Nomogram for prediction of hearing rehabilitation outcome in children with congenital sensorineural hearing loss after cochlear implantation.

Background: Reliable predictors for rehabilitation outcomes in patients with congenital sensorineural hearing loss (CSNHL) after cochlear implantation (CI) are lacking. The purchase of this study was to develop a nomogram based on clinical characteristics and neuroimaging features to predict the outcome in children with CSNHL after CI. Methods: Children with CSNHL prior to CI surgery and children with normal hearing were enrolled into the study. Clinical data, high resolution computed tomography (HRCT) for ototemporal bone, conventional brain MRI for structural analysis and brain resting-state fMRI (rs-fMRI) for the power spectrum assessment were assessed. A nomogram combining both clinical and imaging data was constructed using multivariate logistic regression analysis. Model performance was evaluated and validated using bootstrap resampling. Results: The final cohort consisted of 72 children with CSNHL (41 children with poor outcome and 31 children with good outcome) and 32 healthy controls. The white matter lesion from structural assessment and six power spectrum parameters from rs-fMRI, including Power4, Power13, Power14, Power19, Power23 and Power25 were used to build the nomogram. The area under the receiver operating characteristic (ROC) curve of the nomogram obtained using the bootstrapping method was 0.812 (95 % CI = 0.772-0.836). The calibration curve showed no statistical difference between the predicted value and the actual value, indicating a robust performance of the nomogram. The clinical decision analysis curve showed a high clinical value of this model. Conclusions: The nomogram constructed with clinical data, and neuroimaging features encompassing ototemporal bone measurements, white matter lesion values from structural brain MRI and power spectrum data from rs-fMRI showed a robust performance in predicting outcome of hearing rehabilitation in children with CSNHL after CI.

High-skilled first-person shooting game players have specific frontal lobe activity: Power spectrum analysis in an electroencephalogram study.

First-person shooting (FPS) games are among the most famous video games worldwide. However, cortical activities in environments related to real FPS games have not been studied. This study aimed to determine differences in cortical activity between low- and high-skilled FPS game players using 160-channel electroencephalography. Nine high-skilled FPS game players (official ranks: above the top 10%) and eight low-skilled FPS game players (official ranks: lower than the top 20%) were recruited for the experiment. The task was set for five different conditions using the AimLab program, which was used for the FPS game players' training. Additionally, we recorded the brain activity in the resting condition before and after the task, in which the participants closed their eyes and relaxed. The reaction time and accuracy (the number of hit-and-miss targets) were calculated to evaluate the task performance. The results showed that high-skilled FPS game players have fast reaction times and high accuracy during tasks. High-skilled FPS game players had higher cortical activity in the frontal cortex than low-skilled FPS game players during each task. In low-skilled players, cortical activity level and performance level were associated. These results suggest that high cortical activity levels were critical to achieving high performance in FPS games.

Visually evoked local field potential changes in the caudate nucleus are remarkably more frequent in awake, behaving cats than in anaesthetized animals.

Previous results show that halothane gas anaesthesia has a suppressive effect on the visually evoked single-cell activities in the feline caudate nucleus (CN). In this study, we asked whether the low-frequency neuronal signals, the local field potentials (LFP) are also suppressed in the CN of anaesthetized animals.To answer this question, we compared the LFPs recorded from the CN of two halothane-anaesthetized (1.0%), paralyzed, and two awake, behaving cats during static and dynamic visual stimulation. The behaving animals were trained to perform a visual fixation task.Our results denoted a lower proportion of significant power changes to visual stimulation in the CN of the anesthetized cats in each frequency range (from delta to beta) of the LFPs, except gamma. These differences in power changes were more obvious in static visual stimulation, but still, remarkable differences were found in dynamic stimulation, too. The largest differences were found in the alpha and beta frequency bands for static stimulation. Concerning dynamic stimulation, the differences were the biggest in the theta, alpha and beta bands.Similar to the single-cell activities, remarkable differences were found between the visually evoked LFP changes in the CN of the anaesthetized, paralyzed and awake, behaving cats. The halothane gas anaesthesia and the immobilization suppressed the significant LFP power alterations in the CN to both static and dynamic stimulation. These results suggest the priority of the application of behaving animals even in the analysis of the visually evoked low-frequency electric signals, the LFPs recorded from the CN.

