Predefined Frequency Bands Research Articles

A systematic data-driven approach to analyze sensor-level EEG connectivity: Identifying robust phase-synchronized network components using surface Laplacian with spectral-spatial PCA.

Although conventional averaging across predefined frequency bands reduces the complexity of EEG functional connectivity (FC), it obscures the identification of resting-state brain networks (RSN) and impedes accurate estimation of FC reliability. Extending prior work, we combined scalp current source density (CSD; spherical spline surface Laplacian) and spectral-spatial PCA to identify FC components. Phase-based FC was estimated via debiased-weighted phase-locking index from CSD-transformed resting EEGs (71 sensors, 8min, eyes open/closed, 35 healthy adults, 1-week retest). Spectral PCA extracted six robust alpha and theta components (86.6% variance). Subsequent spatial PCA for each spectral component revealed seven robust regionally focused (posterior, central, and frontal) and long-range (posterior-anterior) alpha components (peaks at 8, 10, and 13 Hz) and a midfrontal theta (6Hz) component, accounting for 37.0% of FC variance. These spatial FC components were consistent with well-known networks (e.g., default mode, visual, and sensorimotor), and four were sensitive to eyes open/closed conditions. Most FC components had good-to-excellent internal consistency (odd/even epochs, eyes open/closed) and test-retest reliability (ICCs ≥ .8). Moreover, the FC component structure was generally present in subsamples (session × odd/even epoch, or smaller subgroups [n= 7-10]), as indicated by high similarity of component loadings across PCA solutions. Apart from systematically reducing FC dimensionality, our approach avoids arbitrary thresholds and allows quantification of meaningful and reliable network components that may prove to be of high relevance for basic and clinical research applications.

Electrophysiological correlates of thalamocortical function in acute severe traumatic brain injury

Tools assaying the neural networks that modulate consciousness may facilitate tracking of recovery after acute severe brain injury. The ABCD framework classifies resting-state EEG into categories reflecting levels of thalamocortical network function that correlate with outcome in post-cardiac arrest coma. In this longitudinal cohort study, we applied the ABCD framework to 20 patients with acute severe traumatic brain injury requiring intensive care (12 of whom were also studied at ≥6-months post-injury) and 16 healthy controls. We tested four hypotheses: 1) EEG ABCD classifications are spatially heterogeneous and temporally variable; 2) ABCD classifications improve longitudinally, commensurate with the degree of behavioral recovery; 3) ABCD classifications correlate with behavioral level of consciousness; and 4) the Coma Recovery Scale-Revised arousal facilitation protocol yields improved ABCD classifications. Channel-level EEG power spectra were classified based on spectral peaks within pre-defined frequency bands: ‘A’ = no peaks above delta (<4 Hz) range (complete thalamocortical disruption); ‘B’ = theta (4–8 Hz) peak (severe thalamocortical disruption); ‘C’ = theta and beta (13–24 Hz) peaks (moderate thalamocortical disruption); or ‘D’ = alpha (8–13 Hz) and beta peaks (normal thalamocortical function). Acutely, 95% of patients demonstrated ‘D’ signals in at least one channel but exhibited within-session temporal variability and spatial heterogeneity in the proportion of different channel-level ABCD classifications. By contrast, healthy participants and patients at follow-up consistently demonstrated signals corresponding to intact thalamocortical network function. Patients demonstrated longitudinal improvement in ABCD classifications (p < .05) and ABCD classification distinguished patients with and without command-following in the subacute-to-chronic phase of recovery (p < .01). In patients studied acutely, ABCD classifications improved after the Coma Recovery Scale-Revised arousal facilitation protocol (p < .05) but did not correspond with behavioral level of consciousness. These findings support the use of the ABCD framework to characterize channel-level EEG dynamics and track fluctuations in functional thalamocortical network integrity in spatial detail.

Time-Frequency Analysis of Scalp EEG With Hilbert-Huang Transform and Deep Learning.

