Frequency Band Research Articles

Searching the InSight Seismic Data for Mars’s Background-Free Oscillations

Abstract Mars’s atmosphere has theoretically been predicted to be strong enough to continuously excite Mars’s background-free oscillations, potentially providing an independent means of verifying radial seismic body-wave models of Mars determined from marsquakes and meteorite impacts recorded during the Interior Exploration using Seismic Investigations, Geodesy, and Heat Transport (InSight) mission. To extract the background-free oscillations, we processed and analyzed the continuous seismic data, consisting of 966 Sols (a Sol is equivalent to a Martian day), collected by the Mars InSight mission using both automated and manual deglitching schemes to remove nonseismic disturbances. We then computed 1-Sol-long autocorrelations for the entire data set and stacked these to enhance any normal-mode peaks present in the spectrum. We find that while peaks in the stacked spectrum in the 2–4 mHz frequency band align with predictions based on seismic body-wave models and appear to be consistent across the different processing and stacking methods applied, unambiguous detection of atmosphere-induced free oscillations in the Martian seismic data nevertheless remains difficult. This possibly relates to the limited number of Sols of data that stack coherently and the continued presence of glitch-related signal that affects the seismic data across the normal-mode frequency range (∼1–10 mHz). Improved deglitching schemes may allow for clearer detection and identification in the future.

Layout design of densest weakly coupled multi-core fibers to minimize the network blocking rate

The suppression of inter-core crosstalk (IC-XT) that affects each lightpath is crucial for resource allocation in space-division multiplexing elastic optical networks (SDM-EONs) with multi-core fibers (MCFs). Resource allocation approaches that limit the simultaneous use of adjacent cores in the same frequency band to the MCFs composing each lightpath have been widely adopted to suppress IC-XT. However, in principle, such methods are inefficient because they cannot fully utilize all cores. This study examines the core density from the perspective of the core layout in weakly coupled MCFs and the IC-XT suppression requirement. The densest MCF layout maximizes the network capacity while restricting the amount of IC-XT within the tolerance threshold for each lightpath. Specifically, we propose an XT-free condition, maintaining the IC-XT to each lightpath within the acceptable tolerance level. In addition, we evaluated numerous MCFs that satisfy or do not satisfy the XT-free condition with various network topologies and cladding diameters. This evaluation also validates the IC-XT reduction performance of the proposed framework compared with that of the conventional resource-allocation approach. Here, we incorporate our indirect IC-XT calculation method that affects lightpaths from other cores via its nearest cores, which was overlooked in the resource allocation problem. Based on these comprehensive examinations, we propose a method to determine the densest core layout for a given network topology and route and modulation format selection algorithm.

Gamma frequency connectivity in frontostriatal networks associated with social preference is reduced with traumatic brain injury

Abstract Among the myriad of complications associated with traumatic brain injury (TBI), impairments in social behaviors and cognition have emerged as a significant area of concern. Animal models of social behavior are necessary to explore the underlying brain mechanisms contributing to chronic social impairments following brain injury. Here, we utilize large-scale brain recordings of local field potentials to identify neural signatures linked with social preference deficits following frontal brain injury. We used a controlled cortical impact model of TBI to create a severe bilateral injury centered on the prefrontal cortex. Behavior (social preference and locomotion) and brain activity (power and coherence) during a three-chamber social preference task were compared between sham and injured animals. Sham rats preferred to spend time with a social conspecific over an inanimate object. An analysis of local field oscillations showed that social preference was associated with a significant increase in coherence in gamma frequency band across widespread brain regions in these animals. Animals with a frontal TBI showed a significant reduction in this social preference, visiting an inanimate object more frequently and for more time. Reflecting these changes in social behavior, these animals also showed a significant reduction in gamma frequency (25–60 Hz) coherence associated with social preference.

Machine-Learning-Based Depression Detection Model from Electroencephalograph (EEG) Data Obtained by Consumer-Grade EEG Device

Background/Objectives: There have been attempts to detect depression using medical-grade electroencephalograph (EEG) data based on a machine learning approach. EEG has garnered interest as a method for assessing brainwaves by attaching electrodes to the scalp to obtain electrical activity in the brain. Recently, machine learning has been applied to the EEG data to detect depression, with encouraging results. Specifically, studies using medical-grade EEG data have shown that depression can be accurately detected. However, there is a need to expand the range of applications by achieving a score with machine learning using simpler consumer-grade brain wave sensors. At present, a sufficient score has not been achieved.; Methods: To improve the score of depression detection, we quantified various EEG indices to train models such as power spectrum, asymmetry, complexity, and functional connectivity. In addition, feature selection was performed to ensure that the model learns only promising EEG indices for depression detection. The feature selection methods were Light Gradient Boosting Machine (LightGBM) feature importance, mutual information, ReliefF and ElasticNet coefficients. The selected EEG indices were learned by the LightGBM model, which is reported to be as accurate as the latest deep learning models. In cross-validation, the independence of test and training data was ensured to avoid excessively calculated score; Results: The results showed that the Macro F1 score was 91.59%, suggesting that a consumer-grade EEG can detect depression. In addition, analysis of the EEG indices selected by feature selection indicated that the Macro F1 score was about 80% for single EEG indices such as differential entropy in the frequency band β and functional connectivity in the left frontal region in the frequency band 1–128 Hz; Conclusions: Although the data were obtained from a consumer-grade EEG, the results suggest that these EEG indices are promising for detection depression.