Regional Thermal Radiation Characteristics of FY Satellite Remote Sensing Based on Big Data Analysis.

It is of great significance to study the thermal radiation anomalies of earthquake swarms in the same area in terms of selecting abnormal characteristic determination parameters, optimizing and determining the processing model, and understanding the abnormal machine. In this paper, we investigated short-term and long-term thermal radiation anomalies induced by earthquake swarms in Iran and Pakistan between 2007 and 2016. The anomalies were extracted from infrared remote sensing black body temperature data from the China Geostationary Meteorological Satellites (FY-2C/2E/2F/2G) using the multiscale time-frequency relative power spectrum (MS T-FRPS) method. By analyzing and summarizing the thermal radiation anomalies of series earthquake groups with consistency law through a stable and reliable MS T-FRPS method, we first obtained the relationship between anomalies and ShakeMaps from USGS and proposed the anomaly regional indicator (ARI) to determine seismic anomalies and the magnitude decision factor (MDF) to determine seismic magnitude. In addition, we explored the following discussions: earthquake impact on regional thermal radiation background and the relationship between thermal anomalies and earthquake magnitude and the like. Future research directions using the MS T-FRPS method to characterize regional thermal radiation anomalies induced by strong earthquakes could help improve the accuracy of earthquake magnitude determination.

Quantitative and Qualitative Representation of Introductory and Advanced EEG Concepts: An Exploration of Different EEG Setups.

Electroencephalograms (EEGs) are the gold standard test used in the medical field to diagnose epilepsy and aid in the diagnosis of many other neurological and mental disorders. Growing in popularity in terms of nonmedical applications, the EEG is also used in research, neurofeedback, and brain-computer interface, making it increasingly relevant to student learning. Recent innovations have made EEG setups more accessible and affordable, thus allowing their integration into neuroscience educational settings. Introducing students to EEGs, however, can be daunting due to intricate setup protocols, individual variation, and potentially expensive equipment. This paper aims to provide guidance for introducing students and educators to fundamental beginning and advanced level EEG concepts. Specifically, this paper tested the potential of three different setups, with varying channel number and wired or wireless connectivity, for introducing students to qualitative and quantitative exploration of alpha enhancement when eyes are closed, and observation of the alpha/beta anterior to posterior gradient. The setups were compared to determine their relative advantages and their robustness in detecting these well-established parameters. The basic 1- or 2-channel setups are sufficient for observing alpha and beta waves, while more advanced systems containing 8 or 16 channels are required for consistent observation of an anterior-posterior gradient. In terms of localization, the 16-channel setup, in principle, was more adept. The 8-channel setup, however, was more effective than the 16-channel setup with regards to displaying the anterior to posterior gradient. Thus, an 8-channel setup is sufficient in an education setting to display these known trends. Modification of the 16-channel setup may provide a better observation of the anterior to posterior gradient.

Reduced Resting-State EEG Power Spectra and Functional Connectivity after 24 and 36 Hours of Sleep Deprivation.

Total sleep deprivation (TSD) leads to cognitive decline; however, the neurophysiological mechanisms underlying resting-state electroencephalogram (EEG) changes after TSD remain unclear. In this study, 42 healthy adult participants were subjected to 36 h of sleep deprivation (36 h TSD), and resting-state EEG data were recorded at baseline, after 24 h of sleep deprivation (24 h TSD), and after 36 h TSD. The analysis of resting-state EEG at baseline, after 24 h TSD, and after 36 h TSD using source localization analysis, power spectrum analysis, and functional connectivity analysis revealed a decrease in alpha-band power and a significant increase in delta-band power after TSD and impaired functional connectivity in the default mode network, precuneus, and inferior parietal lobule. The cortical activities of the precuneus, inferior parietal lobule, and superior parietal lobule were significantly reduced, but no difference was found between the 24 h and 36 h TSD groups. This may indicate that TSD caused some damage to the participants, but this damage temporarily slowed during the 24 h to 36 h TSD period.