Electroencephalography (EEG) is a brain imaging approach that has been widely used in neuroscience and clinical settings. The conventional EEG analyses usually require pre-defined frequency bands when characterizing neural oscillations and extracting features for classifying EEG signals. However, neural responses are naturally heterogeneous by showing variations in frequency bands of brainwaves and peak frequencies of oscillatory modes across individuals. Fail to account for such variations might result in information loss and classifiers with low accuracy but high variation across individuals. To address these issues, we present a systematic time-frequency analysis approach for analyzing scalp EEG signals. In particular, we propose a data-driven method to compute the subject-specific frequency bands for brain oscillations via Hilbert-Huang Transform, lifting the restriction of using fixed frequency bands for all subjects. Then, we propose two novel metrics to quantify the power and frequency aspects of brainwaves represented by sub-signals decomposed from the EEG signals. The effectiveness of the proposed metrics are tested on two scalp EEG datasets and compared with four commonly used features sets extracted from wavelet and Hilbert-Huang Transform. The validation results show that the proposed metrics are more discriminatory than other features leading to accuracies in the range of 94.93% to 99.84%. Besides classification, the proposed metrics show great potential in quantification of neural oscillations and serving as biomarkers in the neuroscience research.

Data-Driven Extraction of Uniformly Stable and Passive Parameterized Macromodels

A robust algorithm for the extraction of reduced-order behavioral models from sampled frequency responses is proposed. The system under investigation can be any Linear and Time Invariant structure, although the main emphasis is on devices that are relevant for Signal and Power Integrity and RF design, such as electrical interconnects and integrated passive components. We assume that the device under modeling is parameterized by one or more design variables, which can be related to geometry or materials. Therefore, we seek for multivariate macromodels that reproduce the dynamic behavior over a predefined frequency band, with an explicit embedded dependence of the model equations on these external parameters. Such parameterized macromodels may be used to construct component libraries and prove very useful in fast system-level numerical simulations in time or frequency domain, including optimization, what-if, and sensitivity analysis. The main novel contribution is the formulation of a finite set of convex constraints that are applied during model identification, which provide sufficient conditions for uniform model stability and passivity throughout the parameter space. Such constraints are characterized by an explicit control allowing for a trade-off between model accuracy and runtime, thanks to some special properties of Bernstein polynomials. In summary, we propose a method to systematically address the longstanding problem of multivariate stability and passivity enforcement in data-driven model order reduction, which insofar has been tackled only via either over-conservative or heuristic and possibly unreliable methods.

Osnovne karakteristike EEG epileptiformnih pražnjenja izazvanih lindanom u modelu eksperimentalnog prostatitisa

Introduction: Chronic prostatitis/Chronic Pelvic Pain Syndrome (CP/CPPS) is the most commonly diagnosed non-infectious prostatitis in urology. Studies have shown that CP/CPPS can induce neuroinflammation, which may result in CNS hyperexcitability and a tendency to develop epileptic seizures. Spike salvos are ictal EEG graph elements typical for the experimental model of lindane-induced seizures. There are a number of mathematical models for quantitative analysis of EEG, including the Fast Fourier Transform (FFT). It transforms the signal from time into the frequency domain, providing information on Power Spectral Densities (PSD). Aim: The aim of this study was to investigate the basic characteristics of epileptiform discharges induced by subconvulsive dose of lindane in rats, with experimentally induced CP/CPPS. Material and methods: CP/CPPS was induced by intraprostatic injection of 3% l-carrageenan in male Wistar albino rats. Animals with CP/CPPS were implanted with EEG registration electrodes, and then administered lindane (4 mg/kg, i.p, experimental group, n = 6 per group) or its solvent (DMSO, control group, n = 6 per group). An 8-channel EEG device was used in combination with software developed in the laboratory (NeuroSciLaBG). Ictal EEG epochs were extracted from the original signal and FFT analysis was performed to obtain information taking into account PSD in predefined frequency bands. Results: There was no ictal activity in the EEG of control animals. In experimental animals, ictal activity occurred and the mean duration of the ictal period was 2.06 s. FFT analysis revealed that the Alpha frequency range (7-15 Hz) was markedly dominant during ictal activity. Conclusion: The results of this study showed the characteristics of epileptiform discharges in animals with experimentally induced CP/CPPS. This study and animal model are suitable for future translational studies of the comorbidities of this syndrome.

