OBJECTIVE: To explore the utility of high frequency oscillations (HFO) and long-range temporal correlations (LRTCs) in preoperative assessment of epilepsy.
METHODS: MEG ripples were detected in 59 drug-resistant epilepsy patients, comprising 5 with parietal lobe epilepsy (PLE), 21 with frontal lobe epilepsy (FLE), 14 with lateral temporal lobe epilepsy (LTLE), and 19 with mesial temporal lobe epilepsy (MTLE) to identify the epileptogenic zone (EZ). The results were compared with clinical MEG reports and resection area. Subsequently, LRTCs were quantified at the source-level by detrended fluctuation analysis (DFA) and life/waiting -time at 5 bands for 90 cerebral cortex regions. The brain regions with larger DFA exponents and standardized life-waiting biomarkers were compared with the resection results.
RESULTS: Compared to MEG sensor-level data, ripple sources were more frequently localized within the resection area. Moreover, source-level analysis revealed a higher proportion of DFA exponents and life-waiting biomarkers with relatively higher rankings, primarily distributed within the resection area (p<0.01). Moreover, these two LRCT indices across five distinct frequency bands correlated with EZ.
CONCLUSIONS: HFO and source-level LRTCs are correlated with EZ. Integrating HFO and LRTCs may be an effective approach for presurgical evaluation of epilepsy.

In this paper, a MEMS piezoresistive ultrathin silicon membrane-based strain sensor is presented. The sensor\'s ability to capture an acoustic emission signal is demonstrated using a Hsu-Nielsen source, and shows comparable frequency content to a commercial piezoceramic ultrasonic transducer. To the authors\' knowledge, this makes the developed sensor the first known piezoresistive strain sensor which is capable of recording low-energy acoustic emissions. The improvements to the nondestructive evaluation and structural health monitoring arise from the sensor\'s low minimum detectable strain and wide-frequency bandwidth, which are generated from the improved fabrication process that permits crystalline semiconductor membranes and advanced polymers to be co-processed, thus enabling a dual-use application of both acoustic emission and static strain sensing. The sensor\'s ability to document quasi-static bending is also demonstrated and compared with an ultrasonic transducer, which provides no significant response. This dual-use application is proposed to effectively combine the uses of both strain and ultrasonic transducer sensor types within one sensor, making it a novel and useful method for nondestructive evaluations. The potential benefits include an enhanced sensitivity, a reduced sensor size, a lower cost, and a reduced instrumentation complexity.

To explore the relationship between pericardial meridian acupoints and brain, the electroencephalogram (EEG) signals were collected synchronously during transcutaneous electrical stimulation at PC3, PC5, PC7, and PC8 on the pericardial meridian in 21 healthy subjects. The cerebral cortex functional networks were constructed by standard low-resolution electromagnetic tomography (sLORETA), phase-locking value (PLV) and complex network methods. The prefrontal cortex (BA10), the orbitofrontal cortex (BA11), the middle temporal gyrus (BA21), the temporal gyrus (BA22), the temporal pole (BA38), the triangular part (BA44), the dorsolateral prefrontal cortex (BA46), and the inferior frontal cortex (BA47) were activated by electrical stimulation at PC3, PC5, PC7, and PC8 on the pericardium meridian. These activated brain regions are able to modulate both local and remote emotion and cognitive networks. Acupoint stimulation of pericardium meridian mainly activated the frontal and the temporal lobes. Compared with non-acupoint stimulation, the node degree in the frontal lobe of electrical stimulation at PC3 (p < 0.05), PC5 (p < 0.05), PC7 (p < 0.01), PC8 (p < 0.05) and the temporal lobe of PC3 (p < 0.05), PC5 (p < 0.05), PC7 (p < 0.05), PC8 (p < 0.01) were significantly increased. The clustering coefficient in the frontal lobe of the stimulation at PC3 (p < 0.05), PC5 (p < 0.05), PC7 (p < 0.01), PC8 (p < 0.05) and the temporal lobe of PC3 (p < 0.05), PC5 (p < 0.05), PC7 (p < 0.01), PC8 (p < 0.05) were significantly increased. The characteristic path length decreased and the global efficiency increased during acupoint stimulation. The changes of functional network of stimulated pericardium meridian through cerebral cortex may provide theoretical support for the specificity of meridian and acupoints.