CFD analysis of heat transfer enhancement by wall mounted flexible flow modulators in a channel with pulsatile flow.

The aim of the present study is to explore heat transfer and pressure drop characteristics in a pulsating channel flow due to wall-mounted flexible flow modulators (FFM). Cold air in pulsating fashion is forced to enter through the channel having isothermally heated top and bottom walls with one/multiple FFMs mounted on them. The dynamic conditions of pulsating inflow are characterized by Reynolds number, non-dimensional pulsation frequency and amplitude. Applying the Galerkin finite element method in an Arbitrary Lagrangian-Eulerian (ALE) framework, the present unsteady problem has been solved. Flexibility (10-4 ≤ Ca ≤ 10-7), orientation angle (60° ≤ Î¸ ≤ 120°), and location of FFM(s) have been considered in this study to find out the best-case scenario for heat transfer enhancement. The system characteristics have been analyzed by vorticity contours and isotherms. Heat transfer performance has been evaluated in terms of Nusselt number variations and pressure drop across the channel. Besides, power spectrum analysis of thermal field oscillation along with that of the FFM's motion induced by pulsating inflow has been performed. The present study reveals that single FFM having flexibility of Ca = 10-5 and an orientation angle of θ = 90° offers the best-case scenario for heat transfer enhancement.

Electroencephalographic slowdowns during sleep are associated with cognitive impairment in patients who have obstructive sleep apnea but no dementia.

OBJECTIVES: To research the relationship between quantitative electroencephalogram (qEEG) and impaired cognitive function patients who have obstructive sleep apnea (OSA) but no dementia. METHODS: Subjects who complained of snoring between March 2020 and April 2021 in the Sleep Medicine Center of Weihai Municipal Hospital were included. All subjects underwent overnight in-laboratory polysomnography (PSG) and were assessed using a neuropsychological scale. Standard fast fourier transform (FFT) was used to obtain the electroencephalogram (EEG) power spectral density curve, and to calculate the delta, theta, alpha, and beta relative power and the ratio between slow and fast frequencies. Binary logistic regression was used to assess the risk factors for cognitive impairment in patients who had OSA but no dementia. Correlation analysis was performed to determine the relationship between qEEG and cognitive impairment. RESULTS: A total of 175 participants without dementia who met the inclusion criteria were included in this study. There were 137 patients with OSA, including 76 with mild cognitive impairment (OSA + MCI), 61 without mild cognitive impairment (OSA-MCI), and 38 participants without OSA (non-OSA). The relative theta power in the frontal lobe in stage 2 of non-rapid eye movement sleep (NREM 2) in OSA + MCI was higher than that in OSA-MCI (P = 0.038) and non-OSA (P = 0.018). Pearson correlation analysis showed that the relative theta power in the frontal lobe in NREM 2 was negatively correlated with Mini-Mental State Examination (MMSE) scores, Montreal Cognitive Assessment (MoCA) Beijing version scores, and MoCA subdomains scores (visual executive function, naming, attention, language, abstraction, delayed recall and orientation) outside language. CONCLUSIONS: In patients who had OSA but no dementia, the EEG slower frequency power increased. The relative theta power in the frontal lobe in NREM 2 was associated with MCI of patients with OSA. These results suggest that the slowing of theta activity may be one of the neurophysiological changes in the early stage of cognitive impairment in patients with OSA.

Evaluation of Drug Abuse on Brain Function using Power Spectrum Analysis of Electroencephalogram Signals in Methamphetamine, Opioid, Cannabis, and Multi-Drug Abuser Groups.