Recognition of Electroencephalography-Related Features of Neuronal Network Organization in Patients With Schizophrenia Using the Generalized Choquet Integrals.

In this study, we focused on the verification of suitable aggregation operators enabling accurate differentiation of selected neurophysiological features extracted from resting-state electroencephalographic recordings of patients who were diagnosed with schizophrenia (SZ) or healthy controls (HC). We built the Choquet integral-based operators using traditional classification results as an input to the procedure of establishing the fuzzy measure densities. The dataset applied in the study was a collection of variables characterizing the organization of the neural networks computed using the minimum spanning tree (MST) algorithms obtained from signal-spaced functional connectivity indicators and calculated separately for predefined frequency bands using classical linear Granger causality (GC) measure. In the series of numerical experiments, we reported the results of classification obtained using numerous generalizations of the Choquet integral and other aggregation functions, which were tested to find the most appropriate ones. The obtained results demonstrate that the classification accuracy can be increased by 1.81% using the extended versions of the Choquet integral called in the literature, namely, generalized Choquet integral or pre-aggregation operators.

The effects of tapentadol and oxycodone on central processing of tonic pain

ObjectiveThe present study investigated differences between opioids to experimental tonic pain in healthy men. MethodsTwenty-one males participated in this cross-over-trial. Interventions twice daily were oxycodone (10 mg), tapentadol (50 mg) and placebo for 14 days. Tonic pain was induced on day 1, 4 and 14 by immersing the hand in 2 °C water for 120 s. Electroencephalography was recorded during test pain at baseline and after 14 days. Spectral analysis and source localization were investigated in predefined frequency bands. ResultsA decreased perception of pain on day 4 persisted throughout the 14 days compared to baseline (p < 0.006). Oxycodone decreased the electroencephalography spectral power in the delta and theta bands and increased power in the alpha1, alpha2 and beta1 bands (p < 0.03). Tapentadol increased spectral power in the alpha1 band (p < 0.001). Source localization revealed that oxycodone decreased activity of the temporal and limbic region in the delta band, and frontal lobe in the alpha2 and beta1 bands, whereas tapentadol decreased alpha1 band activity in the temporal lobe compared to placebo. ConclusionOxycodone and tapentadol reduced pain perception and changed the central processing of tonic pain. SignificanceDifferent mechanisms of action were involved, where oxycodone affected cortical structures more than tapentadol.

Cross-term events of scale-decomposed skewness factor in turbulent boundary layer at moderate Reynolds number

This study reports the observation of cross term events of scale-decomposed skewness factor in turbulent boundary layer at moderate Reynolds number. The large field-of-view particle image velocimetry was utilized to measure the flow fields. By the approach of multi-scale proper orthogonal decomposition (mPOD), the large- and small-scale structures were reconstructed by the mPOD modes relevant to the predefined frequency bands. Then, the cross term of the scale-decomposed skewness was observed, which was proposed in the previous works by Schlatter and Örlü [Phys. Fluids 22, 051704 (2010)] and Mathis et al. [Phys. Fluids, 23, 121702 (2011)]. The cross term events are featured by both the large and small scales, which were consolidated by the linear fitting of correlation coefficients with different slope angles. The characteristic length of the local intense cross term events is around 0.1δ (δ is the boundary layer thickness), which is comparable with that of the swirling structures related to hairpin vortice in the form of hairpin packets. The conditional averaging results presented the arrangement that the local cross term event appears underneath the hairpin vortex in the statistical viewpoint. Based on the hairpin vortex model, it was proposed that the local intense cross term events are associated with the local low-speed fluids induced by the hairpins through the ejection process. Especially, in the wake region, the cross term events are promoted, and also well-correlated with the swirling structures. This kind of configuration was attributed to the combination of the vortex induction and the entrainment process relative to the turbulent/non-turbulent intermittency.

Compact multi-functional frequency-selective absorber based on customizable impedance films.