Acoustic emission (AE) technology is a non-destructive testing (NDT) technique that is able to monitor the process of hydrogen-induced cracking (HIC). AE uses piezoelectric sensors to convert the elastic waves generated from the growth of HIC into electric signals. Most piezoelectric sensors have resonance and thus are effective for a certain frequency range, and they will fundamentally affect the monitoring results. In this study, two commonly used AE sensors (Nano30 and VS150-RIC) were used for monitoring HIC processes using the electrochemical hydrogen-charging method under laboratory conditions. Obtained signals were analyzed and compared on three aspects, i.e., in signal acquisition, signal discrimination, and source location to demonstrate the influences of the two types of AE sensors. A basic reference for the selection of sensors for HIC monitoring is provided according to different test purposes and monitoring environments. Results show that signal characteristics from different mechanisms can be identified more clearly by Nano30, which is conducive to signal classification. VS150-RIC can identify HIC signals better and provide source locations more accurately. It can also acquire low-energy signals better, which is more suitable for monitoring over a long distance.

The most common source of transformer failure is in the insulation, and the most prevalent warning signal for insulation weakness is partial discharge (PD). Locating the positions of these partial discharges would help repair the transformer to prevent failures. This work investigates algorithms that could be deployed to locate the position of a PD event using data from ultra-high frequency (UHF) sensors inside the transformer. These algorithms typically proceed in two steps: first determining the signal arrival time, and then locating the position based on time differences. This paper reviews available methods for each task and then propose new algorithms: a convolutional iterative filter with thresholding (CIFT) to determine the signal arrival time and a reference table of travel times to resolve the source location. The effectiveness of these algorithms are tested with a set of laboratory-triggered PD events and two sets of simulated PD events inside transformers in production use. Tests show the new approach provides more accurate locations than the best-known data analysis algorithms, and the difference is particularly large, 3.7X, when the signal sources are far from sensors.

To explore the association between high-frequency oscillations (HFOs) and epilepsy types and to improve the accuracy of source localization.
Magnetoencephalography (MEG) ripples of 63 drug-resistant epilepsy patients were detected. Ripple rates, distribution, spatial complexity, and the clustering coefficient of ripple channels were used for the preliminary classification of lateral temporal lobe epilepsy (LTLE), mesial temporal lobe epilepsy (MTLE), and nontemporal lobe epilepsy (NTLE), mainly frontal lobe epilepsy (FLE). Furthermore, the seizure site identification was improved using the Tucker LCMV method and source-level betweenness centrality.
Ripple rates were significantly higher in MTLE than in LTLE and NTLE (p < 0.05). The LTLE and MTLE were mainly distributed in the temporal lobe, followed by the parietal lobe, occipital lobe, and frontal lobe, whereas MTLE ripples were mainly distributed in the frontal lobe, then parietal lobe and occipital lobe. Nevertheless, the NTLE ripples were primarily in the frontal lobe and partially in the occipital lobe (p < 0.05). Meanwhile, the spatial complexity of NTLE was significantly higher than that of LTLE and MTLE and was lowest in MTLE (p < 0.01). However, an opposite trend was observed for the standardized clustering coefficient compared with spatial complexity (p < 0.01). Finally, the tucker algorithm showed a higher percentage of ripples at the surgical site when the betweenness centrality was added (p < 0.01).
This study demonstrated that HFO rates, distribution, spatial complexity, and clustering coefficient of ripple channels varied considerably among the three epilepsy types. Additionally, tucker MEG estimation combined with ripple rates based on the source-level functional connectivity is a promising approach for presurgical epilepsy evaluation.

The acoustic emission (AE) technique is one of the most widely used in the field of structural monitoring. Its popularity mainly stems from the fact that it belongs to the category of non-destructive techniques (NDT) and allows the passive monitoring of structures. The technique employs piezoelectric sensors to measure the elastic ultrasonic wave that propagates in the material as a result of the crack formation\'s abrupt release of energy. The recorded signal can be investigated to obtain information about the source crack, its position, and its typology (Mode I, Mode II). Over the years, many techniques have been developed for the localization, characterization, and quantification of damage from the study of acoustic emission. The onset time of the signal is an essential information item to be derived from waveform analysis. This information combined with the use of the triangulation technique allows for the identification of the crack location. In the literature, it is possible to find many methods to identify, with increasing accuracy, the onset time of the P-wave. Indeed, the precision of the onset time detection affects the accuracy of identifying the location of the crack. In this paper, two techniques for the definition of the onset time of acoustic emission signals are presented. The first method is based on the Akaike Information Criterion (AIC) while the second one relies on the use of artificial intelligence (AI). A recurrent convolutional neural network (R-CNN) designed for sound event detection (SED) is trained on three different datasets composed of seismic signals and acoustic emission signals to be tested on a real-world acoustic emission dataset. The new method allows taking advantage of the similarities between acoustic emissions, seismic signals, and sound signals, enhancing the accuracy in determining the onset time.

