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While the impact of task difficulty level on working memory (WM) has been addressed using a visual N-Back task, only few studies considered the effect of stimulus blur level. We investigated the effect of N-Back difficultly level, blur level as well as stimulus type using, for the first time, electroencephalogram (EEG) responses. We recruited 18 healthy young individuals and recorded their behavioural and EEG responses when participating in a single N-Back session. Our results showed interactions between blur level and stimulus type. Frequency band differences were found for all three stimulus types (faces, objects and natural scenes) when comparing no blur, modest, and severe blur. Additionally, when comparing stimuli, we found differences in higher frequency bands for faces compared to objects and natural scenes, for all three N-Back levels. Only in the frontal areas, differences in higher frequency bands were identified in objects compared to faces and natural scenes. These results offer new insights into the neural dynamics underlying WM. [ABSTRACT FROM AUTHOR]

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Fatigue affects approximately 80% of people with Multiple Sclerosis (PwMS) and can impact several domains of daily life. However, the neural underpinnings of fatigue in MS are still not completely clear. The aim of our study was to investigate the spontaneous large-scale networks functioning associated with fatigue in PwMS using the EEG microstate approach with a spectral decomposition. Forty-three relapsing–remitting MS patients and twenty-four healthy controls (HCs) were recruited. All participants underwent an administration of Modified Fatigue Impact scale (MFIS) and a 15-min resting-state high-density EEG recording. We compared the microstates of healthy subjects, fatigued (F-MS) and non-fatigued (nF-MS) patients with MS; correlations with clinical and behavioral fatigue scores were also analyzed. Microstates analysis showed six templates across groups and frequencies. We found that in the F-MS emerged a significant decrease of microstate F, associated to the salience network, in the broadband and in the beta band. Moreover, the microstate B, associated to the visual network, showed a significant increase in fatigued patients than healthy subjects in broadband and beta bands. The multiple linear regression showed that the high cognitive fatigue was predicted by both an increase and decrease, respectively, in delta band microstate B and beta band microstate F. On the other hand, higher physical fatigue was predicted with lower occurrence microstate F in beta band. The current findings suggest that in MS the higher level of fatigue might be related to a maladaptive functioning of the salience and visual network. [ABSTRACT FROM AUTHOR]

To date, it is largely unknown how frequency range of neural oscillations measured with EEG is related to functional connectivity. To address this question, we investigated frequency-dependent directed functional connectivity among the structures of mesial and anterior temporal network including amygdala, hippocampus, temporal pole and parahippocampal gyrus in the living human brain. Intracranial EEG recording was obtained from 19 consecutive epilepsy patients with normal anterior mesial temporal MR imaging undergoing intracranial presurgical epilepsy diagnostics with multiple depth electrodes. We assessed intratemporal bidirectional functional connectivity using several causality measures such as Granger causality (GC), directed transfer function (DTF) and partial directed coherence (PDC) in a frequency-specific way. In order to verify the obtained results, we compared the spontaneous functional networks with intratemporal effective connectivity evaluated by means of SPES (single pulse electrical stimulation) method. The overlap with the evoked network was found for the functional connectivity assessed by the GC method, most prominent in the higher frequency bands (alpha, beta and low gamma), yet vanishing in the lower frequencies. Functional connectivity assessed by means of DTF and PCD obtained a similar directionality pattern with the exception of connectivity between hippocampus and parahippocampal gyrus which showed opposite directionality of predominant information flow. Whereas previous connectivity studies reported significant divergence between spontaneous and evoked networks, our data show the role of frequency bands for the consistency of functional and evoked intratemporal directed connectivity. This has implications for the suitability of functional connectivity methods in characterizing local brain circuits. [ABSTRACT FROM AUTHOR]

This study aimed to perform a comparative study of the functional connectivity of different frequency bands for the identification of resting and arithmetic cognitive workload EEG using machine learning techniques. Functional connectivity was calculated from preprocessed EEGs for both rest and task states in 5 EEG sub-bands: alpha (8–13 Hz), theta (4–8 Hz), delta (1–4 Hz), gamma (30–45 Hz), and beta (13–30 Hz). This was done through Weighted Phase Lag Index (WPLI). After that, PCA was applied to the calculated feature vectors to decrease the dimensionality of the feature space. Eventually, the normalized chosen features were used as input for different machine learning-based classification models, and the performance was assessed through the leave-one-subject cross-validation (LOSOCV) algorithm. Experimental results showed that the classification results on the basis of the connectivity features of delta, theta, alpha, beta, and gamma frequency bands were 90.27%, 77.78%, 62.50%, 62.50%, and 76.39%, respectively. The obtained results showed that used machine learning models and functional connectivity technique are successfully applied to detect mental workload from rest- and task-EEG. In summary, EEG functional connectivity in the delta frequency is a potent tool for comprehending the neural basis of mental workload and has significant applications in various fields. [ABSTRACT FROM AUTHOR]

EEG-based emotion recognition is becoming crucial in brain-computer interfaces (BCI). Currently, most researches focus on improving accuracy, while neglecting further research on the interpretability of models, we are committed to analyzing the impact of different brain regions and signal frequency bands on emotion generation based on graph structure. Therefore, this paper proposes a method named Dual Attention Mechanism Graph Convolutional Neural Network (DAMGCN). Specifically, we utilize graph convolutional neural networks to model the brain network as a graph to extract representative spatial features. Furthermore, we employ the self-attention mechanism of the Transformer model which allocates more electrode channel weights and signal frequency band weights to important brain regions and frequency bands. The visualization of attention mechanism clearly demonstrates the weight allocation learned by DAMGCN. During the performance evaluation of our model on the DEAP, SEED, and SEED-IV datasets, we achieved the best results on the SEED dataset, showing subject-dependent experiments' accuracy of 99.42% and subjectindependent experiments' accuracy of 73.21%. The results are demonstrably superior to the accuracies of most existing models in the realm of EEG-based emotion recognition. [ABSTRACT FROM AUTHOR]

Mental fatigue during driving poses significant risks to road safety, necessitating accurate assessment methods to mitigate potential hazards. This study explores the impact of individual variability in brain networks on driving fatigue assessment, hypothesizing that subject-specific connectivity patterns play a pivotal role in understanding fatigue dynamics. By conducting a linear regression analysis of subject-specific brain networks in different frequency bands, this research aims to elucidate the relationships between frequency-specific connectivity patterns and driving fatigue. As such, an EEG sustained driving simulation experiment was carried out, estimating individuals' brain networks using the Phase Lag Index (PLI) to capture shared connectivity patterns. The results unveiled notable variability in connectivity patterns across frequency bands, with the alpha band exhibiting heightened sensitivity to driving fatigue. Individualized connectivity analysis underscored the complexity of fatigue assessment and the potential for personalized approaches. These findings emphasize the importance of subject-specific brain networks in comprehending fatigue dynamics, while providing sensor space minimization, advocating for the development of efficient mobile sensor applications for real-time fatigue detection in driving scenarios. [ABSTRACT FROM AUTHOR]