Background: The effect of different types of substances on brain function is still challenging; however, many studies have shown the functional and structural damage to the brain under influence of substance abuse. Objective: This study aimed to quantitatively compare the effect of opioid (Op), methamphetamine (Meth), cannabis (Can), and simultaneous methamphetamine and opioid (Multi-Drug (MD)) abuse on brain function. Furthermore, the impacts of pure Op and Meth abuse were considered with simultaneous substance abuse. Material and Methods: In this descriptive study, the electroencephalogram (EEG) signal was recorded from 52 participants in the Meth, Op, Can, and MD abusers, and the Healthy Control (HC) groups at rest state. EEG data were analyzed on the frequency domain with electrode-based, cortex-based, and hemisphere-based approaches. Results: However, the power spectrum in the delta band in the Op group, the gamma band in the Can group, and the gamma and beta bands in the MD group more significantly increased compared to the HC group, the power spectrum values in the Meth group reduced in the alpha, beta, and gamma bands. Moreover, the power spectrum values in the MD group more significantly higher than the Meth and Op groups in the beta and gamma bands. Conclusion: Since substance abuse in different types caused various changes in frequency components, the different power spectrum bands analysis in abusers can be reasonable to apply as a biomarker to detect the drug types.

Electroencephalographic power spectrum changes in cerebral small vessel disease combined with cognitive dysfunction and its relationship with neutrophil/lymphocyte ratio and its clinical value - a pilot study.

Objective: The study aimed to explore the changes in the electrical power spectrum of the brain and its correlation with neutrophil/lymphocyte ratio (NLR) in patients with cognitively impaired cerebral small vessel disease (CSVD) and to explore its clinical application. Methods: A total of 61 patients with CSVD who attended the People's Hospital of Shaanxi Province from September 2021 to September 2022 were divided into the group with cognitive impairment (cerebral small vascular with cognitive impairment, CSVCI group, n = 29) and the group without cognitive impairment (CSVD group, n = 32) based on the Montreal Cognitive Assessment Scale (MoCA) score, while 20 healthy subjects were recruited as the control group (healthy control, HC group). EEG was performed in the three groups, and the difference in whole brain quantitative EEG power spectral density (PSD) was calculated and compared between the three groups. Results: The PSD values in the δ and θ bands of the CSVCI group were higher than those of the CSVD group, while the PSD values in the α band were lower than those of the CSVD and HC groups. In addition, PSD values in the δ-band in the CSVD group were lower than those in the HC group (all p < 0.05). Multifactorial logistic regression showed that reduced α-band global average PSD and low years of education were independent risk factors for cognitive impairment in patients with CSVD (p < 0.05). In patients with cerebral small-vessel disease, α-band PSD was positively and δ-band PSD negatively correlated with MoCA score, and paraventricular, deep white matter, and total Fazekas scores were negatively correlated with MoCA score. Furthermore, θ-band PSD is positively correlated with NLR (all p < 0.05). Conclusion: EEG activity was slowed down in patients with CSVD with cognitive impairment. The α-band global mean PSD values independently affected the occurrence of cognitive impairment in CSVD patients beyond the Fazekas score. NLR may be one of the mechanisms leading to the slowing down of the EEG, which can be used as an objective indicator for the early prediction of cognitive impairment but still needs to be clarified by further studies.

EEG-Validated Photobiomodulation Treatment of Dementia-Case Study.

In this article, we perform a case study of the impact of photobiomodulation (PBM) on brain power spectrum and connectivity in an elderly person with a Self Administered Gerocognitive Exam (SAGE) score indicating probable memory and thinking disorder. First, we designed and realized the prototype of a near-infrared (NIR) device for PBM. Analysing the alpha band of the power spectrum, we found a positive long-term effect in nine out of sixteen electrodes in the eyes-open condition (OE) and in twelve out of sixteen electrodes in the eyes-closed condition (CE), while in the theta band, a positive long-term effect was found in nine out of sixteen electrodes for OE and seven out of sixteen electrodes for CE. When considering the theta-alpha ratio (TAR), the positive long-term effect is found on thirteen of sixteen electrodes for OE and on fourteen of sixteen electrodes for CE. A connectivity analysis using the imaginary component of the complex Pearson correlation coefficient (imCPCC) was also performed, and a global efficiency measure based on connectivity matrices with thresholds was calculated. The global efficiency calculated for the long-term effect was higher than before stimulation by a factor of 5.24 for the OE condition and by a factor of 1.25 for the CE condition. This case study suggests that PBM could have positive effects on improving desired brain activity, measured as improvement in power spectrum and connectivity measures in theta and alpha bands, for elderly people with memory and thinking disorders.

