Experimental analysis of rotating bridge structural responses to existing railway train loads via time-frequency and Hilbert-Huang transform energy spectral analysis

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.

Exploring neurophysiological correlates of visually induced motion sickness using electroencephalography (EEG)

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.

Global energy markets connectedness: evidence from time-frequency domain

We examine the presence of dependence across 51 energy markets classified into different regions from Jan 2007 to June 2021. In order to examine the presence of dependence across different energy markets, we apply standard and threshold dependence measures proposed by Diebold and Yilmaz, Int J Forecast 28:57-66, (2012) and Baruník and Křehlík, J Financ Econ 16(2):271-296, (2018). We highlight the presence of strong dependence between the energy markets at both regional level and across other regions. European and American energy markets are highly connected within the region over the long-run whereas Asia-Pacific and the African energy markets offer optimal diversification opportunities. Both short- and long-run dependence exists between Chinese and the Hong Kong energy markets and between the US and Canadian energy markets. We also witness substantial increase dependence across all the energy markets during different crisis periods.

Information transmission in regional energy stock markets

Since markets are undergoing severe turbulent economic periods, this study investigates the information transmission of energy stock markets of five regions including North America, South America, Europe, Asia, and Pacific where we differentiated the regional energy markets based on their developing and developed state of economy. We employed time-frequency domain from Jan 1995 to May 2021 and found that energy stocks of developed regions are highly connected. The energy markets of North America, South America, and Europe are the net transmitters of spillovers, whereas the Asian and Pacific energy markets are the net receivers of spillovers. The results also reveal that the connectedness of regional energy markets is time and frequency dependent. Regional energy stocks were highly connected following the Asian financial crisis (AFC), global financial crisis (GFC), European debt crisis (EDC), shale oil revolution (SOR), and COVID-19 pandemic. Time-dependent results reveal that high spillovers formed during stress periods and frequency domain show the higher connectedness of regional energy stock markets in the short run followed by an extreme economic condition. These results have significant implications for policymakers, regulators, investors, and regional controlling bodies to adopt effective strategies during short run to avoid economic downturns and information distortions.

Hold your horses: Differences in EEG correlates of inhibition in cancelling and stopping an action

Behavioral adaptation to changing contextual contingencies often requires the rapid inhibition of planned or ongoing actions. Inhibitory control has been mostly studied using the stop-signal paradigm, which conceptualizes action inhibition as the outcome of a race between independent GO and STOP processes. Inhibition is predominantly considered to be independent of action type, yet it is questionable whether this conceptualization can apply to stopping an ongoing action. To test the claimed generality of action inhibition, we investigated behavioral stop-signal reaction time (SSRT) and scalp electroencephalographic (EEG) activity in two inhibition contexts: Using variants of the stop-signal task, we asked participants to cancel a prepared-discrete action or to stop an ongoing-rhythmic action in reaction to a STOP signal. The behavioral analysis revealed that the discrete and rhythmic SSRTs were not correlated. The EEG analysis showed that the STOP signal evoked frontocentral activity in the time and frequency domains (Delta/Theta range) in a task-specific manner: The P3 onset latency was the best correlate of discrete SSRT whereas N2/P3 peak-to-peak amplitude was the best correlate of rhythmic SSRT. These findings do not support a conceptualization of inhibition as action-independent but rather suggest that the differential engagement of both components of the N2/P3-complex as a function of action type pertains to functionally independent inhibition subprocesses.

Automated detection of COVID-19 cough

Easy detection of COVID-19 is a challenge. Quick biological tests do not give enough accuracy. Success in the fight against new outbreaks depends not only on the efficiency of the tests used, but also on the cost, time elapsed and the number of tests that can be done massively. Our proposal provides a solution to this challenge. The main objective is to design a freely available, quick and efficient methodology for the automatic detection of COVID-19 in raw audio files.

Our proposal is based on automated extraction of time–frequency cough features and selection of the more significant ones to be used to diagnose COVID-19 using a supervised machine-learning algorithm.

Random Forest has performed better than the other models analysed in this study. An accuracy close to 90% was obtained.

This study demonstrates the feasibility of the automatic diagnose of COVID-19 from coughs, and its applicability to detecting new outbreaks.