Geomagnetic vertical intensity polarization is a method with a clear mechanism, mature processing methods, and a strong ability to extract anomalous information in the quantitative analysis of seismogenic geomagnetic disturbances. The existing analyses of geomagnetic vertical intensity polarization are all based on the 5~100 s frequency band without refinement of the partitioning process. Although many successful results have been obtained, there are still two problems in the process of extracting anomalies: the geomagnetic anomalies that satisfy the determination criteria are still high in occurrence frequency; and the anomalies are distributed over too large an area in space, which leads to difficulties in determining the location of the epicenter. In this study, based on observations from western China, where fluxgate observation points are positioned in areas with frequent, densely distributed medium-strength earthquakes, we refined the frequency bands of geomagnetic vertical intensity polarization, recalculated the spatial and temporal evolution characteristics of geomagnetic disturbances before earthquakes, and improved the crossover frequency anomaly prediction index while promoting the application of the method in earthquake forecasting. [ABSTRACT FROM AUTHOR]

Pulmonary diseases are the third biggest cause of deaths worldwide. A prominent method to detect these diseases is the observation of lung sound signals. There is an increasing need for an efficient technique that can automatically diagnose such diseases with high accuracy. In this paper, two popular public datasets are considered, and every lung sound signal is decomposed into 8 frequency bands using rectangular zero-phase filters. Features are extracted from every band, including energy, kurtosis, mean absolute deviation and Lp norm. The extracted features are utilized for classification using machine learning schemes. The proposed method achieves 99.9% accuracy for multi-class classification on the combined dataset, and for binary classification, we have evaluated normal signal versus pathogenic signal, which is found to be 100% accurate for most of the diseases. High accuracy is obtained for the individual datasets as well. Top 20 features selected using minimum redundancy maximum relevance algorithm also yield 98.6% accuracy. Therefore, the proposed method can be easily deployed in real-time systems. [ABSTRACT FROM AUTHOR]

This work offers the radiation insight of a compact modified monopole rectangular printed antenna suitable for 5G applications. A Double copper cladded RT Duroid 5880 material sheet with dimensions 4.4x5.0x0.5mm³ is chosen as the base substrate in the offered design with a loss tangent of 0.004 and a dielectric constant of 2.2. In the provided strategy lower-left corner of a rectangular printed antenna is made in the form of a smooth circular curve. After extensive simulation, a circular slot with an elliptical patch is incorporated into this monopole antenna. The designed antenna parameters are adjusted to cover the impedance bandwidth from 21.7 GHz to more than 70.0 GHz. Three resonances at 28.0 GHz, 38.1 GHz and 60.2 GHz were observed in three bands allocated for 5G communication applications. The efficiency and gain values found sustained in the entire bandwidth range. Stable and desired radiation patterns at all three resonant frequencies 28/38/60 GHz prove the suitability of this antenna for recent 5G applications. [ABSTRACT FROM AUTHOR]

In recent years, there has been many efforts to establish a comprehensive theoretical framework explaining the working mechanisms involved in perception-action integration. This framework stresses the importance of the immediate past on mechanisms supporting perception-action integration. The present study investigates the neurophysiological principles of dynamic perception-action bindings, particularly considering the influence of the immediate history on action control mechanisms. For this purpose, we conducted an established stimulus-response binding paradigm during EEG recording. The SR-task measures stimulus-response binding in terms of accuracy and reaction time differences depending on the degree of feature overlap between conditions. Alpha, beta and theta band activity in distinct time domains as well as associated brain regions were investigated applying time-frequency analyses, a beamforming approach as well as correlation analyses. We demonstrate, for the first time, interdependencies of neuronal processes relying on the immediate past. The reconfiguration of an action seems to overwrite immediately preceding processes. The analyses revealed modulations of theta (TBA), alpha (ABA) and beta band activity (BBA) in connection with fronto-temporal structures supporting the theoretical assumptions of the considered conceptual framework. The close interplay of attentional modulation by gating irrelevant information (ABA) and binding and retrieval processes (TBA) is reflected by the correlation of ABA in all pre-probe-intervals with post-probe TBA. Likewise, the role of BBA in maintaining the event file until retrieval is corroborated by BBA preceding the TBA-associated retrieval of perception-action codes. Following action execution, TBA shifted towards visual association cortices probably reflecting preparation for upcoming information, while ABA and BBA continue to reflect processes of attentional control and information selection for goal-directed behavior. The present work provides the first empirical support for concepts about the neurophysiological mechanisms of dynamic management of perception and action.

Mental fatigue during driving poses significant risks to road safety, necessitating accurate assessment methods to mitigate potential hazards. This study explores the impact of individual variability in brain networks on driving fatigue assessment, hypothesizing that subject-specific connectivity patterns play a pivotal role in understanding fatigue dynamics. By conducting a linear regression analysis of subject-specific brain networks in different frequency bands, this research aims to elucidate the relationships between frequency-specific connectivity patterns and driving fatigue. As such, an EEG sustained driving simulation experiment was carried out, estimating individuals’ brain networks using the Phase Lag Index (PLI) to capture shared connectivity patterns. The results unveiled notable variability in connectivity patterns across frequency bands, with the alpha band exhibiting heightened sensitivity to driving fatigue. Individualized connectivity analysis underscored the complexity of fatigue assessment and the potential for personalized approaches. These findings emphasize the importance of subject-specific brain networks in comprehending fatigue dynamics, while providing sensor space minimization, advocating for the development of efficient mobile sensor applications for real-time fatigue detection in driving scenarios.

• Spontaneous neural activity of GHD children was explored in sub-frequency bands. • ReHo abnormalities in GHD children are related to specific frequency bands. • The results provide neuroimaging bases for understanding pathophysiology of GHD. Abnormal spontaneous neural activity in children with growth hormone deficiency (GHD) has been found in previous resting-state functional magnetic resonance imaging (rs-fMRI) studies. Nevertheless, the spontaneous neural activity of GHD in different frequency bands is still unclear. Here, we combined rs-fMRI and regional homogeneity (ReHo) methods to analyze the spontaneous neural activity of 26 GHD children and 15 healthy controls (HCs) with age- and sex-matching in four frequency bands: slow-5 (0.014–0.031 Hz), slow-4 (0.031–0.081 Hz), slow-3 (0.081–0.224 Hz), and slow-2 (0.224–0.25 Hz). In the slow-5 band, GHD children compared with HCs displayed higher ReHo in the left dorsolateral part of the superior frontal gyrus, triangular part of the inferior frontal gyrus, precentral gyrus and middle frontal gyrus, and right angular gyrus, while lower ReHo in the right precentral gyrus, and several medial orbitofrontal regions. In the slow-4 band, GHD children relative to HCs revealed increased ReHo in the right middle temporal gyrus, whereas reduced ReHo in the left superior parietal gyrus, right middle occipital gyrus, and bilateral medial parts of the superior frontal gyrus. In the slow-2 band, compared with HCs, GHD children showed increased ReHo in the right anterior cingulate gyrus, and several prefrontal regions, while decreased ReHo in the left middle occipital gyrus, and right fusiform gyrus and anterior cingulate gyrus. Our findings demonstrate that regional brain activity in GHD children exhibits extensive abnormalities, and these abnormalities are related to specific frequency bands, which may provide bases for understanding its pathophysiology significance. [ABSTRACT FROM AUTHOR]

