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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 taus 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.

Nowadays studies using Virtual Reality (VR) are gaining high popularity due to VR being a better approximation of the ecological environment for visual experiments than standard 2D display settings. VR technology has been already applied in medicine in the therapy of mental disorders, neurorehabilitation, and neurofeedback. However, its effectiveness compared to the standard 2D procedure is still not fully documented and limited information about the neurophysiological underpinnings of VR is provided. In this study, we tested participants' performance during several sessions of the computer game in two different environments, VR vs. 2D monitor display. Participants performed three 25 min gaming sessions of adapted Delay Match-To-Sample task during EEG recording. The results showed that the VR group outperformed the 2D display group in the first session and then maintained its performance level throughout the remaining two sessions while the 2D group increased performance in each session eventually leveling up in the last one. Also group differences in the EEG activity were most profound only in the first session. In this session, the VR group was characterized by stronger and more synchronized neuronal activity, especially in delta, theta, and gamma bands. The VR group was less impacted by visual arousals as indicated by the theta/beta2 ratio in parietal electrodes. [ABSTRACT FROM AUTHOR]

Introduction: We discuss event-related power differences (ERPDs) in low- and broadband-γ oscillations as the embedded-clause edge is processed in wh-dependencies such as Which decision regarding/about him/her did Paul say that Lydie rejected without hesitation? in first (L1) and second language (L2) French speakers. Methods: The experimental conditions manipulated whether pronouns appeared in modifiers (Mods; regarding him/her) or in noun complements (Comps; about him/ her) and whether they matched or mismatched a matrix-clause subject in gender. Results: Across L1 and L2 speakers, we found that anaphora-linked ERPDs for Mods vs. Comps in evoked power first arose in low γ and then in broadband γ. Referential elements first seem to be retrieved from working memory by narrowband processes in low γ and then referential identification seems to be computed in broadband-γ output. Interactions between discourse- and syntax-based referential processes for the Mods vs. Comps in these ERPDs furthermore suggest that multidomain γ-band processing enables a range of elementary operations for discourse and semantic interpretation. Discussion: We argue that a multidomain mechanism enabling operations conditioned by the syntactic and semantic nature of the elements processed interacts with local brain microcircuits representing features and feature sets that have been established in L1 or L2 acquisition, accounting for a single language epistemology across learning contexts. [ABSTRACT FROM AUTHOR]

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]

As they are the core components of the drive motor in electric vehicles, the accurate fault diagnosis of rolling bearings is the key to ensuring the safe operation of electric vehicles. At present, intelligent diagnostic methods based on current signals (CSs) are widely used owing to the advantages of the easy collection, low cost, and non-invasiveness of CSs. However, in practical applications, the fault characteristics of the CS are weak, resulting in diagnostic performance that fails to meet the expected standards. In this paper, a diagnosis method is proposed to address this problem and enhance the diagnosis accuracy. Firstly, CSs from two phases are processed by periodic resampling to enhance data features, which are then fused through splicing operations. Subsequently, a feature enhancement module is constructed using multi-scale feature fusion for decomposing the input. Finally, a diagnosis model is constructed by using an improved channel attention module (CAM) for enhancing the diagnosis performance. The results from experiments containing two different types of bearing datasets show that the proposed method can extract high-quality fault features and improve the diagnosis accuracy, presenting great potential in intelligent fault diagnosis and the maintenance of electric vehicles. [ABSTRACT FROM AUTHOR]

Nowadays studies using Virtual Reality (VR) are gaining high popularity due to VR being a better approximation of the ecological environment for visual experiments than standard 2D display settings. VR technology has been already applied in medicine in the therapy of mental disorders, neurorehabilitation, and neurofeedback. However, its effectiveness compared to the standard 2D procedure is still not fully documented and limited information about the neurophysiological underpinnings of VR is provided. In this study, we tested participants’ performance during several sessions of the computer game in two different environments, VR vs. 2D monitor display. Participants performed three 25 min gaming sessions of adapted Delay Match-To-Sample task during EEG recording. The results showed that the VR group outperformed the 2D display group in the first session and then maintained its performance level throughout the remaining two sessions while the 2D group increased performance in each session eventually leveling up in the last one. Also group differences in the EEG activity were most profound only in the first session. In this session, the VR group was characterized by stronger and more synchronized neuronal activity, especially in delta, theta, and gamma bands. The VR group was less impacted by visual arousals as indicated by the theta/beta2 ratio in parietal electrodes.