Computational Approaches and Tools as Applied to the Study of Rhythms and Chaos in Biology.

The temporal dynamics in biological systems displays a wide range of behaviors, from periodic oscillations, as in rhythms, bursts, long-range (fractal) correlations, chaotic dynamics up to brown and white noise. Herein, we propose a comprehensive analytical strategy for identifying, representing, and analyzing biological time series, focusing on two strongly linked dynamics: periodic (oscillatory) rhythms and chaos. Understanding the underlying temporal dynamics of a system is of fundamental importance; however, it presents methodological challenges due to intrinsic characteristics, among them the presence of noise or trends, and distinct dynamics at different time scales given by molecular, dcellular, organ, and organism levels of organization. For example, in locomotion circadian and ultradian rhythms coexist with fractal dynamics at faster time scales. We propose and describe the use of a combined approach employing different analytical methodologies to synergize their strengths and mitigate their weaknesses. Specifically, we describe advantages and caveats to consider for applying probability distribution, autocorrelation analysis, phase space reconstruction, Lyapunov exponent estimation as well as different analyses such as harmonic, namely, power spectrum; continuous wavelet transforms; synchrosqueezing transform; and wavelet coherence. Computational harmonic analysis is proposed as an analytical framework for using different types of wavelet analyses. We show that when the correct wavelet analysis is applied, the complexity in the statistical properties, including temporal scales, present in time series of signals, can be unveiled and modeled. Our chapter showcase two specific examples where an in-depth analysis of rhythms and chaos is performed: (1) locomotor and food intake rhythms over a 42-day period of mice subjected to different feeding regimes; and (2) chaotic calcium dynamics in a computational model of mitochondrial function.

Quick identification of prostate cancer by wavelet transform-based photoacoustic power spectrum analysis.

Pathology is currently the gold standard for grading prostate cancer (PCa). However, pathology takes considerable time to provide a final result and is significantly dependent on subjective judgment. In this study, wavelet transform-based photoacoustic power spectrum analysis (WT-PASA) was used for grading PCa with different Gleason scores (GSs). The tumor region was accurately identified via wavelet transform time-frequency analysis. Then, a linear fitting was conducted on the photoacoustic power spectrum curve of the tumor region to obtain the quantified spectral parameter slope. The results showed that high GSs have small glandular cavity structures and higher heterogeneity, and consequently, the slopes at both 1210 nm and 1310 nm were high (p < 0.01). The classification accuracy of the PA time frequency spectrum (PA-TFS) of tumor region using ResNet-18 was 89% at 1210 nm and 92.7% at 1310 nm. Further, the testing time was less than 7 mins. The results demonstrated that identification of PCa can be rapidly and objectively realized using WT-PASA.

Quantifying the Influence of Surface Texture and Shape on Structure from Motion 3D Reconstructions.

In general, optical methods for geometrical measurements are influenced by the surface properties of the examined object. In Structure from Motion (SfM), local variations in surface color or topography are necessary for detecting feature points for point-cloud triangulation. Thus, the level of contrast or texture is important for an accurate reconstruction. However, quantitative studies of the influence of surface texture on geometrical reconstruction are largely missing. This study tries to remedy that by investigating the influence of object texture levels on reconstruction accuracy using a set of reference artifacts. The artifacts are designed with well-defined surface geometries, and quantitative metrics are introduced to evaluate the lateral resolution, vertical geometric variation, and spatial-frequency information of the reconstructions. The influence of texture level is compared to variations in capturing range. For the SfM measurements, the ContextCapture software solution and a 50 Mpx DSLR camera are used. The findings are compared to results using calibrated optical microscopes. The results show that the proposed pipeline can be used for investigating the influence of texture on SfM reconstructions. The introduced metrics allow for a quantitative comparison of the reconstructions at varying texture levels and ranges. Both range and texture level are seen to affect the reconstructed geometries although in different ways. While an increase in range at a fixed focal length reduces the spatial resolution, an insufficient texture level causes an increased noise level and may introduce errors in the reconstruction. The artifacts are designed to be easily replicable, and by providing a step-by-step procedure of our testing and comparison methodology, we hope that other researchers will make use of the proposed testing pipeline.