Sugammadex has been associated with increases in the bispectral index (BIS). We evaluated the effects of sugammadex administration on quantitative electroencephalographic (EEG) and electromyographic (EMG) measures. We performed a prospective observational study of adult male patients undergoing robot-assisted radical prostatectomy. All patients received a sevoflurane-based general anaesthetic and a continuous infusion of rocuronium, which was reversed with 2 mg kg−1 of sugammadex i.v. BIS, EEG, and EMG measures were captured with the BIS Vista™ monitor. Twenty-five patients were included in this study. Compared with baseline, BIS increased at 4–6 min (β coefficient: 3.63; 95% confidence interval [CI]: 2.22–5.04; P <0.001), spectral edge frequency 95 (SEF95) increased at 2–4 min (β coefficient: 0.29; 95% CI: 0.05–0.52; P =0.016) and 4–6 min (β coefficient: 0.71; 95% CI: 0.47–0.94; P <0.001), and EMG increased at 4–6 min (β coefficient: 1.91; 95% CI: 1.00–2.81; P <0.001) after sugammadex administration. Compared with baseline, increased beta power was observed at 2–4 min (β coefficient: 93; 95% CI: 1–185; P =0.046) and 4–6 min (β coefficient: 208; 95% CI: 116–300; P <0.001), and decreased delta power was observed at 4–6 min (β coefficient: −526.72; 95% CI: −778 to −276; P <0.001) after sugammadex administration. Neither SEF95 nor frequency band data analysis adjusted for EMG showed substantial differences. None of the patients showed clinical signs of awakening. After neuromuscular block reversal with 2 mg kg−1 sugammadex, BIS, SEF95, EMG, and beta power showed small but statistically significant increases over time, while delta power decreased. [ABSTRACT FROM AUTHOR]

Geomagnetic vertical intensity polarization is a method with a clear mechanism, mature processing methods, and a strong ability to extract anomalous information in the quantitative analysis of seismogenic geomagnetic disturbances. The existing analyses of geomagnetic vertical intensity polarization are all based on the 5~100 s frequency band without refinement of the partitioning process. Although many successful results have been obtained, there are still two problems in the process of extracting anomalies: the geomagnetic anomalies that satisfy the determination criteria are still high in occurrence frequency; and the anomalies are distributed over too large an area in space, which leads to difficulties in determining the location of the epicenter. In this study, based on observations from western China, where fluxgate observation points are positioned in areas with frequent, densely distributed medium-strength earthquakes, we refined the frequency bands of geomagnetic vertical intensity polarization, recalculated the spatial and temporal evolution characteristics of geomagnetic disturbances before earthquakes, and improved the crossover frequency anomaly prediction index while promoting the application of the method in earthquake forecasting.

The majority of electroencephalographic (EEG) and magnetoencephalographic (MEG) studies filter and analyse neural signals in specific frequency ranges, known as “canonical” frequency bands. However, this segmentation, is not exempt from limitations, mainly due to the lack of adaptation to the neural idiosyncrasies of each individual. In this study, we introduce a new data-driven method to automatically identify frequency ranges based on the topological similarity of the frequency-dependent functional neural network. The resting-state neural activity of 195 cognitively healthy subjects from three different databases (MEG: 123 subjects; EEG1: 27 subjects; EEG2: 45 subjects) was analysed. In a first step, MEG and EEG signals were filtered with a narrow-band filter bank (1 Hz bandwidth) from 1 to 70 Hz with a 0.5 Hz step. Next, the connectivity in each of these filtered signals was estimated using the orthogonalized version of the amplitude envelope correlation to obtain the frequency-dependent functional neural network. Finally, a community detection algorithm was used to identify communities in the frequency domain showing a similar network topology. We have called this approach the “Connectivity-based Meta-Bands” (CMB) algorithm. Additionally, two types of synthetic signals were used to configure the hyper-parameters of the CMB algorithm. We observed that the classical approaches to band segmentation are partially aligned with the underlying network topologies at group level for the MEG signals, but they are missing individual idiosyncrasies that may be biasing previous studies, as revealed by our methodology. On the other hand, the sensitivity of EEG signals to reflect this underlying frequency-dependent network structure is limited, revealing a simpler frequency parcellation, not aligned with that defined by the “canonical” frequency bands. To the best of our knowledge, this is the first study that proposes an unsupervised band segmentation method based on the topological similarity of functional neural network across frequencies. This methodology fully accounts for subject-specific patterns, providing more robust and personalized analyses, and paving the way for new studies focused on exploring the frequency-dependent structure of brain connectivity.

Automatic emotion recognition using electroencephalogram (EEG) has obtained a wide range of attention in the domain of human-computer interaction (HCI) owing to the notable differences in brain activities in the event of different types of emotions. In this paper, a novel emotion recognition approach is proposed based on a deep learning scheme utilizing the temporal, spatial, and frequency effects of the EEG signal. As neural firing provides a pathway to elicit emotions, temporal, spatial, and frequency sub-band analysis of EEG signals uncovers salient information to categorize different classes of emotions. In this regard, temporal data from each channel are divided into major spectral bands and 2D signal matrices are constructed by combining the temporal information of different frequency band signals. After concatenating all signal matrices obtained from the available channels, a 3D feature space is obtained, which can better characterize emotion variations and, thus, better classification performance is obtained. The feature space is applied to a 2D deep neural network where the band information is passed to the depth dimension of the neural network. In order to highlight the important channels, a channel attention mechanism is proposed with the neural network to distribute the weights among the channels according to the contribution. Hence, the modified feature space effectively captures distinctive information about specific channels in the context of emotion recognition. In this study, detailed and extensive experimentations are carried out on a publicly available DEAP dataset and a very satisfactory performance is obtained for the valence and the arousal domain in 2-class scenario for the subject-dependent case. The average accuracies obtained for valence and arousal domain in binary class problem are 97.06% and 96.93%, respectively.

Objectives: The purpose of this paper is to provide a mini-review covering the recent progress in human and animal studies on local field potentials (LFPs) of major depressive disorder (MDD) and obsessive-compulsive disorder (OCD). Materials and methods: PubMed and EMBASE were searched to identify related studies. Inclusion criteria were (1) reported the LFPs on OCD or MDD, (2) published in English, and (3) human or animal studies. Exclusion criteria were (1) review or meta-analysis or other literature types without original data and (2) conference abstract without full text. Descriptive synthesis of data was performed. Results: Eight studies on LFPs of OCD containing 22 patients and 32 rats were included: seven were observational studies with no controls, and one animal study included a randomized and controlled phase. Ten studies on LFPs of MDD containing 71 patients and 52 rats were included: seven were observational studies with no controls, one study with control, and two animal studies included a randomized and controlled phase. Conclusion: The available studies revealed that different frequency bands were associated with specific symptoms. Low frequency activity seemed to be closely related to OCD symptoms, whereas LFPs findings in patients with MDD were more complicated. However, limitations of recent studies restrict the drawing of definite conclusions. Combined with other measures such as Electroencephalogram, Electrocorticography, or Magnetoencephalography and long-term recordings in various physiological states (rest state, sleep state, task state) could help to improve the understanding of potential mechanisms. [ABSTRACT FROM AUTHOR]