Abstract We used daily return series for three pairs of datasets from the crude oil markets (WTI and Brent), stock indices (the Dow Jones Industrial Average and S&P 500), and benchmark cryptocurrencies (Bitcoin and Ethereum) to examine the connections between various data during the COVID-19 pandemic. We consider two characteristics: time and frequency. Based on Diebold and Yilmaz’s (Int J Forecast 28:57–66, 2012) technique, our findings indicate that comparable data have a substantially stronger correlation (regarding return) than volatility. Per Baruník and Křehlík’ (J Financ Econ 16:271–296, 2018) approach, interconnectedness among returns (volatilities) reduces (increases) as one moves from the short to the long term. A moving window analysis reveals a sudden increase in correlation, both in volatility and return, during the COVID-19 pandemic. In the context of wavelet coherence analysis, we observe a strong interconnection between data corresponding to the COVID-19 outbreak. The only exceptions are the behavior of Bitcoin and Ethereum. Specifically, Bitcoin combinations with other data exhibit a distinct behavior. The period precisely coincides with the COVID-19 pandemic. Evidently, volatility spillover has a long-lasting impact; policymakers should thus employ the appropriate tools to mitigate the severity of the relevant shocks (e.g., the COVID-19 pandemic) and simultaneously reduce its side effects.

Abstract 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 f 1 (2–5 Hz) is the primary factor contributing to the amplification of the acceleration response in the rotating bridge, while f 3 (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.

We used daily return series for three pairs of datasets from the crude oil markets (WTI and Brent), stock indices (the Dow Jones Industrial Average and S&P 500), and benchmark cryptocurrencies (Bitcoin and Ethereum) to examine the connections between various data during the COVID-19 pandemic. We consider two characteristics: time and frequency. Based on Diebold and Yilmaz's (Int J Forecast 28:57–66, 2012) technique, our findings indicate that comparable data have a substantially stronger correlation (regarding return) than volatility. Per Baruník and Křehlík' (J Financ Econ 16:271–296, 2018) approach, interconnectedness among returns (volatilities) reduces (increases) as one moves from the short to the long term. A moving window analysis reveals a sudden increase in correlation, both in volatility and return, during the COVID-19 pandemic. In the context of wavelet coherence analysis, we observe a strong interconnection between data corresponding to the COVID-19 outbreak. The only exceptions are the behavior of Bitcoin and Ethereum. Specifically, Bitcoin combinations with other data exhibit a distinct behavior. The period precisely coincides with the COVID-19 pandemic. Evidently, volatility spillover has a long-lasting impact; policymakers should thus employ the appropriate tools to mitigate the severity of the relevant shocks (e.g., the COVID-19 pandemic) and simultaneously reduce its side effects. [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]

IntroductionWe discuss event-related power differences (ERPDs) in low- and broadband-γ oscillations as the embedded-clause edge is processed in wh-dependencies such as Which decision regarding/about him/her did Paul say that Lydie rejected without hesitation? in first (L1) and second language (L2) French speakers.MethodsThe experimental conditions manipulated whether pronouns appeared in modifiers (Mods; regarding him/her) or in noun complements (Comps; about him/her) and whether they matched or mismatched a matrix-clause subject in gender.ResultsAcross L1 and L2 speakers, we found that anaphora-linked ERPDs for Mods vs. Comps in evoked power first arose in low γ and then in broadband γ. Referential elements first seem to be retrieved from working memory by narrowband processes in low γ and then referential identification seems to be computed in broadband-γ output. Interactions between discourse- and syntax-based referential processes for the Mods vs. Comps in these ERPDs furthermore suggest that multidomain γ-band processing enables a range of elementary operations for discourse and semantic interpretation.DiscussionWe argue that a multidomain mechanism enabling operations conditioned by the syntactic and semantic nature of the elements processed interacts with local brain microcircuits representing features and feature sets that have been established in L1 or L2 acquisition, accounting for a single language epistemology across learning contexts.