Conjoint fluctuations of PFC-mediated processes and behavior: An investigation of error-related neural mechanisms in relation to sustained attention

The ability to detect errors, which derives from the medial prefrontal cortex (mPFC), is crucial to maintain attention over a long period of time. While impairment of this ability has been reported in patients with sustained attention disruption, the role mPFC-mediated processes play in the intra-individual fluctuation of sustained attention remains an open question. In this context, we computed the variance time course of reaction time (RT) of 42 healthy individuals to distinguish intra-individual periods of low and high performance instability, assumed to represent optimal and suboptimal attentional states, when performing a sustained Go/NoGo task. Analysis of the neurophysiological mechanisms of response monitoring revealed a specific reduction in the error-related negativity (ERN) amplitude and frontal midline theta power during periods of high compared to low RT variability, but only in individuals with a higher standard deviation of reaction time (SD-RT). Concerning post-error adaptation, an increase in the correct-related negativity (CRN) amplitude as well as the frontal lateral theta power on trials following errors was observed in individuals with lower SD-RT but not in those with higher SD-RT. Our results thus show that individuals with poor sustained attention ability exhibit altered post-error adaptation and attentional state-dependent efficiency of error monitoring. Conversely, individuals with good sustained attention performances retained their post-error adaptation and response monitoring regardless of the attentional periods. These findings reveal the critical role of the action-monitoring system in intra-individual behavioral stability and highlight the importance of considering attentional states when studying mPFC-mediated processes, especially in subjects with low sustained attention ability.

Automated epilepsy detection techniques from electroencephalogram signals: a review study

Epilepsy is a serious neurological condition which contemplates as top 5 reasons for avoidable mortality from ages 5-29 in the worldwide. The avoidable deaths due to epilepsy can be reduced by developing efficient automated epilepsy detection or prediction machines or software. To develop an automated epilepsy detection framework, it is essential to properly understand the existing techniques and their benefit as well as detriment also. This paper aims to provide insight on the information about the existing epilepsy detection and classification techniques as they are crucial for supporting clinical-decision in the course of epilepsy treatment. This review study accentuate on the existing epilepsy detection approaches and their drawbacks. This information presented in this article will be helpful to the neuroscientist, researchers as well as to technicians for assisting them in selecting the reliable and appropriate techniques for analyzing epilepsy and developing an automated software system of epilepsy identification.

SinusCor: an advanced tool for heart rate variability analysis

Background

Heart rate variability (HRV) is a widespread non-invasive technique to assess cardiac autonomic function. Time and frequency domain analyses have been used in HRV studies, and their interpretations are linked with both clinical prognostic and diagnostic information. Statistical and geometrical parameters, Fast Fourier Transform and Autoregressive based periodograms are commonly used approaches for the assessment of stationary RR intervals (RRi) signals. However, some conditions result in non-stationary HRV behavior such as the “tilt test” and exercise. This study presents the SinusCor, a new free software for HRV analysis that includes the classical time and frequency domain indices and also techniques for non-stationary data analyses in both time (i.e. root mean squared of successive differences; RMSSD calculated with moving segments) and frequency domains (i.e. time–frequency analysis).

Results

An example of RRi was acquired from a young male subject and its time and frequency domain indices were calculated. Time-varying and time–frequency analyses were also presented using the RMSSD and total power, respectively. Validation of the present software against a standard software for HRV analysis (Kubios v 3.0.1) was also performed [SinusCor vs. Kubios: RMSSD—93.96 (41.55) vs. 93.96 (41.55) ms; SDNN—101.29 (29.03) vs. 101.29 (29.03) ms; LF—50.42 (19.76) vs. 50.56 (19.56) n.u.; HF—49.57 (19.76) vs. 49.38 (19.56) n.u.; LF/HF—1.38 (1.08) vs. 1.38 (1.07)].

Conclusions

SinusCor might be a useful tool for classical stationary and non-stationary HRV analysis.

Analysis of the tennis racket vibrations during forehand drives: Selection of the mother wavelet

The time-frequency analysis of the tennis racket and hand vibrations is of great interest for discomfort and pathology prevention. This study aimed to (i) to assess the stationarity of the vibratory signal of the racket and hand and (ii) to identify the best mother wavelet to perform future time-frequency analysis, (iii) to determine if the stroke spin, racket characteristics and impact zone can influence the selection of the best mother wavelet. A total of 2364 topspin and flat forehand drives were performed by fourteen male competitive tennis players with six different rackets. One tri-axial and one mono-axial accelerometer were taped on the racket throat and dominant hand respectively. The signal stationarity was tested through the wavelet spectrum test. Eighty-nine mother wavelet were tested to select the best mother wavelet based on continuous and discrete transforms. On average only 25±17%, 2±5%, 5±7% and 27±27% of the signal tested respected the hypothesis of stationarity for the three axes of the racket and the hand respectively. Regarding the two methods for the detection of the best mother wavelet, the Daubechy 45 wavelet presented the highest average ranking. No effect of the stroke spin, racket characteristics and impact zone was observed for the selection of the best mother wavelet. It was concluded that alternative approach to Fast Fourier Transform should be used to interpret tennis vibration signals. In the case where wavelet transform is chosen, the Daubechy 45 mother wavelet appeared to be the most suitable.