Multi-functional metamaterial absorbers have attracted considerable attention for applications in the microwave frequency regime. In this paper, we report the design, fabrication, and characterization of frequency-selective absorbers, which exhibit substantial absorption property within a pre-defined frequency band, while at the same time behaving as a highly transparent screen in another targeted frequency band. The proposed designs consist of a symmetrically patterned indium tin oxide film acting as an absorbing layer, two dielectric substrates, and a cross-slot metal sheet frequency selective surface playing the role of a transmitting layer. In order to validate the functionalities of the designed absorbers, equivalent circuit models, full-wave numerical simulations and measurements are presented. The measured results, in good agreement with the numerical ones, show that the proposed designs realize 80% broadband absorption over the desired frequency range and possess a transparent window in a higher or lower frequency band for a wide range of incidence angles up to 60°. These performances suggest that the proposed designs are promising candidates for multi-functional scattering control and communication applications.

Tomographic particle image velocimetry flow structures downstream of a dynamic cylindrical element in a turbulent boundary layer by multi-scale proper orthogonal decomposition

This study reports the modification of large and small scales in a turbulent boundary layer (TBL) perturbed by a dynamic cylindrical element (DCE). Tomographic particle image velocimetry (Tomo-PIV) was utilized to measure the flow fields downstream of the dynamic perturbation. By the approach of multi-scale proper orthogonal decomposition (mPOD), the coherent modes relevant to the predefined frequency bands were extracted from the Tomo-PIV dataset. Then, a method was developed to construct the large- and small-scale structures and the DCE-perturbed structure based on the mPOD modes. The DCE impact on the large- and small-scale structures was elaborated by comparing with the unperturbed TBL case. The two-point correlation analysis indicated that large-scale structures appear downstream of the DCE perturbation in a short streamwise length scale. More importantly, the scale rearrangements were further examined by presenting the modulation coefficients between the large scales and small-scale energy. It revealed that even though the DCE perturbation alters the level of correlation, three different types of interaction scenario can still be observed. In the near-wall region, the large-scale structures have an amplitude modulation effect on the small-scale energy with the lower positive coefficients. The reversal scale arrangement was observed at the wall-normal height around the DCE amplitude, which could be attributed to the fluid exchange caused by the new-generated turbulent structures. In the log region, it confirmed that the inclined shear layer resides along the low-speed regions, which supported the robustness of the conceptual model of hairpin packets in the current DCE-perturbed TBL.

Simultaneously Spatiospectral Pattern Learning and Contaminated Trial Pruning for Electroencephalography-Based Brain Computer Interface

Electroencephalography (EEG)-based brain computer interfaces (BCIs) translate motor imagery commands into the movements of an external device (e.g., a robotic arm). The automatic design of spectral and spatial filters is a challenging task, as the frequency bands of the spectral filters must be predefined by previously published studies and given that they may be affected during trials by artifacts and improper motor imagery (MI). This study aimed to eliminate the contaminated trials automatically during classifier training, and to simultaneously learn the spectral and spatial patterns without the need for predefined frequency bands. Compared with previous studies that measured the discriminative power of a frequency band based on mutual information, this study determined the difference of the class conditional probability density function between two MI classes. This information was further shared to measure the contamination level of the trial that simplified the computation. A particle-based approximation technique iteratively constructed a filter bank that extracted discriminative features, and simultaneously removed potentially contaminated trials. The particle weight was estimated by an analysis of variance F-test instead of mutual information as commonly used in previous studies. The experimental results of a publicly available dataset revealed that the proposed method outperformed the other BCI in terms of the classification accuracy. Asymmetrical spatial patterns were found on left- versus right-hand MI classifications. The learnt spectral and spatial patterns were consistent with prior neurophysiological knowledge.

A texture based descriptors used for real time tool condition monitoring

Tool Condition Monitoring Systems (TCMs), in the task of tool wear recognition, are highly dependent on the choice of descriptors. It is very important to extract the appropriate marking that contains information on the tool wear condition from the sensor signal. We consider the portions of the Short-Term Discrete Furrier Transform (STDFT) spectrum obtained by speaking from a vibration signal sensor as a 2D textured image. This is achieved by setting the STDFT timeline as the first dimension and the frequency scales as the second dimension of the resulting textured image. We divide the resulting textured image into special particular patches of texture, focusing on a portion of the frequency range of interest. Applying the set filter bank, 2D textons are allocated for each predefined frequency band. From 2D textons, for each frequency band, we extract probability density function (PDF) data in the form of lower order statistical moments. The applied method gives robust descriptors of the Tool wear state.