Aging is the inevitable biological process that results in a progressive structural and functional decline associated with alterations in the resting/task-related brain activity, morphology, plasticity, and functionality. In the present study, we analyzed the effects of physiological aging on the human brain through entropy measures of electroencephalographic (EEG) signals. One hundred sixty-one participants were recruited and divided according to their age into young (n = 72) and elderly (n = 89) groups. Approximate entropy (ApEn) values were calculated in each participant for each EEG recording channel and both for the total EEG spectrum and for each of the main EEG frequency rhythms: delta (2–4 Hz), theta (4–8 Hz), alpha 1 (8–11 Hz), alpha 2 (11–13 Hz), beta 1 (13–20 Hz), beta 2 (20–30 Hz), and gamma (30–45 Hz), to identify eventual statistical differences between young and elderly. To demonstrate that the ApEn represents the age-related brain changes, the computed ApEn values were used as features in an age-related classification of subjects (young vs elderly), through linear, quadratic, and cubic support vector machine (SVM). Topographic maps of the statistical results showed statistically significant difference between the ApEn values of the two groups found in the total spectrum and in delta, theta, beta 2, and gamma. The classifiers (linear, quadratic, and cubic SVMs) revealed high levels of accuracy (respectively 93.20 ± 0.37, 93.16 ± 0.30, 90.62 ± 0.62) and area under the curve (respectively 0.95, 0.94, 0.93). ApEn seems to be a powerful, very sensitive–specific measure for the study of cognitive decline and global cortical alteration/degeneration in the elderly EEG activity. [ABSTRACT FROM AUTHOR]

Digital image processing makes it possible to use the most sophisticated methods, so it can provide both more complex performance in simple tasks and the implementation of impossible methods. Digital cameras usually have a special hardware for image processing (specially added to the circuits or other chips) to convert the original data into a standard image file format in the form of a standard image file changes. The Digital filters are used to blur and sharpen visual based digital images. These filters can be done in the space sphere through the frequency field by covering specially designed cores or certain frequency bands. The images are usually stacked before the Fourier is transferred and the overlay filter images show the results of different layer techniques. In this paper, an innovation filter model was proposed interms of color pixels in the images. The proposed model has a highpass filter shows extra edges when zero is compared to the repeat edge. The visual transformations make the basic changes of the film, including scaling, rotation, exchange, reflection and cultivation.

Lane-changing recognition is an important task for advanced driver assistance systems, but is heavily challenged by poor driving habits, such as turning without using turn signals. To address this problem in this study, a lane-changing recognition method using frequency analysis was proposed. First, highest-frequency–based and frequency-bands–based methods were employed to evaluate the three behaviors of left lane changing, lane keeping, and right lane changing. To improve the recognition accuracy, the two methods were fused according to their classification advantages for different behaviors. The fused method was verified by lateral position data incorporating lane features that were manually extracted and annotated from the Next-Generation Simulation dataset. The frequency analysis framework achieved recognition accuracy of 91.8%, 97.4%, and 99.1% in 2, 1, and 0 s, respectively, before the vehicle crossed the lane line, which were significant improvements over the time-domain analysis methods. The proposed method was also validated by real-world road data with promising results. [ABSTRACT FROM AUTHOR]

A compact, reconfigurable antenna supporting multiple wireless services with a minimum number of switches is found lacking in literature and the same became the focus and outcome of this work. It was achieved by designing a Th-Shaped frequency reconfigurable multi-band microstrip planar antenna, based on use of a single switch within the radiating structure of the antenna. Three frequency bands (i.e., 2007-2501 MHz, 3660-3983MHz and 9341-1046 MHz) can be operated with the switch in the ON switch state. In the OFF state of the switch, the antenna operates within the 2577-3280MHz and 9379-1033MHz Bands. The proposed antenna shows an acceptable input impedance match with Voltage Standing Wave Ratio (VSWR) less than 1.2. The peak radiation efficiency of the antenna is 82%. A reasonable gain is obtained from 1.22 to 3.31 dB within the operating bands is achieved. The proposed antenna supports UniversalMobile Telecommunication System (UMTS)-1920 to 2170 MHz, Worldwide Interoperability and Microwave Access (WiMAX)/Wireless Broadband/(Long Term Evolution) LTE2500-2500 to 2690 MHz, Fifth Generation (5G)-2500/3500 MHz, Wireless Fidelity (Wi-Fi)/Bluetooth-2400 to 2480 MHz, and Satellite communication applications in X-Band-8000 to 12000 MHz. The overall planar dimension of the proposed antenna is 40×20mm2. The antennawas designed, along with the parametric study, using Electromagnetic (EM) simulation tool. The antenna prototype is fabricated for experimental validation with the simulated results. The proposed antenna is low profile, tunable, lightweight, cheap to fabricate and highly efficient and hence is deemed suitable for use in modern wireless communication electronic devices. [ABSTRACT FROM AUTHOR]

The process of categorizing sounds into distinct phonetic categories is known as categorical perception (CP). Response times (RTs) provide a measure of perceptual difficulty during labeling decisions (i.e., categorization). The RT is quasi-stochastic in nature due to individuality and variations in perceptual tasks. To identify the source of RT variation in CP, we have built models to decode the brain regions and frequency bands driving fast, medium and slow response decision speeds. In particular, we implemented a parameter optimized convolutional neural network (CNN) to classify listeners' behavioral RTs from their neural EEG data. We adopted visual interpretation of model response using Guided-GradCAM to identify spatial-spectral correlates of RT. Our framework includes (but is not limited to): (i) a data augmentation technique designed to reduce noise and control the overall variance of EEG dataset; (ii) bandpower topomaps to learn the spatial-spectral representation using CNN; (iii) large-scale Bayesian hyper-parameter optimization to find best performing CNN model; (iv) ANOVA and posthoc analysis on Guided-GradCAM activation values to measure the effect of neural regions and frequency bands on behavioral responses. Using this framework, we observe that α − β (10–20 Hz) activity over left frontal, right prefrontal/frontal, and right cerebellar regions are correlated with RT variation. Our results indicate that attention, template matching, temporal prediction of acoustics, motor control, and decision uncertainty are the most probable factors in RT variation. [ABSTRACT FROM AUTHOR]