Controlling Alzheimer's Disease Through the Deep Brain Stimulation to Thalamic Relay Cells.

Experimental and clinical studies have shown that the technique of deep brain stimulation (DBS) plays a potential role in the regulation of Alzheimer's disease (AD), yet it still desires for ongoing studies including clinical trials, theoretical approach and action mechanism. In this work, we develop a modified thalamo-cortico-thalamic (TCT) model associated with AD to explore the therapeutic effects of DBS on AD from the perspective of neurocomputation. First, the neuropathological state of AD resulting from synapse loss is mimicked by decreasing the synaptic connectivity strength from the Inter-Neurons (IN) neuron population to the Thalamic Relay Cells (TRC) neuron population. Under such AD condition, a specific deep brain stimulation voltage is then implanted into the neural nucleus of TRC in this TCT model. The symptom of AD is found significantly relieved by means of power spectrum analysis and nonlinear dynamical analysis. Furthermore, the therapeutic effects of DBS on AD are systematically examined in different parameter space of DBS. The results demonstrate that the controlling effect of DBS on AD can be efficient by appropriately tuning the key parameters of DBS including amplitude A, period P and duration D. This work highlights the critical role of thalamus stimulation for brain disease, and provides a theoretical basis for future experimental and clinical studies in treating AD.

Conditional adaptive Bayesian spectral analysis of replicated multivariate time series.

This article introduces a flexible nonparametric approach for analyzing the association between covariates and power spectra of multivariate time series observed across multiple subjects, which we refer to as multivariate conditional adaptive Bayesian power spectrum analysis (MultiCABS). The proposed procedure adaptively collects time series with similar covariate values into an unknown number of groups and nonparametrically estimates group-specific power spectra through penalized splines. A fully Bayesian framework is developed in which the number of groups and the covariate partition defining the groups are random and fit using Markov chain Monte Carlo techniques. MultiCABS offers accurate estimation and inference on power spectra of multivariate time series with both smooth and abrupt dynamics across covariate by averaging over the distribution of covariate partitions. Performance of the proposed method compared with existing methods is evaluated in simulation studies. The proposed methodology is used to analyze the association between fear of falling and power spectra of center-of-pressure trajectories of postural control while standing in people with Parkinson's disease.

Relationships Between Cardiovascular Autonomic Profile and Work Ability in Patients With Pure Autonomic Failure.

