuli. This study explores the neural patterns associated with PM in different sensory modalities, to identify differences depending on this variable and discuss their functional meaning. Data were collected using a High-Density EEG during a baseline and a PM condition, proposed in a visual and an auditory version. Theta and alpha oscillations were compared between the two conditions within each modality using a cluster-based permutation approach. PM conditions were associated with clusters of decreased alpha and theta activity in both modalities. However, different spatiotemporal dynamics were elicited as a function of sensory modality: alpha decreases displayed an overlapping onset between modalities, but different durations, lasting longer in the auditory modality. Conversely, the clusters of decreased theta activity presented similar durations between modalities, but different temporal and spatial onsets, appearing at different moments over the respective sensory areas. The similar spatiotemporal properties of alpha suppression between modalities indicate that such oscillations may represent a supramodal, top-down process, presumably reflecting the external direction of attention to successfully detect the prospective cue (strategic monitoring). In theta, the clusters showed more modality-specific differences, which temporal and spatial properties correspond to the ones necessary to perform the ongoing task, suggesting a shift in resource allocation in favor of the PM task. • Prospective Memory (PM) tasks lead to clusters of decreased activity in the alpha and theta bands. • Behavioral and neurophysiological "PM costs" are greater for auditory stimuli. • In alpha, clusters present similar topographies between sensory modalities. • Alpha clusters seem to reflect supramodal, top-down processes (strategic monitoring). • Theta clusters differ between modalities, reflecting bottom-up processes. [ABSTRACT FROM AUTHOR]

Depressed mood has been proposed to possibly possess a unique mode of defocused attention. However, this argument needs to be supported by experimental evidence based on attentional performance. The present study used a perceptual load paradigm, combining factors of perceptual load, distractor-target compatibility, and eccentricity, to investigate the degree of attentional distraction in depressed mood. In addition, the mode of attentional distraction associated with depressed mood was explored with the time-frequency features of electroencephalography (EEG). The behavioral results showed that the high depressed mood (HD) group had significantly higher attentional distraction than the low depressed mood (LD) group. EEG results showed that 1) the beta power (especially beta-2, 18–30 Hz) of the two groups differed in the medio-late part of the attentional distraction, with significantly lower power in the HD group than in the LD group; 2) the results of the correlation between beta-2 power and depression scores revealed a significant negative correlation. These results imply that beta-2 is a potential marker that may be sensitive to depressed mood during attentional processing, which was further supported by the classification results of the support vector machine (SVM) with 80.65% accuracy between the HD and LD groups. • HD group have greater attentional distraction in behavior than the LD group. • Attentional distraction differs between the HD and LD groups in EEG analysis. • The beta band may serve as a marker associated with depressed mood. [ABSTRACT FROM AUTHOR]

We present a novel family of continuous, linear time-frequency transforms adaptable to a multitude of (nonlinear) frequency scales. Similar to classical time-frequency or time-scale representations, the representation coefficients are obtained as inner products with the elements of a continuously indexed family of time-frequency atoms. These atoms are obtained from a single prototype function, by means of modulation, translation and warping. By warping we refer to the process of nonlinear evaluation according to a bijective, increasing function, the warping function. Besides showing that the resulting integral transforms fulfill certain basic, but essential properties, such as continuity and invertibility, we will show that a large subclass of warping functions gives rise to families of generalized coorbit spaces, i.e. Banach spaces of functions whose representations possess a certain localization. Furthermore, we obtain sufficient conditions for subsampled warped time-frequency systems to form atomic decompositions and Banach frames. To this end, we extend results previously presented by Fornasier and Rauhut to a larger class of function systems via a simple, but crucial modification. The proposed method allows for great flexibility, but by choosing particular warping functions Φ we also recover classical time-frequency representations, e.g. Φ (t) = c t provides the short-time Fourier transform and Φ (t) = log a ⁡ (t) provides wavelet transforms. This is illustrated by a number of examples provided in the manuscript. [ABSTRACT FROM AUTHOR]

Recent studies show that pre-stimulus band-specific power and phase in the electroencephalogram (EEG) can predict accuracy on tasks involving the detection of near-threshold stimuli. However, results in the auditory modality have been mixed, and few works have examined pre-stimulus features when more complex decisions are made (e.g. identifying supra-threshold sounds). Further, most auditory studies have used background sounds known to induce oscillatory EEG states, leaving it unclear whether phase predicts accuracy without such background sounds. To address this gap in knowledge, the present study examined pre-stimulus EEG as it relates to accuracy in a tone pattern identification task. On each trial, participants heard a triad of 40-ms sinusoidal tones (separated by 40-ms intervals), one of which was at a different frequency than the other two. Participants' task was to indicate the tone pattern (low-low-high, low-high-low, etc.). No background sounds were employed. Using a phase opposition measure based on inter-trial phase consistencies, pre-stimulus 7–10 Hz phase was found to differ between correct and incorrect trials ∼200 to 100 ms prior to tone-pattern onset. After sorting trials into bins based on phase, accuracy was found to be lowest at around π − + relative to individuals' most accurate phase bin. No significant effects were found for pre-stimulus power. In the context of the literature, findings suggest an important relationship between the complexity of task demands and pre-stimulus activity within the auditory domain. Results also raise interesting questions about the role of induced oscillatory states or rhythmic processing modes in obtaining pre-stimulus effects of phase in auditory tasks. • Ongoing EEG phase predicted accuracy in pattern identification. • Pre-stimulus EEG phase was a better predictor of performance than power. • Task complexity may modulate pre-stimulus phase effects on auditory perception. [ABSTRACT FROM AUTHOR]

Incidental learning allows us to extract statistical relations between events in our daily lives without the intention to learn them. Whereas anticipation during intentional associative learning has been linked to increased and decreased theta band activity, comparatively little research has focused on incidental learning. The study of such a pervasive mechanism of incidental learning faces the challenge of finding an appropriate paradigm. Similarly, while posterior alpha band activity has been shown to facilitate attention to a predictable target location, it is not clear whether alpha power could mediate attention given other predictive information; e.g., when the only available information provided by the cue is the likelihood of the target outcome. Here we used a stimulus-stimulus associative learning task to investigate whether a cue carries information on its contingent relationship with a target outcome, not only when their relationship is learned intentionally but also when it could be learned incidentally. Moreover, by presenting the target outcome in a visual search task, we were also able to study whether anticipatory attention can be modulated by the intentional or the incidental knowledge of the likelihood of a target outcome given a predictive (or non-predictive) cue. Participants were exposed to streams of cue-target outcome trials, where one of two possible cues and one of two possible outcomes were displayed. Intention to learn was manipulated by asking participants to assess whether one of the target outcomes (the intentional one) was more likely to appear following one of the cues (the intentional one). Any learning regarding the other cue-outcome relationship would be incidental. We found that frontal and temporal theta band activity were sensitive to the predictive value of a cue (predictive cues elicited lower theta power). Moreover, left temporal theta was sensitive to the intention to learn associations (theta activity elicited by intentional learning cues was higher). Alpha power, by contrast, was not modulated by cue predictiveness of the target outcome. These findings suggest that theta band activity carries information about the predictive value of a cue. The topographical differences between theta for intentional and incidental learning suggest distinct cortical networks activated depending on whether the relationship between a cue and an outcome has been learned intentionally or incidentally. • Cue predictiveness of the likelihood of upcoming events modulated theta band activity. • This effect characterized both intentional and incidental learning of stimulus-stimulus associations but with different topography. • No differences were found in preparatory alpha band activity to predictable upcoming target outcomes. [ABSTRACT FROM AUTHOR]