Aim: To describe the spatial and temporal electrocorticographic (ECoG) changes after middle cerebral artery occlusion (MCAo), including those caused by spreading depolarization (SD) in the pig brain. Methods: The left middle cerebral arteries (MCAs) were clipped in six pigs. The clipping procedure lasted between 8 and 12 min, achieving a permanent occlusion (MCAo). Five-contact ECoG stripes were placed bilaterally over the frontoparietal cortices corresponding to the irrigation territory of the MCA and anterior cerebral artery (ACA). ECoG recordings were performed around 24 h: 1 h before and 23 h after the MCAo, and SDs were quantified. Five-minute ECoG signal segments were sampled before, 5 min, and 4, 8, and 12 h after cerebral artery occlusion and before, during, and after the negative direct current shift of the SDs. The power spectrum of the signals was decomposed into delta, theta, alpha, beta, and gamma bands. Descriptive statistics, Wilcoxon matched-pairs signed-rank tests, and Friedman tests were performed. Results: Electrodes close to the MCAo showed instant decay in all frequency bands and SD onset during the first 5 h. Electrodes far from the MCAo exhibited immediate loss of fast frequencies and progressive decline of slow frequencies with an increased SD incidence between 6 and 14 h. After 8 h, the ACA electrode reported a secondary reduction of all frequency bands except gamma and high SD incidence within 12-17 h. During the SD, all electrodes showed a decline in all frequency bands. After SD passage, frequency band recovery was impaired only in MCA electrodes. Conclusion: ECoG can identify infarct progression and secondary brain injury. Severe disturbances in all the frequency bands are generated in the cortices where the SDs are passing by. [ABSTRACT FROM AUTHOR]

Background: Although increased food cue reactivity is evidenced to be crucial to the development and maintenance of pediatric obesity, virtually nothing is known about the underlying neurophysiological aspects of food cue reactivity in children with obesity. Therefore, this study aimed at investigating neural characteristics in children with overweight or obesity using electroencephalography (EEG). Methods: Electrophysiological brain activity was measured using EEG frequency band analysis in n = 9 children with overweight or obesity versus n = 16 children with normal weight (8–13 years) during the presentation of high- and low-calorie food pictures and images of appealing non-food stimuli. Results: Children with overweight or obesity showed significantly increased relative central beta band activity induced by high-calorie foods and appealing non-food stimuli compared to children with normal weight. Beyond significant effects of the scalp region on EEG activity, non-significant effects of stimulus category or weight status were seen for theta and alpha frequency bands. Conclusions: This study demonstrated elevated beta band activity in children with overweight or obesity when viewing high-calorie food stimuli. Beta band activity may, thus, be a valuable target for neuromodulatory interventions in children with overweight or obesity. [ABSTRACT FROM AUTHOR]

Background: Cortico-striato-thalamo-cortical (CSTC) network alterations are hypothesized to contribute to symptoms of obsessive-compulsive disorder (OCD). To date, very few studies have examined whether CSTC network alterations are present in children with OCD, who are medication naive. Medication-naive pediatric imaging samples may be optimal to study neural correlates of illness and identify brain-based markers, given the proximity to illness onset. Methods: Magnetoencephalography (MEG) data were analyzed at rest, in 18 medication-naive children with OCD (M = 12.1 years ±2.0 standard deviation [SD]; 10 M/8 F) and 13 typically developing children (M = 12.3 years ±2.2 SD; 6 M/7 F). Whole-brain MEG-derived resting-state functional connectivity (rs-fc), for alpha- and gamma-band frequencies were compared between OCD and typically developing (control) groups. Results: Increased MEG-derived rs-fc across alpha- and gamma-band frequencies was found in the OCD group compared to the control group. Increased MEG-derived rs-fc at alpha-band frequencies was evident across a number of regions within the CSTC circuitry and beyond, including the cerebellum and limbic regions. Increased MEG-derived rs-fc at gamma-band frequencies was restricted to the frontal and temporal cortices. Conclusions: This MEG study provides preliminary evidence of altered alpha and gamma networks, at rest, in medication-naive children with OCD. These results support prior findings pointing to the relevance of CSTC circuitry in pediatric OCD and further support accumulating evidence of altered connectivity between regions that extend beyond this network, including the cerebellum and limbic regions. Given the substantial portion of children and youth whose OCD symptoms do not respond to conventional treatments, our findings have implications for future treatment innovation research aiming to target and track whether brain patterns associated with having OCD may change with treatment and/or predict treatment response. [ABSTRACT FROM AUTHOR]

Previous research has shown an interplay between the thalamus and cerebral cortex during NREM sleep in humans, however the directionality of the thalamocortical synchronization is as yet unknown. In this study thalamocortical connectivity during different NREM sleep stages using sleep scalp electroencephalograms and local field potentials from the left and right anterior thalamus was measured in three epilepsy patients implanted with deep brain stimulation electrodes. Connectivity was assessed as debiased weighted phase lag index and granger causality between the thalamus and cortex for the NREM sleep stages N1, N2 and N3. Results showed connectivity was most prominently directed from cortex to thalamus. Moreover, connectivity varied in strength between the different sleep stages, but barely in direction or frequency. These results imply relatively stable thalamocortical connectivity during NREM sleep directed from the cortex to the thalamus. [ABSTRACT FROM AUTHOR]

Accurate diagnosis of Attention-Deficit/Hyperactivity Disorder (ADHD) is a significant challenge. Misdiagnosis has significant negative medical side effects. Due to the complex nature of this disorder, there is no computational expert system for diagnosis. Recently, automatic diagnosis of ADHD by machine learning analysis of brain signals has received an increased attention. This paper aimed to achieve an accurate model to discriminate between ADHD patients and healthy controls by pattern discovery. Event-Related Potentials (ERP) data were collected from ADHD patients and healthy controls. After pre-processing, ERP signals were decomposed and features were calculated for different frequency bands. The classification was carried out based on each feature using seven machine learning algorithms. Important features were then selected and combined. To find specific patterns for each model, the classification was repeated using the proposed patterns. Results indicated that the combination of complementary features can significantly improve the performance of the predictive models. The newly developed features, defined based on band power, were able to provide the best classification using the Generalized Linear Model, Logistic Regression, and Deep Learning with the average accuracy and Receiver operating characteristic curve > %99.85 and > 0.999, respectively. High and low frequencies (Beta, Delta) performed better than the mid, frequencies in the discrimination of ADHD from control. Altogether, this study developed a machine learning expert system that minimises misdiagnosis of ADHD and is beneficial for the evaluation of treatment efficacy. [ABSTRACT FROM AUTHOR]

As the older population continues to expand, there is a growing prevalence of individuals who experience subjective cognitive decline (SCD), characterized by self-reported failures in cognitive function and an increased risk of cognitive impairment. Recognizing that preventive interventions are typically more effective in preclinical stages, current research endeavors to focus on identifying early biological markers of SCD using resting-state electroencephalogram (rsEEG) methods. To do so, a systematic literature review covering the past 20 years was conducted following PRISMA guidelines, in order to consolidate findings on rsEEG frequency bands in individuals with SCD. Pubmed and Web of Science databases were searched for rsEEG studies of people with SCD. Quality assessments were completed using a modified Newcastle-Ottawa scale. A total of 564 articles published from December 2003 to December 2023 were reviewed, and significant aspects of these papers were analyzed to provide a general overview of the research on this technique. After removing unrelated articles, nine articles were selected for the present study. The review emphasizes patterns in frequency band activity, revealing that individuals classified as SCD exhibited increased theta power than healthy controls, but decreased than MCI. However, findings for the alpha, delta, and beta bands were inconsistent, demonstrating variability across studies and highlighting the need for further research. Although the rsEEG of frequency bands emerges as a promising early biomarker, there is a noteworthy need to establish uniform standards and consistent measurement approaches in order to ensure the reliability and comparability of the results obtained in the research. • This systematic review reveals consistent rsEEG patterns in SCD individuals. • Methodological heterogeneity in rsEEG studies underscores need for standardization. • rsEEG of frequency bands emerges as a promising early biomarker of SCD. [ABSTRACT FROM AUTHOR]