The cerebral cortex functional network of Electroencephalogram (EEG) signals during transcutaneous electrical acupoint stimulation (TEAS) on 21 healthy subjects was constructed by using three modules: standard low-resolution brain electromagnetic tomography (sLORETA), phase-locking value (PLV), and complex network. We investigated the brain functional network triggered by PC7 stimulation by comparing with resting state and non-acupoint stimulation. The results showed that the PC7 stimulation mainly activated frontal lobe and temporal lobe including prefrontal cortex (BA10), insular lobe (BA13), temporal gyrus (BA22), anterior cingulate cortex (BA32), temporal pole (BA38), dorsolateral prefrontal cortex (BA46), and inferior frontal cortex (BA47), which are all closely linked to cognition, spirit, and emotion in brain. Furthermore, the degrees of node in frontal, temporal, and whole brain are increased significantly or extreme significantly with p < 0.05, p < 0.05, and p < 0.01, respectively; clustering coefficient in frontal, temporal, and whole brain are all statistically significant (p < 0.05). The information transmission efficiency of cerebral cortex has been greatly improved. During PC7 stimulation, the topological changes in the activation of cerebral regions and cortical functional networks are consistent with the therapeutic effect, which may provide theoretical support for acupoint stimulation to regulate nerve function.

One of the most significant benefits of Acoustic Emission (AE) testing over other Non-Destructive Evaluation (NDE) techniques lies in its damage location capability over a wide area. The delta-T mapping technique developed by researchers has been shown to enable AE source location to a high level of accuracy in complex structures. However, the time-consuming and laborious data training process of the delta-T mapping technique has prevented this technique from large-scale application on large complex structures. In order to solve this problem, a Finite Element (FE) method was applied to model training data for localization of experimental AE events on a complex plate. Firstly, the FE model was validated through demonstrating consistency between simulated data and the experimental data in the study of Hsu-Nielsen (H-N) sources on a simple plate. Then, the FE model with the same parameters was applied to a planar location problem on a complex plate. It has been demonstrated that FE generated delta-T mapping data can achieve a reasonable degree of source location accuracy with an average error of 3.88 mm whilst decreasing the time and effort required for manually collecting and processing the training data.

Objective: Childhood epilepsy with centrotemporal spikes (CECTS), the most common childhood epilepsy, still lacks longitudinal imaging studies involving antiepileptic drugs (AEDs). In order to examine the effect of AEDs on cognition and brain activity. We investigated the neuromagnetic activities and cognitive profile in children with CECTS before and after 1 year of treatment. Methods: Fifteen children with CECTS aged 6-12 years underwent high-sampling magnetoencephalography (MEG) recordings before treatment and at 1 year after treatment, and 12 completed the cognitive assessment (The Wechsler Intelligence Scale for Children). Next, magnetic source location and functional connectivity (FC) were investigated in order to characterize interictal neuromagnetic activity in the seven frequency sub-bands, including: delta (1-4 Hz), theta (4-8 Hz), alpha (8-12 Hz), beta (12-30 Hz), gamma (30-80 Hz), ripple (80-250 Hz), and fast ripple (250-500 Hz). Results: After 1 year of treatment, children with CECTS had increased scores on full-scale intelligence quotient, verbal comprehension index (VCI) and perceptual reasoning index (PRI). Alterations of neural activity occurred in specific frequency bands. Source location, in the 30-80 Hz frequency band, was significantly increased in the posterior cingulate cortex (PCC) after treatment. Moreover, FC analysis demonstrated that after treatment, the connectivity between the PCC and the medial frontal cortex (MFC) was enhanced in the 8-12 Hz frequency band. Additionally, the whole-brain network distribution was more dispersed in the 80-250 Hz frequency band. Conclusion: Intrinsic neural activity has frequency-dependent characteristic. AEDs have impact on regional activity and FC of the default mode network (DMN). Normalization of aberrant DMN in children with CECTS after treatment is likely the reason for improvement of cognitive function.