Pure autonomic failure (PAF) is a rare disorder belonging to the group of synucleinopathies, characterized by autonomic nervous system degeneration. Severe orthostatic intolerance with recurrent syncope while standing are the two most disabling manifestations. Symptoms may start at middle age, thus affecting people at their working age. The aims of this study were to evaluate the autonomic and work ability impairment of a group of PAF patients and assess the relationships between cardiovascular autonomic control and work ability in these patients. Eleven PAF patients (age 57.3 ± 6.7 years), engaged in work activity, participated in the study. They completed the Composite Autonomic Symptom Score (COMPASS-31, range 0 no symptom-100 maximum symptom intensity) and Work Ability questionnaires (Work Ability Index, WAI, range 7-49; higher values indicate better work ability and lower values indicating unsatisfactory or jeopardized work ability). Electrocardiogram, blood pressure and respiratory activity were continuously recorded for 10 min while supine and during 75° head-up tilt (HUT). Autoregressive spectral analysis of cardiac cycle length approximated as the time distance between two consecutive R-wave peaks (RR) and systolic arterial pressure (SAP) variabilities provided the power in the high frequency (HF, 0.15-0.40 Hz) and low frequency (LF, 0.04-0.15 Hz) bands of RR and SAP variabilities. Cardiac sympatho-vagal interaction was assessed by LF to HF ratio (LF/HF), while the LF power of SAP (LFSAP) quantified the vascular sympathetic modulation. Changes in cardiovascular autonomic indexes induced by HUT were calculated as the delta (Δ) between HUT and supine resting positions. Spearman correlation analysis was applied. PAF patients were characterized by a moderate autonomic dysfunction (COMPASS-31 total score 47.08 ± 20.2) and by a reduction of work ability (WAI 26.88 ± 10.72). Direct significant correlations were found between WAI and ΔLFRR (r = 0.66, p = 0.03) and ΔLF/HFRR (r = 0.70, p = 0.02). Results indicate that patients who were better able to modulate heart rate, as revealed by a greater cardiac sympathetic increase and/or vagal withdrawal during the orthostatic stimulus, were those who reported higher values of WAI. This finding could be relevant to propose new strategies in the occupational environment to prevent early retirement or to extend the working life of these patients.

Adaptive variational mode decomposition and its application to multi-fault detection using mechanical vibration signals.

Vibration-based feature extraction of multiple transient fault signals is a challenge in the field of rotating machinery fault diagnosis. Variational mode decomposition (VMD) has great potential for multiple faults decoupling because of its equivalent filtering characteristics. However, the two key hyper-parameters of VMD, i.e., the number of modes and balancing parameter, require to be predefined, thereby resulting in sub-optimal decomposition performance. Although some studies focused on the adaptive parameter determination, the problems in these improved methods like mode redundancy or being sensitive to random impacts still need to be solved. To overcome these drawbacks, an adaptive variational mode decomposition (AVMD) method is developed in this paper. In the proposed method, a novel index called syncretic impact index (SII) is firstly introduced for better evaluation of the complex impulsive fault components of signals. It can exclude the effects of interference terms and concentrate on the fault impacts effectively. The optimal parameters of VMD are selected based on the index SII through the artificial bee colony (ABC) algorithm. The envelope power spectrum, proved to be more capable for fault feature extraction than the envelope spectrum, is applied in this study. Analysis on simulated signals and two experimental applications based on the proposed method demonstrates its effectiveness over other existing methods. The results indicate that the proposed method outperforms in separating impulsive multi-fault signals, thus being an efficient method for multi-fault diagnosis of rotating machines.

Haptic Perception in Extreme Obesity: qEEG Study Focused on Predictive Coding and Body Schema.

Haptic perception (HP) is a perceptual modality requiring manual exploration to elaborate the physical characteristics of external stimuli through multisensory integrative cortical pathways. Cortical areas exploit processes of predictive coding that collect sensorial inputs to build and update internal perceptual models. Modifications to the internal representation of the body have been associated with eating disorders. In the light of this, obese subjects were selected as a valid experimental model to explore predictive coding in haptic perception. To this purpose, we performed electroencephalographic (EEG) continuous recordings during a haptic task in normally weighted versus obese subjects. EEG power spectra were analyzed in different time intervals. The quality of haptic performance in the obese group was poorer than in control subjects, though exploration times were similar. Spectral analysis showed a significant decrease in theta, alpha and beta frequencies in the right temporo-parietal areas of obese group, whereas gamma bands significantly increased in the left frontal areas. These results suggest that severe obesity could be characterized by an impairment in haptic performances and an altered activation of multisensory integrative cortical areas. These are involved in functional coding of external stimuli, which could interfere with the ability to process a predicted condition.

[EEG Signal Analysis Methods and Their Applications].

EEG is a weak physiological electrical signal, which has important value in clinical and laboratory research. This paper mainly introduces several common methods of EEG signal processing, including power spectrum analysis, time-frequency analysis, bispectral analysis, etc, it mainly introduces their principles and applications in EEG signal processing, and provides methods and approaches for studying EEG.