This paper presents an in- depth investigation of the silicon substrate characteristics based on frequency-dependent parameters in the through-silicon via (TSV)-based 3-D ICs. It is the first time to define the frequency bands accurately on the ground of a quantitative standard of calibration to represent different characteristics of the silicon substrate. Moreover, by converting the conventional model of a TSV pair into an RC parallel circuit, a simplified model is established which makes the analysis of the silicon substrate concise associated with the frequency variation. To further reveal the influence of silicon substrate on the signal transmission between adjacent TSVs, relevant impedance parameters of the silicon substrate are converted into the impedance ( $Z$ ) matrix for parametric modeling, which is validated by 3D full-wave simulations. This work contributes a systematic examination of the silicon substrate characteristics with the variation of frequency, and provides significant guidance for parametric analysis and modeling in TSV-based 3-D ICs.

Dimensionality reduction and the automatic learning of key features from electroencephalographic (EEG) signals have always been challenging tasks. Variational autoencoders (VAEs) have been used for EEG data generation and augmentation, denoising, and automatic feature extraction. However, investigations of the optimal shape of their latent space have been neglected. This research tried to understand the minimal size of the latent space of convolutional VAEs, trained with spectral topographic EEG head maps of different frequency bands, that leads to the maximum reconstruction capacity of the input and maximum utility for classification tasks. Head maps are generated employing a sliding window technique with a 125ms shift. Person-specific convolutional VAEs are trained to learn latent spaces of varying dimensions while a dense neural network is trained to investigate their utility on a classification task. The empirical results suggest that when VAEs are deployed on spectral topographic maps with shape $32\times 32$ , deployed for 32 electrodes from 2 seconds cerebral activity, they were capable of reducing the input up to almost 99%, with a latent space of 28 means and standard deviations. This did not compromise the salient information, as confirmed by a structural similarity index, and mean squared error between the input and reconstructed maps. Additionally, along the 28 means maximized the utility of latent spaces in the classification task, with an average 0.93% accuracy. This study contributes to the body of knowledge by offering a pipeline for effective dimensionality reduction of EEG data by employing convolutional variational autoencoders.

Background: Post-stroke depression (PSD) is one of the most frequent psychiatric disorders after stroke. However, the underlying brain mechanism of PSD remains unclarified. Using the amplitude of low-frequency fluctuation (ALFF) approach, we aimed to investigate the abnormalities of neural activity in PSD patients, and further explored the frequency and time properties of ALFF changes in PSD. Methods: Resting-state fMRI data and clinical data were collected from 39 PSD patients (PSD), 82 S patients without depression (Stroke), and 74 age- and sex-matched healthy controls (HC). ALFF across three frequency bands (ALFF-Classic: 0.01–0.08 Hz; ALFF-Slow4: 0.027–0.073 Hz; ALFF-Slow5: 0.01–0.027 Hz) and dynamic ALFF (dALFF) were computed and compared among three groups. Ridge regression analyses and spearman’s correlation analyses were further applied to explore the relationship between PSD-specific alterations and depression severity in PSD. Results: We found that PSD-specific alterations of ALFF were frequency-dependent and time-variant. Specially, compared to both Stroke and HC groups, PSD exhibited increased ALFF in the contralesional dorsolateral prefrontal cortex (DLPFC) and insula in all three frequency bands. Increased ALFF in ipsilesional DLPFC were observed in both slow-4 and classic frequency bands which were positively correlated with depression scales in PSD, while increased ALFF in the bilateral hippocampus and contralesional rolandic operculum were only found in slow-5 frequency band. These PSD-specific alterations in different frequency bands could predict depression severity. Moreover, decreased dALFF in contralesional superior temporal gyrus were observed in PSD group. Limitations: Longitudinal studies are required to explore the alterations of ALFF in PSD as the disease progress. Conclusions: The frequency-dependent and time-variant properties of ALFF could reflect the PSD-specific alterations in complementary ways, which may assist to elucidate underlying neural mechanisms and be helpful for early diagnosis and interventions for the disease.

AimTo describe the spatial and temporal electrocorticographic (ECoG) changes after middle cerebral artery occlusion (MCAo), including those caused by spreading depolarization (SD) in the pig brain.MethodsThe left middle cerebral arteries (MCAs) were clipped in six pigs. The clipping procedure lasted between 8 and 12 min, achieving a permanent occlusion (MCAo). Five-contact ECoG stripes were placed bilaterally over the frontoparietal cortices corresponding to the irrigation territory of the MCA and anterior cerebral artery (ACA). ECoG recordings were performed around 24 h: 1 h before and 23 h after the MCAo, and SDs were quantified. Five-minute ECoG signal segments were sampled before, 5 min, and 4, 8, and 12 h after cerebral artery occlusion and before, during, and after the negative direct current shift of the SDs. The power spectrum of the signals was decomposed into delta, theta, alpha, beta, and gamma bands. Descriptive statistics, Wilcoxon matched-pairs signed-rank tests, and Friedman tests were performed.ResultsElectrodes close to the MCAo showed instant decay in all frequency bands and SD onset during the first 5 h. Electrodes far from the MCAo exhibited immediate loss of fast frequencies and progressive decline of slow frequencies with an increased SD incidence between 6 and 14 h. After 8 h, the ACA electrode reported a secondary reduction of all frequency bands except gamma and high SD incidence within 12–17 h. During the SD, all electrodes showed a decline in all frequency bands. After SD passage, frequency band recovery was impaired only in MCA electrodes.ConclusionECoG can identify infarct progression and secondary brain injury. Severe disturbances in all the frequency bands are generated in the cortices where the SDs are passing by.

Gait dysfunctions are debilitating motor symptoms of Parkinson's disease (PD) and may result in frequent falling with health complications. The contribution of the motor-cognitive network to gait disturbance can be studied more thoroughly by challenging motor-cognitive dual-task gait performances. Gait is a complex motor task that requires an appropriate contribution from motor and cognitive networks, reflected in frequency modulations among several cortical and subcortical networks. Electrophysiological recordings by scalp electroencephalography and implanted deep brain stimulation (DBS) electrodes have unveiled modulations of specific oscillatory patterns in the cortical-subcortical circuits in PD. In this review, we summarize oscillatory contributions of the cortical, basal ganglia, mesencephalic locomotor, and cerebellar regions during gait and dual-task activities in PD. We detail the involvement of the cognitive network in dual-task settings and compare how abnormal oscillations in the specific frequency bands in the cortical and subcortical regions correlate with gait deficits in PD, particularly freezing of gait (FOG). We suggest that altered neural oscillations in different frequencies can cause derangements in broader brain networks, so neuromodulation and pharmacological therapies should be considered to normalize those network oscillations to improve challenged gait and dual-task motor functions in PD. Specifically, the theta and beta bands in premotor cortical areas, subthalamic nucleus, as well as alpha band activity in the brainstem prepontine nucleus, modulate under clinically effective levodopa and DBS therapies, improving gait and dual-task performance in PD with FOG, compared to PD without FOG and age-matched healthy control groups. [ABSTRACT FROM AUTHOR]