The use of continuous spectral analysis for the assessment of postural stability changes after sports-related concussion

Impaired postural stability is associated with a variety of pathologies including sports-related concussion (SRC). Quantification of centre of pressure (COP) movement is the most common focus of instrumented assessment. Frequency-domain COP analyses have focused primarily on summary measures or pre-defined frequency bands but continuous analysis may provide novel and complementary insight into pathological control mechanisms. Our aims were (i) to compare post-SRC COP trajectory changes identified using clinician scores (Modified Balance Error Scoring System (M-BESS)), time-domain COP variables and continuous frequency spectral comparison; and (ii) to characterise frequency spectra changes. Male rugby players aged 15–19 years (n = 135) completed a pre-season baseline assessment comprising vision-obscured double-leg, single-leg and tandem stances on a force platform. Participants diagnosed with SRC during the season (n = 15) underwent repeat testing (median 4 days post-SRC; IQR 2.5–6.5). Baseline and post-SRC COP trajectories were compared using common time-domain COP variables, M-BESS scores and continuous frequency spectra. Post-SRC changes were identified using all three approaches. Spectral analysis revealed the largest effect size (Cliff’s delta 0.39) and was the only method to identify differences in all three stances and in double-leg stance. All post-SRC increases in spectral content were in the anteroposterior direction; all decreases were in the mediolateral direction. Changes were localised to higher frequencies (1.7–8 Hz) except for double-leg stance anteroposterior direction, for which increases were observed throughout the analysed range. Our findings suggest that this method of spectral comparison may provide a more responsive and meaningful measure of postural stability changes after SRC than other commonly-used variables.

A penalized time-frequency band feature selection and classification procedure for improved motor intention decoding in multichannel EEG

Objective. Motor imagery brain–computer interfaces (MI-BCIs) based on electroencephalography (EEG), a promising technology to provide assistance and support rehabilitation of neurological patients with sensorimotor impairments, require a reliable and adaptable subject-specific model to efficiently decode motor intention. The most popular EEG feature extraction algorithm for MI-BCIs is the common spatial patterns (CSP) method, but its performance strongly depends on the predefined frequency band and time segment length for analyzing the EEG signal. Approach. In this work, a novel method for efficiently decoding motor intention for EEG-based BCIs performing multiple frequency band analysis in multiple EEG segments is presented. This decoding algorithm uses raw multichannel EEG data which are decomposed into specific temporal and frequency bands. Features are extracted at each - band by using CSP. Feature selection and classification are simultaneously performed by means of a fast procedure, based on elastic-net regression, which allows for the inclusion of a priori discriminative information into the model. The effectiveness of the proposed method is tested off-line on two public EEG-based MI-BCI datasets and on a self-acquired dataset in two configurations: multiple temporal windows and single temporal window. Main results. The experimental results show that the proposed multiple time-frequency band method yields overall accuracy improvements of up to (average accuracy of 84.8%) as compared to the best current state-of-the-art methods based on filter bank analysis and CSP for MI detection. Also, classification variability is reduced, making the proposed method more robust to intra-subject EEG fluctuations. Significance. This paper presents a novel approach for improving motor intention detection by automatically selecting subject-specific spatio-temporal-spectral features, especially when MI has to be detected against rest condition. This technique contributes to the further advancement and application of EEG-based MI-BCIs for assistance and neurorehabilitation therapy.

EEG Frequency Bands in Psychiatric Disorders: A Review of Resting State Studies.