Abstract Objective To investigate the altered trends of regional homogeneity (ReHo) based on time and frequency, and clarify the time-frequency characteristics of ReHo in 48 classical trigeminal neuralgia (CTN) patients after a single pain stimulate. Methods All patients underwent three times resting-state functional MRI (before stimulation (baseline), after stimulation within 5 s (triggering-5 s), and in the 30th min of stimulation (triggering-30 min)). The spontaneous brain activity was investigated by static ReHo (sReHo) in five different frequency bands and dynamic ReHo (dReHo) methods. Results In the five frequency bands, the number of brain regions which the sReHo value changed in classical frequency band were most, followed by slow 4 frequency band. The left superior occipital gyrus was only found in slow 2 frequency band and the left superior parietal gyrus was only found in slow 3 frequency band. The dReHo values were changed in midbrain, left thalamus, right putamen, and anterior cingulate cortex, which were all different from the brain regions that the sReHo value altered. There were four altered trends of the sReHo and dReHo, which dominated by decreased at triggering-5 s and increased at triggering-30 min. Conclusions The duration of brain function changed was more than 30 min after a single pain stimulate, although the pain of CTN was transient. The localized functional homogeneity has time-frequency characteristic in CTN patients after a single pain stimulate, and the changed brain regions of the sReHo in five frequency bands and dReHo complemented to each other. Which provided a certain theoretical basis for exploring the pathophysiology of CTN.

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 evaluate asset returns and volatility connectedness using the time‐frequency connectedness model and machine learning approaches. Using 48 years of monthly indices of equity real estate investment trusts (EREITs), mortgage real estate investment trusts (MREITs), stocks, commodities, and bonds, we find that shocks to EREIT, and MREIT returns have a transitory impact on other assets. However, asset volatility connectedness among assets occurs at lower frequencies as markets slowly process pricing information. Therefore, shocks to EREIT and MREIT decay gradually and spill over to the other three assets for long periods. We also find that the intensity of the time‐frequency connectedness of returns and volatility varies with business cycles and significant domestic and global non‐recession events. We attribute the dominance of real estate investment trusts (REITs) in destabilising the financial system through long‐term volatility transmission to the heavy linkage of REITs to highly illiquid underlying direct real estate markets and high REITs leverage, making them more sensitive to real estate fundamentals, monetary shocks and macroeconomic risks than stocks and bonds. The result of the algorithm‐based GA2M machine learning model broadly supports the dominance of EREITs in the transmission of returns and volatility and shows that commodities have more explanatory power in the transmission of volatility than in returns. The empirical findings have implications for strategic and tactical asset allocation and policy design for market stability. [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]

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]

In this paper, we examine the presence of spillover among the regional energy markets in Asia, Europe, North America, Pacific region, and South America from January 2, 1995 to May 27, 2021. We report heterogeneous results for spillover between the regional energy markets. Our work separately highlights the presence of negative and positive returns spillover across sampled energy markets. We also report a significant increase in spillover during different crisis periods, supporting the contagion phenomena. Finally, we also report the asymmetry in short- and long-run spillover and negative and positive returns during normal and crisis periods. Our results carry implications for investments in the regional energy markets under different market conditions and investment periods. [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]

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; NADHD = 346, NCONTROL = 327), alpha decreases (d = 0.44, p

The consequences of frontotemporal lobar degeneration include changes in prefrontal cortical neurophysiology, with abnormalities of neural dynamics reported in the beta frequency range (14–30 Hz) that correlate with functional severity. We examined beta dynamics in two clinical syndromes associated with frontotemporal lobar degeneration: the behavioral variant of frontotemporal dementia (bvFTD) and progressive supranuclear palsy (PSP). Whilst these two syndromes are partially convergent in cognitive effects, they differ in disease mechanisms such as molecular pathologies and prefrontal atrophy. Whether bvFTD and PSP also differ in neurophysiology remains to be fully investigated. We compared magnetoencephalography from 20 controls, 23 people with bvFTD and 21 people with PSP (Richardson’s syndrome) during an auditory roving oddball paradigm. We measured changes in low and high total beta power responses (14–22 and 22–30 Hz respectively) over frontotemporal cortex in the period of the mismatch negativity response (100–250 ms post-stimulus). In controls, we found increased 14–22 Hz beta power following unexpected sensory events (i.e. increased deviant versus standard response), from right prefrontal cortex. Relative to controls, PSP reversed the mismatch response in this time–frequency window, reflecting reduced responses to the deviant stimuli (relative to standard stimuli). Abnormal beta at baseline in PSP could account for the reduced task-modulation of beta. Across bvFTD and PSP groups, the beta response to deviant stimuli (relative to standard stimuli) correlated with clinical severity, but not with atrophy of the prefrontal source region. These findings confirm the proposed importance of higher-order cortical regions, and their beta-power generators, in sensory change detection and context-updating during oddball paradigms. The physiological effects are proposed to result from changes in synaptic density, cortical neurotransmitters and subcortical connections, rather than merely atrophy. Beta-power changes may assist clinical stratification and provide intermediate outcomes for experimental medicine studies of novel therapeutic strategies.