To date, it is largely unknown how frequency range of neural oscillations measured with EEG is related to functional connectivity. To address this question, we investigated frequency-dependent directed functional connectivity among the structures of mesial and anterior temporal network including amygdala, hippocampus, temporal pole and parahippocampal gyrus in the living human brain. Intracranial EEG recording was obtained from 19 consecutive epilepsy patients with normal anterior mesial temporal MR imaging undergoing intracranial presurgical epilepsy diagnostics with multiple depth electrodes. We assessed intratemporal bidirectional functional connectivity using several causality measures such as Granger causality (GC), directed transfer function (DTF) and partial directed coherence (PDC) in a frequency-specific way. In order to verify the obtained results, we compared the spontaneous functional networks with intratemporal effective connectivity evaluated by means of SPES (single pulse electrical stimulation) method. The overlap with the evoked network was found for the functional connectivity assessed by the GC method, most prominent in the higher frequency bands (alpha, beta and low gamma), yet vanishing in the lower frequencies. Functional connectivity assessed by means of DTF and PCD obtained a similar directionality pattern with the exception of connectivity between hippocampus and parahippocampal gyrus which showed opposite directionality of predominant information flow. Whereas previous connectivity studies reported significant divergence between spontaneous and evoked networks, our data show the role of frequency bands for the consistency of functional and evoked intratemporal directed connectivity. This has implications for the suitability of functional connectivity methods in characterizing local brain circuits., (© 2024. The Author(s).)

Radio frequency interference (RFI) constitutes a significant problem in achieving a good quality of service in radio links. Several techniques have been proposed to identify and mitigate RFI in wireless networks. However, most of these techniques are not generalized for all propagation environments due to varying geographical features from one environment to another. The need for extensive frequency scan measurements on the links to identify the available channels, evaluate the performances of the links, and detect RFI in the channels becomes imperative. This study presents a performance evaluation of frequency scan measurements from active microwave links comprising eighteen base stations. The measurements equipment included a spectrum analyzer and a 0.6 m antenna dish. The frequency scans were taken at 6 GHz, 7 GHz, and 8 GHz with full azimuth coverage of the horizontal and vertical polarization. Measured data were processed to determine the available frequencies and RFI in the channels. The histogram and probability density function of the frequency scans were computed. The cumulative distribution functions were determined, and the statistical error characteristics of the frequency scans for the estimated normal distribution and the estimated fitness curve were derived. The short-time Fourier transform of the noisy signal was obtained, and the signal without noise was recovered using the inverse short-time Fourier transform. Analysis of the scanned signals before and after the noise removal is demonstrated. The denoised signals compare favorably with related results in the preliminary literature. Overall, these frequency scans would be beneficial in evaluating RFI measurements and spectrum planning and hold great promise for designing robust RFI detection algorithms for future wireless systems.

Gait dysfunctions are debilitating motor symptoms of Parkinson’s disease (PD) and may result in frequent falling with health complications. The contribution of the motor-cognitive network to gait disturbance can be studied more thoroughly by challenging motor-cognitive dual-task gait performances. Gait is a complex motor task that requires an appropriate contribution from motor and cognitive networks, reflected in frequency modulations among several cortical and subcortical networks. Electrophysiological recordings by scalp electroencephalography and implanted deep brain stimulation (DBS) electrodes have unveiled modulations of specific oscillatory patterns in the cortical-subcortical circuits in PD. In this review, we summarize oscillatory contributions of the cortical, basal ganglia, mesencephalic locomotor, and cerebellar regions during gait and dual-task activities in PD. We detail the involvement of the cognitive network in dual-task settings and compare how abnormal oscillations in the specific frequency bands in the cortical and subcortical regions correlate with gait deficits in PD, particularly freezing of gait (FOG). We suggest that altered neural oscillations in different frequencies can cause derangements in broader brain networks, so neuromodulation and pharmacological therapies should be considered to normalize those network oscillations to improve challenged gait and dual-task motor functions in PD. Specifically, the theta and beta bands in premotor cortical areas, subthalamic nucleus, as well as alpha band activity in the brainstem prepontine nucleus, modulate under clinically effective levodopa and DBS therapies, improving gait and dual-task performance in PD with FOG, compared to PD without FOG and age-matched healthy control groups.

Digital image processing makes it possible to use the most sophisticated methods, so it can provide both more complex performance in simple tasks and the implementation of impossible methods. Digital cameras usually have a special hardware for image processing (specially added to the circuits or other chips) to convert the original data into a standard image file format in the form of a standard image file changes. The Digital filters are used to blur and sharpen visual based digital images. These filters can be done in the space sphere through the frequency field by covering specially designed cores or certain frequency bands. The images are usually stacked before the Fourier is transferred and the overlay filter images show the results of different layer techniques. In this paper, an innovation filter model was proposed interms of color pixels in the images. The proposed model has a highpass filter shows extra edges when zero is compared to the repeat edge. The visual transformations make the basic changes of the film, including scaling, rotation, exchange, reflection and cultivation. [ABSTRACT FROM AUTHOR]

• USN is a spatial attention impairment caused by a brain injury. • The severity of the condition is proportional to the duration of the recovery time. • Recent EEG investigations indicate that the lack of structural or functional communication in USN may be a major cause of their abnormal clinical behavior. • We examined EEG variations at various phases of the USN rehabilitation period and shown that EEG is a practicable test for diagnosing and monitoring USN recovery. • Finally, we investigated the cause of heterogeneity, recommending that more research be undertaken in the future. The investigation of brainwave changes during the recovery process of unilateral spatial neglect (USN) has garnered considerable attention in recent years. This paper presents an updated overview of the evolving brainwave patterns during USN rehabilitation, aiming to predict clinical outcomes and guide the selection of effective recovery strategies. A systematic review was conducted, encompassing English literature published up to June 2024. Databases including PubMed, Web of Science, and clinical trials were utilized. The included studies assessed brainwaves using electroencephalography (EEG) in at least one group with USN. However, the diverse nature of these studies posed challenges for a quantitative synthesis. The final quantitative synthesis comprised 36 studies, incorporating a total of 4517 data points. The analysis revealed abnormalities in alpha, beta, and gamma brainwave activity, along with alterations in the functional monitoring of the alpha band during USN rehabilitation. Additionally, reductions were observed in specific brainwave components such as P1, N1, P2, P300, early directing attention negativity (EDAN), late directing attention positivity (LDAP), and contingent negative variation (CNV). However, findings regarding measures of synchrony, connectivity, and evoked responses across different frequency bands exhibited variability. Various indicators of brainwave activity displayed changes at different stages of post-stroke neglect rehabilitation, highlighting the significance of neural network dysfunction in this process. Nonetheless, due to the diversity of the studies, further investigation is necessary to achieve a more comprehensive understanding in future research endeavors. [ABSTRACT FROM AUTHOR]

The effect of horror sound on electro-cardiac activity is not yet sufficiently explored despite recommendable research on different genres of music and sound. Detecting the impact of these stimuli is a complex task. The current study intends to automatically detect the horror-sound induced cardiac changes using wavelet coefficient-based features extracted from the electrocardiograph signals and several machine learning models. The statistical comparison of these features in the two data groups suggests a significant change in different feature values after exposure to horror sound while watching a horror clip. The statistically discriminative features were employed as input to nine machine learning (ML) models, namely Naïve Bayes, Fast large margin, Logistic regression, generalized linear model, Decision tree, Random Forest, deep learning, gradient boosted tree, and Support vector machine. The performance of these ML models was compared using different performance measures, including accuracy, precision, F-measure, sensitivity, specificity, and area under the curve (AUC). The results suggest the gradient boosted tree is the best performing model in differentiating the horror sound-induced changes in the ECG signals.