Cerebrovascular pattern improved by ozone autohemotherapy: an entropy-based study on multiple sclerosis patients

Ozone major autohemotherapy is effective in reducing the symptoms of multiple sclerosis (MS) patients, but its effects on brain are still not clear. In this work, we have monitored the changes in the cerebrovascular pattern of MS patients and normal subjects during major ozone autohemotherapy by using near-infrared spectroscopy (NIRS) as functional and vascular technique. NIRS signals are analyzed using a combination of time, time-frequency analysis and nonlinear analysis of intrinsic mode function signals obtained from empirical mode decomposition technique. Our results show that there is an improvement in the cerebrovascular pattern of all subjects indicated by increasing the entropy of the NIRS signals. Hence, we can conclude that the ozone therapy increases the brain metabolism and helps to recover from the lower activity levels which is predominant in MS patients.

Characteristics of local field potentials correlate with pain relief by deep brain stimulation

OBJECTIVE

To investigate the link between neuronal activity recorded from the sensory thalamus and periventricular gray/periaqueductal gray (PVAG) and pain relief by deep brain stimulation (DBS).

METHODS

Local field potentials (LFPs) were recorded from the sensory thalamus and PVAG post-operatively from ten patients with neuropathic pain. The LFPs were quantified using spectral and time-frequency analysis, the relationship between the LFPs and pain relief was quantified with nonlinear correlation analysis.

RESULTS

The theta oscillations of both sensory thalamus and PVAG correlated inversely with pain relief. The high beta oscillations in the sensory thalamus and the alpha oscillations in the PVAG correlated positively with pain relief. Moreover, the ratio of high-power duration to low-power duration of theta band activity in the sensory thalamus and PVAG correlated inversely with pain relief. The duration ratio at the high beta band in the sensory thalamus correlated positively with pain relief.

CONCLUSIONS

Our results reveal distinct neuronal oscillations at the theta, alpha, and beta frequencies correlating with pain relief by DBS.

SIGNIFICANCE

The study provides quantitative measures for predicting the outcomes of neuropathic pain relief by DBS as well as potential biomarkers for developing adaptive stimulation strategies.

Witnessing the first sign of retinitis pigmentosa onset in the allegedly normal eye of a case of unilateral RP: a 30-year follow-up

PURPOSE

A patient initially presented with constricted visual field, attenuated retinal vasculature, pigmentary clumping and reduced ERG in OS only, suggestive of unilateral retinitis pigmentosa (RP). This patient was subsequently seen on eight occasions (over three decades), and, with time, the initially normal eye (OD) gradually showed signs of RP-like degeneration. The purpose of this study was to evaluate which clinical modality (visual field, funduscopy or electroretinography) could have first predicted this fate.

METHODS

At each time points, data obtained from our patient were compared to normative data using Z tests.

RESULTS

At initial visit, all tests were significantly (p < 0.05) altered in OS and normal in OD. Visual field and retinal vessel diameter in OD reduced gradually to reach statistical significance at the 5th visit and 6th visit (21 and 22 years after the first examination, respectively). In OD, the amplitude of the scotopic and photopic ERGs reduced gradually and was significantly smaller than normal at the 2nd visit (after 11 years) and 3rd visit (after 18 years), respectively. When the photopic ERG was analyzed using the discrete wavelet transform (DWT), we were able to detect a significant change at the 2nd visit (after 11 years) instead of the 3rd visit (18 years).

CONCLUSIONS

Our study allowed us to witness the earliest manifestation of an RP disease process. The ERG was the first test to detect significant RP changes. A significantly earlier detection of ERG anomalies was obtained when the DWT was used, demonstrating its advantage for early detection of ERG changes.