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]

Information security has been defined as one of the most critical issues in the information era, as it is utilized to protect confidential information during transfers in real-world applications. In the case of image encryption, a variety of information security approaches were used. Domain spatial and domain frequency are two domains in which such techniques can be classified. This study uses a combination of the singular value decomposition (SVD) and discrete wavelet transformation (DWT) to construct an encryption method based on a traditional watermarking system. Compared with other traditional methods, the proposed DWT-SVD approach has excellent robustness, and it has been strengthened for having high degree of the robustness against the additive white Gaussian noise (AWGN) attacks by utilizing a de-noising strategy based upon S-transform approach. Compared with DWT algorithm denoising approach, the results reveal that the S-transform denoising algorithm that has been deployed in the present article has a robust protection towards the Gaussian noise attack for mean squared error (MSE) around 0.005 and peak signal to noise ratio (PSNR) around 24 dB. [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]

Abstract The ongoing transformation of electrical power systems highlights the weaknesses of the protection schemes of traditional devices because they are designed and configured according to traditional characteristics of the system. Therefore, this work proposes a new methodology to study the fault-generated high frequency transient signals in transmission lines through multiresolution analysis. The high frequency components are determined by a new digital filtering technique based on mathematical morphology theory and a spectral energy index. Consequently, wide spectra of signals in the time–frequency domain are obtained. The performance of this method is verified on an electrical power system modeled in ATP-Draw, where simulation and test signals are developed for different locations, fault resistances, inception angles, high frequency noises, sampling frequencies, types of faults, and shapes of the structuring element. The results show the characteristics of the fault such as the traveling wave frequency, location, and starting time.

As they are the core components of the drive motor in electric vehicles, the accurate fault diagnosis of rolling bearings is the key to ensuring the safe operation of electric vehicles. At present, intelligent diagnostic methods based on current signals (CSs) are widely used owing to the advantages of the easy collection, low cost, and non-invasiveness of CSs. However, in practical applications, the fault characteristics of the CS are weak, resulting in diagnostic performance that fails to meet the expected standards. In this paper, a diagnosis method is proposed to address this problem and enhance the diagnosis accuracy. Firstly, CSs from two phases are processed by periodic resampling to enhance data features, which are then fused through splicing operations. Subsequently, a feature enhancement module is constructed using multi-scale feature fusion for decomposing the input. Finally, a diagnosis model is constructed by using an improved channel attention module (CAM) for enhancing the diagnosis performance. The results from experiments containing two different types of bearing datasets show that the proposed method can extract high-quality fault features and improve the diagnosis accuracy, presenting great potential in intelligent fault diagnosis and the maintenance of electric vehicles.

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%.

The empirical wavelet transforms and Capon time-frequency applications are used in this work. EMG and EEG are non-invasive ways to measure muscle activity and the electrical activity of the brain. The visual analyses of EMG and EEG are significantly impacted by these noises. The EWT's effectiveness in eliminating the sounds has been assessed by using several common metrics between the clean original signal and the filter's signal output. According to the results, the EWT has performed better than other denoising algorithms in terms of locating the different parts of the aberrant biomedical signal data. The EMG signal is non-stationary. Hence, employing time-frequency methods is unavoidable. Capon is the preferred parametric time-frequency technique. The Capon technique outperforms other non-parametric time-frequency algorithms mentioned in the scientific literature in terms of resolution and removing any interference terms. This study explains why the use of EWT and Capon approaches together is beneficial for biomedicine. [ABSTRACT FROM AUTHOR]