Liheng Chen,1 Jiawei Sun,2,3 Qiaohong Wang,1 Lingzhen Hu,1 Yi Zhang,1 Huibin Ma,2,3 Xize Jia,4,5 Xuyan Yang1 1Department of Rheumatology, Second Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, 310009, People’s Republic of China; 2School of Information and Electronics Technology, Jiamusi University, Jiamusi, People’s Republic of China; 3Integrated Medical Research School, Jiamusi University, Jiamusi, People’s Republic of China; 4Institute of Psychological Sciences, Hangzhou Normal University, Hangzhou, People’s Republic of China; 5Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments, Hangzhou, People’s Republic of ChinaCorrespondence: Xize JiaInstitute of Psychological Sciences, Hangzhou Normal University, Hangzhou, People’s Republic of ChinaEmail jiaxize@hznu.edu.cnXuyan YangDepartment of Rheumatology, Second Affiliated Hospital, College of Medicine, Zhejiang University, 88 Jiefang Road, Hangzhou, 310009, People’s Republic of ChinaEmail hangzhouyxy@zju.edu.cnPurpose: In this study, we seek to investigate dynamic changes of brain activity in non-neuropsychiatric systemic lupus erythematosus (non-NPSLE) patients with inactive disease.Patients and Methods: Thirty-one non-NPSLE patients with inactive disease and 20 matched healthy controls underwent the blood oxygenation level-dependent fMRI examination. Dynamic regional homogeneity (ReHo) and fractional amplitude of low-frequency fluctuations (fALFF) were used to analyze the brain activity in typical band (0.01– 0.08 Hz), slow-4 (0.027– 0.073 Hz) and slow-5 (0.01– 0.027 Hz). Pearson’s correlation analysis was performed to correlate dynamic regional homogeneity (dReHo) and dynamic fractional amplitude of low-frequency fluctuations (dfALFF) values for clusters of voxels where significant group differences were found with clinical variables in non-NPSLE patients with inactive disease.Results: In typical band, non-NPSLE patients showed increased dReHo in left middle occipital gyrus (MOG) compared to healthy controls. Meanwhile, patients showed decreased dfALFF in right superior frontal gyrus (SFG) and bilateral middle frontal gyrus (MFG) in typical band. In slow-4, increased dReHo in left MOG was found in non-NPSLE patients. In slow-5, non-NPSLE patients showed increased dReHo in left MOG, left calcarine fissure and surrounding cortex, right precentral gyrus (PreCG) and left postcentral gyrus (PoCG). Meanwhile, non-NPSLE patients showed decreased dfALFF in left SFG, right MFG, and right PreCG in slow-5. Moreover, the glucocorticoid dose showed significantly negative correlations with dReHo values in right PreCG in slow-5, left PoCG in slow-5, and left MOG in typical band.Conclusion: dReHo and dfALFF abnormalities in different frequency bands may be the key characteristics in the pathogenesis mechanism of non-NPSLE.Keywords: systemic lupus erythematosus, non-neuropsychiatric systemic lupus erythematosus, dynamic fractional amplitude of low-frequency fluctuations, dynamic regional homogeneity, frequency bands, resting-state functional magnetic resonance imaging

The aim of this study was to perform online modification of unobservable but crucial processing parameters for a hot forging tool in real time to maximize workpiece quality and to extend the useful life of the hot forging tool. The hot forging process involves with plastic deformation of workpiece and the inevitable tradeoff is rapid wear of the forging tool surface. However, some uncontrollable factors, e.g., placement of the workpiece in the cavity and varying temperature of the inner forging tool, may substantially affect the nonlinear degeneration of the forging tool. Inadequate workpiece quality or failure of the forging tool may then require unexpected changes in the production schedule. To address this issue, this study fitted accelerometers to a forging machine to acquire the full range of motion required for the machine to complete a forging process. The wear and thermal experiments were also conducted simultaneously to label events on the vibratory signals. A trend analysis was performed to analyze vibratory signals in both the time domain and the frequency domain. Since the proposed feature extraction method could only reveal trends in the specific frequency bands, however, extracted features and labeled events were used as input parameters and output targets, respectively, in a supervised learning model. Then, a systematic design approach for using the model architecture is proposed, including a strategy for model training between model underfitting and overfitting conditions. The methodologies proposed in this study are then evaluated in two case studies. [ABSTRACT FROM AUTHOR]

Visual evoked potentials (VEPs) provide a means to examine neural mechanisms in autism with high temporal resolution. Conventional VEP analysis relies on subjective inspection of a few points (peaks and troughs) in the time‐domain waveform. The current study applied power spectral analysis and magnitude‐squared coherence (MSC) statistics (frequency‐domain measures) to VEPs recorded during 1‐minute runs and with a recently developed short‐duration technique that allow for objective examination of the responses (Zemon & Gordon, European Journal of Neuroscience, 2018, 48, 1765–1788) from nonautistic and autistic children. Results indicate that, for both groups, early time‐domain measures (P60, N75, P100) are highly correlated with middle‐ and high‐frequency (14–28 and 30–48 Hz, respectively) mechanisms, and late measures are highly correlated with a low‐frequency (6–12 Hz) mechanism. One frequency‐domain measure (power in the middle‐frequency band) is capable of predicting the key amplitude measure (N75‐P100) with high accuracy. MSC and power measures were combined to yield separate measures of signal and noise strength to evaluate alternate hypotheses in autism. Linear mixed‐effects modeling demonstrated selective differences in early time‐domain and middle‐to‐high frequency‐domain measures in autistic children as compared to nonautistic children given both recording techniques, implicating weaker excitatory input to the cortex. Receiver‐operating‐characteristic curve analysis showed predictive diagnostic accuracy for middle‐ and high‐frequency bands based on MSC. These findings support the value of frequency analysis measures (power spectral analysis and MSC) in the objective examination of neural differences in autism. Lay Summary: Visual evoked potentials (VEPs) are used to assess neural mechanisms. Typically, VEPs are analyzed by subjective examination of time‐series waveforms; but here objective techniques were applied to quantify VEP frequency components to investigate neural differences between autistic and nonautistic children. The objective measures demonstrate group differences in brain function that point to weaker excitatory input to the cortex in autism. [ABSTRACT FROM AUTHOR]