A significant proportion of the electroencephalography (EEG) literature focuses on differences in historically pre-defined frequency bands in the power spectrum that are typically referred to as alpha, beta, gamma, theta and delta waves. Here, we review 184 EEG studies that report differences in frequency bands in the resting state condition (eyes open and closed) across a spectrum of psychiatric disorders including depression, attention deficit-hyperactivity disorder (ADHD), autism, addiction, bipolar disorder, anxiety, panic disorder, post-traumatic stress disorder (PTSD), obsessive compulsive disorder (OCD) and schizophrenia to determine patterns across disorders. Aggregating across all reported results we demonstrate that characteristic patterns of power change within specific frequency bands are not necessarily unique to any one disorder but show substantial overlap across disorders as well as variability within disorders. In particular, we show that the most dominant pattern of change, across several disorder types including ADHD, schizophrenia and OCD, is power increases across lower frequencies (delta and theta) and decreases across higher frequencies (alpha, beta and gamma). However, a considerable number of disorders, such as PTSD, addiction and autism show no dominant trend for spectral change in any direction. We report consistency and validation scores across the disorders and conditions showing that the dominant result across all disorders is typically only 2.2 times as likely to occur in the literature as alternate results, and typically with less than 250 study participants when summed across all studies reporting this result. Furthermore, the magnitudes of the results were infrequently reported and were typically small at between 20% and 30% and correlated weakly with symptom severity scores. Finally, we discuss the many methodological challenges and limitations relating to such frequency band analysis across the literature. These results caution any interpretation of results from studies that consider only one disorder in isolation, and for the overall potential of this approach for delivering valuable insights in the field of mental health.

A Machine Hearing System for Robust Cough Detection Based on a High-Level Representation of Band-Specific Audio Features.

This paper presents a machine hearing system for audio-based robust cough segmentation that can be easily deployed in mobile scenarios. Cough detection is performed in two steps. First, a short-term spectral feature set is separately computed in five predefined frequency bands: [0, 0.5), [0.5, 1), [1, 1.5), [1.5, 2), and [2, 5.5125]kHz. Feature selection and combination are then applied to make the short-term feature set robust enough in different noisy scenarios. Second, high-level data representation is achieved by computing the mean and standard deviation of short-term descriptors in 300 ms long-term frames. Finally, cough detection is carried out using a support vector machine trained with data from different noisy scenarios. The system is evaluated using a patient signal database which emulates three real-life scenarios in terms of noise content. The system achieves 92.71% sensitivity, 88.58% specificity, and 90.69% Area Under Receiver Operating Charcteristic (ROC) curve (AUC), outperforming state-of-the-art methods. Our research outcome paves the way to create a device for cough monitoring in real-life situations. Our proposal is aligned with a more comfortable and less disruptive patient monitoring, with benefits for patients (allows self-monitoring of cough symptoms), practitioners (e.g., assessment of treatments or better clinical understanding of cough patterns), and national health systems (by reducing hospitalizations).

Binding language: structuring sentences through precisely timed oscillatory mechanisms.

Syntactic binding refers to combining words into larger structures. Using EEG, we investigated the neural processes involved in syntactic binding. Participants were auditorily presented two-word sentences (i.e. pronoun and pseudoverb such as 'I grush' and 'she grushes', for which syntactic binding can take place) and wordlists (i.e. two pseudoverbs such as 'pob grush' and 'pob grushes', for which no binding occurs). Comparing these two conditions, we targeted syntactic binding while minimising contributions of semantic binding and of other cognitive processes such as working memory. We found a converging pattern of results using two distinct analysis approaches: one approach using frequency bands as defined in previous literature, and one data-driven approach in which we looked at the entire range of frequencies between 3 and 30Hz without the constraints of pre-defined frequency bands. In the syntactic binding (relative to the wordlist) condition, a power increase was observed in the alpha and beta frequency range shortly preceding the presentation of the target word that requires binding, which was maximal over frontal-central electrodes. Our interpretation is that these signatures reflect that language comprehenders expect the need for binding to occur. Following the presentation of the target word in a syntactic binding context (relative to the wordlist condition), an increase in alpha power maximal over a left-lateralised cluster of frontal-temporal electrodes was observed. We suggest that this alpha increase relates to syntactic binding taking place. Taken together, our findings suggest that increases in alpha and beta power are reflections of distinct the neural processes underlying syntactic binding.