The purpose of the study is to contrast the impact of 'clean' and 'dirty' electricity production on economic growth for South Africa and determine whether a faster transition from fossil fuels to renewables is beneficial for growth. To this end, we use wavelet coherence analysis to examine the time–frequency relationship between electricity and economic growth for aggregated and disaggregated measures of clean and dirty sources over the period 1985–2021 At an aggregated level, the low frequency (long-run) correlations are eventually substituted with high frequency (short-run) co-movements. At disaggregated level, the results are mixed, with dirty energy components (coal, oil, gas) having a weakening effect on economic growth over time whilst clean energy sources (solar and wind, biomass, hydro) show the greatest potential for growth over both low and high frequency relationships. Moreover, the various structural breaks identified in the frequency bands for different electricity sources allow us to evaluate the impact of energy policies and load shedding on the electricity-growth relationship and offer further insights to which clean sources of electricity production have more potential to be growth enhancing. [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]

Abstract Introduction The present paper discusses the findings of a systematic review of EEG measures in neuromarketing, identifying which EEG measures are the most robust predictor of customer preference in neuromarketing. The review investigated which TF effect (e.g., theta-band power), and ERP component (e.g., N400) was most consistently reflective of self-reported preference. Machine-learning prediction also investigated, along with the use of EEG when combined with physiological measures such as eye-tracking. Methods Search terms ‘neuromarketing’ and ‘consumer neuroscience’ identified papers that used EEG measures. Publications were excluded if they were primarily written in a language other than English or were not published as journal articles (e.g., book chapters). 174 papers were included in the present review. Results Frontal alpha asymmetry (FAA) was the most reliable TF signal of preference and was able to differentiate positive from negative consumer responses. Similarly, the late positive potential (LPP) was the most reliable ERP component, reflecting conscious emotional evaluation of products and advertising. However, there was limited consistency across papers, with each measure showing mixed results when related to preference and purchase behaviour. Conclusions and implications FAA and the LPP were the most consistent markers of emotional responses to marketing stimuli, consumer preference and purchase intention. Predictive accuracy of FAA and the LPP was greatly improved through the use of machine-learning prediction, especially when combined with eye-tracking or facial expression analyses.

In the past few decades, time–frequency (TF) methods have become increasingly popular in the field of modal identification and structural health monitoring. The general and significant shortfalls of the TF models are the distributed and vague frequency components as an output. To overcome the cons of the initial TF technique, segregation of the modes incorporated within the affected vibration signatures evolved eventually. The most utilized and preferred technique can be named empirical mode decomposition (EMD) and its variants. However, these variants, along with the basic framework, possess the multi-mode tendency, which further compels the extension of the whole damage identification procedure. This work presents the development of a temporally evolved, generalized and high impulsive frequency acquisition model for structural damage identification. To meet the said outcome, variational mode decomposition (VMD) is considered an optimization-based signal decomposition tool that utilizes the contents of the generated modal responses. This work also aims to investigate the applicability of VMD for the purpose of structural damage identification for the following situations: the old ADA steel bridge under vehicle-induced vibration for various damage scenarios; a three-story shear frame building model. Along with the said validation of the proposed condition monitoring model, this paper addresses the need of stable and reliable novel damage indicator (DI) as a statistical alteration of the extracted instantaneous outputs. The proposed DI as the mean of the ratio of the instantaneous outputs collectively indicates the relative capacity of the structure to produce a stable response. The selected outputs are instantaneous frequency, phase and energy, taken into consideration to depict the physical alterations in the measured response. The proposed DI evidently explains the physical relation of the involved factors with the degradation causes in the considered structure. The DI also reflects the remaining usefulness of the system and provides the measurements in safety aspects to the operators. The results so obtained are well competent for real bridge structures under the influence of dynamic loading conditions induced by vehicle movements. [ABSTRACT FROM AUTHOR]