High Frequency Oscillations and spikes: Separating real HFOs from false oscillations

OBJECTIVE

To demonstrate and quantify the occurrence of false High Frequency Oscillations (HFOs) generated by the filtering of sharp events. To distinguish real HFOs from spurious ones using analysis of the raw signal.

METHOD

We developed a new method to prevent false HFO detections due to the filtering effect by detecting oscillations in the raw signal at the time of sharp events. We specified temporal features to classify sharp events with and without HFOs using support vector machine in both ripple and fast ripple bands. The traditionally used time-frequency representation served as the gold standard to indicate real and false HFOs.

RESULTS

44% of ripples and 43% of FRs concurring with sharp events were found to be false HFOs. Sharp events with HFOs had significantly more oscillations in the raw signal than sharp events without. They could be distinguished from false HFOs with accuracy of 76.6% in the ripple band and 72.6% in the fast ripple band.

CONCLUSION

It may be most appropriate to detect HFOs as oscillations not only on the filtered signal but also on the raw signal. The classical time-frequency display used for identifying HFOs should be used with great care due to the possible masking effect of broadband activities.

SIGNIFICANCE

The separation of real HFOs from broadband activities will raise the validity of HFO detection methods and will therefore support future HFO investigations.

Comparison of different spatial transformations applied to EEG data: A case study of error processing

The purpose of this paper is to compare the effects of different spatial transformations applied to the same scalp-recorded EEG data. The spatial transformations applied are two referencing schemes (average and linked earlobes), the surface Laplacian, and beamforming (a distributed source localization procedure). EEG data were collected during a speeded reaction time task that provided a comparison of activity between error vs. correct responses. Analyses focused on time-frequency power, frequency band-specific inter-electrode connectivity, and within-subject cross-trial correlations between EEG activity and reaction time. Time-frequency power analyses showed similar patterns of midfrontal delta-theta power for errors compared to correct responses across all spatial transformations. Beamforming additionally revealed error-related anterior and lateral prefrontal beta-band activity. Within-subject brain-behavior correlations showed similar patterns of results across the spatial transformations, with the correlations being the weakest after beamforming. The most striking difference among the spatial transformations was seen in connectivity analyses: linked earlobe reference produced weak inter-site connectivity that was attributable to volume conduction (zero phase lag), while the average reference and Laplacian produced more interpretable connectivity results. Beamforming did not reveal any significant condition modulations of connectivity. Overall, these analyses show that some findings are robust to spatial transformations, while other findings, particularly those involving cross-trial analyses or connectivity, are more sensitive and may depend on the use of appropriate spatial transformations.

Reduced negative affect response in female psychopaths

Studies that investigate the differences between high and low psychopathic persons in brain activity during emotional facial expression processing are rare and commonly focus on males. The current study assessed whether previously reported behavioral differences would be reflected in differential brain activity in a sample of female offenders. The participants included 23 female forensic inpatients with high and low scores on the Psychopathy Checklist Revised (PCL-R). ERPs were recorded during presentation of emotional facial expressions (i.e., fear, angry, and happy). Results revealed no differences in N170, P3 and late positive potential components between groups, but a significant difference in N2 only for angry and fear facial expressions, with high psychopathic participants showing lower reactivity. This N2 effect was found to be related to Factor 2 but not Factor 1 of the PCL-R. In time frequency analysis, theta activity underlying N2 best reflected these differences. Findings in this female sample are consistent with a cortical deficit in processing facial expression of negative emotions in psychopathic men. In addition, differences in processing seem to appear relatively early.

Theta and delta band activity explain N2 and P3 ERP component activity in a go/no-go task

OBJECTIVES

Recent work indicates that the feedback negativity and P3 components from gambling feedback tasks can be understood as mixtures of functionally distinct processes occurring separately in theta and delta frequency bands. The current study was conducted to assess whether dissociable processes occurring in the theta and delta bands would similarly account for activity underlying N2 and P3 components in a go/no-go task.

METHODS

The current study measured EEG signals from 66 participants during a go/no-go task, and a time-frequency principal components analysis decomposition approach was used to extract theta and delta measures from condition averages.

RESULTS

Theta and delta measures separately increased in relation to response inhibition, and were uniquely related to the N2 and P3 components, as predicted.

CONCLUSIONS

Findings support the view that the theta and delta measures indexed separable processes related to response inhibition, and better indexed the processes underlying N2 and P3 components in this go/no-go task.

SIGNIFICANCE

Theta and delta measures may index separable functional processes across other common ERP tasks, and may represent an improved target for research relative to standard time-domain components.