Resting-state functional under-connectivity within and between large-scale cortical networks across three low-frequency bands in adolescents with autism

Although evidence is accumulating that autism spectrum disorder (ASD) is associated with disruption of functional connections between and within brain networks, it remains largely unknown whether these abnormalities are related to specific frequency bands. To address this question, network contingency analysis was performed on brain functional connectomes obtained from 213 adolescent participants across nine sites in the Autism Brain Imaging Data Exchange (ABIDE) multisite sample, to determine the disrupted connections between and within seven major cortical networks in adolescents with ASD at Slow-5, Slow-4 and Slow-3 frequency bands and further assess whether the aberrant intra- and inter-network connectivity varied as a function of ASD symptoms. Overall under-connectivity within and between large-scale intrinsic networks in ASD was revealed across the three frequency bands. Specifically, decreased connectivity strength within the default mode network (DMN), between DMN and visual network (VN), ventral attention network (VAN), and between dorsal attention network (DAN) and VAN was observed in the lower frequency band (slow-5, slow-4), while decreased connectivity between limbic network (LN) and frontal-parietal network (FPN) was observed in the higher frequency band (slow-3). Furthermore, weaker connectivity within and between specific networks correlated with poorer communication and social interaction skills in the slow-5 band, uniquely. These results demonstrate intrinsic under-connectivity within and between multiple brain networks within predefined frequency bands in ASD, suggesting that frequency-related properties underlie abnormal brain network organization in the disorder.

Novel texture-based descriptors for tool wear condition monitoring

Abstract All state-of-the-art tool condition monitoring systems (TCM) in the tool wear recognition task, especially those that use vibration sensors, heavily depend on the choice of descriptors containing information about the tool wear state which are extracted from the particular sensor signals. All other post-processing techniques do not manage to increase the recognition precision if those descriptors are not discriminative enough. In this work, we propose a tool wear monitoring strategy which relies on the novel texture based descriptors. We consider the module of the Short Term Discrete Fourier Transform (STDFT) spectra obtained from the particular vibration sensors signal utterance as the 2D textured image. This is done by identifying the time scale of STDFT as the first dimension, and the frequency scale as the second dimension of the particular textured image. The obtained textured image is then divided into particular 2D texture patches, covering a part of the frequency range of interest. After applying the appropriate filter bank, 2D textons are extracted for each predefined frequency band. By averaging in time, we extract from the textons for each band of interest the information regarding the Probability Density Function (PDF) in the form of lower order moments, thus obtaining robust tool wear state descriptors. We validate the proposed features by the experiments conducted on the real TCM system, obtaining the high recognition accuracy.

Does Sacral Nerve Stimulation Improve Continence Through Enhanced Sensitivity of the Anal Canal? A Pilot Study.

It has been suggested that the effects of sacral nerve stimulation against fecal incontinence involve neuromodulation at spinal or supraspinal levels. This study aims to investigate the afferent sensory pathways from the anorectum before and during sacral nerve stimulation. This is an explorative study. Fifteen women with idiopathic fecal incontinence (mean age, 58 ± 12.2 years) were selected. Cortical evoked potentials were recorded during repeated rapid balloon distension of the rectum and the anal canal both before and during temporary sacral nerve stimulation. Stimuli applied were individualized according to the subjective urge to defecate. The main outcomes measured were 1) stimulus intensity, 2) latencies and amplitudes of cortical evoked potentials, and 3) spectral content in predefined frequency bands of cortical evoked potentials. The median Wexner fecal incontinence score improved from 15.5 ± 3.6 before to 6.7 ± 5 during sacral nerve stimulation (p < 0.001). Sacral nerve stimulation did not affect the threshold for urge to defecate during rectal distension (p = 0.64) but reduced the threshold from stimulation of the anal canal by 50% (p = 0.03). No statistically significant differences were found in latencies, amplitudes, or spectral analysis. This is a pilot study of limited size. In patients with idiopathic fecal incontinence, sacral nerve stimulation reduced the threshold for urge to defecate elicited from the anal canal, whereas supraspinal responses remained unaltered. This may suggest that sacral nerve stimulation, at least in part, acts via somatic afferent fibers enhancing anal sensation.