The trend of using deep learning techniques to classify arbitrary tasks has grown significantly in the last decade. Such techniques in the background provide a stack of non-linear functions to solve tasks that cannot be solved in a linear manner. Naturally, deep learning models can always solve almost any problem with the right amount of functional parameters. However, with the right set of preprocessing techniques, these models might become much more accessible by negating the need for a large set of model parameters and the concomitant computational costs that accompany the need for many parameters. This paper studies the effects of such preprocessing techniques, and is focused, more specifically, on the resulting learning representations, so as to classify the arrhythmia task provided by the ECG MIT-BIH signal dataset. The types of noise we filter out from such signals are the Baseline Wander (BW) and the Powerline Interference (PLI). The learning representations we use as input to a Convolutional Neural Network (CNN) model are the spectrograms extracted by the Short-time Fourier Transform (STFT) and the scalograms extracted by the Continuous Wavelet Transform (CWT). These features are extracted using different parameter values, such as the window size of the Fourier Transform and the number of scales from the mother wavelet. We highlight that the noise with the most significant influence on a CNN's classification performance is the BW noise. The most accurate classification performance was achieved using the 64 wavelet scales scalogram with the Mexican Hat and with only the BW noise suppressed. The deployed CNN has less than 90k parameters and achieved an average F1-Score of 90.11%. [ABSTRACT FROM AUTHOR]

The ongoing transformation of electrical power systems highlights the weaknesses of the protection schemes of traditional devices because they are designed and configured according to traditional characteristics of the system. Therefore, this work proposes a new methodology to study the fault-generated high frequency transient signals in transmission lines through multiresolution analysis. The high frequency components are determined by a new digital filtering technique based on mathematical morphology theory and a spectral energy index. Consequently, wide spectra of signals in the time–frequency domain are obtained. The performance of this method is verified on an electrical power system modeled in ATP-Draw, where simulation and test signals are developed for different locations, fault resistances, inception angles, high frequency noises, sampling frequencies, types of faults, and shapes of the structuring element. The results show the characteristics of the fault such as the traveling wave frequency, location, and starting time. [ABSTRACT FROM AUTHOR]

Fault diagnosis of rolling bearings under variable speed conditions is a challenging task since the vibration signal exhibits time-varying non-stationary characteristics and is usually contaminated by strong noise. Most of the current researches employ the adaptive filtering or signal decomposition methods to obtain the impulse signals caused by the bearing fault before feature extraction, which, however, are not capable of removing the in-band noise. To address this issue, a novel method called non-stationary harmonic summation (NHS) is proposed based on the fact that the repetitive impulses caused by the bearing fault consist of a series of equally-spaced harmonics in the frequency domain. Firstly, the harmonic characteristics are theoretically analyzed and the results show that the impulses contain non-stationary harmonics with a time-varying spacing frequency (i.e., the fault characteristic frequency) under variable speed conditions. Next, according to the harmonic characteristics, an efficient algorithm combining the parameterized demodulation with the adaptive chirp mode decomposition is developed to extract the non-stationary harmonics and then summate these harmonics to reconstruct the repetitive impulses for the fault feature extraction. Since the NHS elaborately exploits the intrinsic harmonic structure of the impulse signals, the noise can be fully removed and the reconstructed signal is free of side-band interference caused by complex amplitude modulation. Both simulated and experimental signals are considered to demonstrate the advantages of the NHS for bearing fault diagnosis under variable speed conditions. [ABSTRACT FROM AUTHOR]

Sleep restriction affects people's decision-making behavior. Nap restriction is a vital subtopic within sleep restriction research. In this study, we used EEG to investigate the impact of nap sleep restriction on intertemporal decision-making (Study 1) and decision-making across risky outcomes (Study 2) from ERP and time-frequency perspectives. Study 1 found that habitual nappers restricting their naps felt more inclined to choose immediate, small rewards over delayed, large rewards in an intertemporal decision-making task. P200s, P300s, and LPP in our nap-restriction group were significantly higher than those in the normal nap group. Time-frequency results showed that the delta band (1 ~ 4 Hz) power of the restricted nap group was significantly higher than that of the normal nap group. In Study 2, the nap-restriction group was more likely to choose risky options. P200s, N2s, and P300s in the nap deprivation group were significantly higher than in the normal nap group. Time-frequency results also found that the beta band (11 ~ 15 Hz) power of the restricted nap group was significantly lower than that of the normal nap group. The habitual nappers became more impulsive after nap restriction and evinced altered perceptions of time. The time cost of the LL (larger-later) option was perceived to be too high when making intertemporal decisions, and their expectation of reward heightened when making risky decisions—believing that they had a higher probability of receiving a reward. This study provided electrophysiological evidence for the dynamic processing of intertemporal decision-making, risky decision-making, and the characteristics of nerve concussions for habitual nappers. [ABSTRACT FROM AUTHOR]