Feed system tracking receive/transmit shaped Cassegrain antenna C/K bands

Background. The need to create, space communication stations based on mobile carriers, for example, on ships, requires the use of multi-band reflector antennas irradiating systems that ensure the combination of not only channels for receiving and transmitting high-frequency signals, but also direction-finding channels for constructing a monopulse tracking system. Aim. Study of the possibility of creating an irradiation system that ensures the combination of reception and transmission of signals in the multi-band reflector antenna in spaced ranges with frequency bands C(Rx) – 21 %, C(Tx) – 16 % and K(Rx) – 21 %, with the implementation of a monopulse angular automatic tracking system in both receiving ranges. Methods. Development of a three-band irradiation system that ensures the implementation of automatic tracking in both receiving ranges. Analysis of the characteristics of a three-band irradiation system that ensures the implementation of reception and automatic tracking in both receiving ranges. Results. Development of a three-band irradiation system that ensures the implementation of automatic tracking in both receiving ranges. Analysis of the characteristics of a three-band irradiation system that ensures the implementation of reception and automatic tracking in both receiving ranges. Conclusion. The irradiation system of the multi-band reflector antenna for mirrors with profiled surfaces is proposed, with the implementation of combined signal reception in the C(Rx) and K(Rx) frequency ranges with a 21 % band and signal transmission in the C(Tx) frequency range with a 16% band, and the formation of partial RPs in both receiving ranges for the implementation of a monopulse angular tracking system. The following characteristics of the irradiation system are implemented: cross-polarization isolation of more than 30 dB, the formation of identical partial RPs and a stable tracking mode based on signals from onboard repeaters in communication systems with frequency reuse.

Analysis of the Flash Phenomenon in Rotor Targets with a Scatter Point Model

This paper thoroughly investigates the occurrence of the flash phenomenon in rotor targets. The flash phenomenon is discernible in rotor targets across the time and frequency domains. This phenomenon is characterized by the modulation of signal amplitude through the Singer function, resulting in periodic peaks in the time domain. The appearance of these peaks in the time domain corresponds to the presence of a corresponding frequency band in the time–frequency domain. The influence of interference between scattering points on the amplitude and phase of echo is investigated by employing the echo scattering point model and the echo analytical expression as analytical foundations. A more comprehensive examination of the mechanisms that contribute to the emergence of the flash phenomenon in the time and time–frequency domains is undertaken. Furthermore, its objective is to verify the validity of the analysis performed on the mechanism of the phenomenon. Based on theoretical investigations and simulation results, it can be concluded that the flash phenomenon observed in rotor targets is due to interference between particular scattering points. Fundamentally, it can be comprehended as a phenomenon of interference. This research achievement has specific theoretical and practical value in detecting and recognizing rotor targets.

Continuous and discrete decoding of overt speech with scalp electroencephalography (EEG).

Neurological disorders affecting speech production adversely impact quality of
life for over 7 million individuals in the US. Traditional speech interfaces like eyetracking
devices and P300 spellers are slow and unnatural for these patients. An
alternative solution, speech Brain-Computer Interfaces (BCIs), directly decodes speech
characteristics, offering a more natural communication mechanism. This research
explores the feasibility of decoding speech features using non-invasive EEG. Nine
neurologically intact participants were equipped with a 63-channel EEG system
with additional sensors to eliminate eye artifacts. Participants read aloud sentences
displayed on a screen selected for phonetic similarity to the English language. Deep
learning models, including Convolutional Neural Networks and Recurrent Neural
Networks with and without attention modules, were optimized with a focus on
minimizing trainable parameters and utilizing small input window sizes for real-time
application. These models were employed for discrete and continuous speech decoding
tasks, achieving statistically significant participant-independent decoding performance
for discrete classes and continuous characteristics of the produced audio signal. A
frequency sub-band analysis highlighted the significance of certain frequency bands
(delta, theta, and gamma) for decoding performance, and a perturbation analysis
was used to identify crucial channels. Assessed channel selection methods did not
significantly improve performance, suggesting a distributed representation of speech
information encoded in the EEG signals. Leave-One-Out training demonstrated
the feasibility of utilizing common speech neural correlates, reducing data collection
requirements from individual participants.

Are carbon costs transmitting to the building materials industry?

IntroductionReconciling economic development and emission reduction of polluting gases requires balancing long-term and short-term goals across various markets. As a new production cost, the price of carbon allowances is assumed to affect the supply and demand of carbon-intensive sectors. Therefore, this study examines the dynamic price spillover effects between China as the largest carbon emission market and the carbon-intensive building materials industry in a unified time-frequency framework.MethodsThis study measures the dynamic overall and directional spillover effects of carbon and building materials markets in three frequency bands, considering eight carbon pilots and the national carbon market in China and four important building materials.ResultsThe empirical results show that the bi-directional spillover between carbon and the building materials market shows pronounced characteristics in the time-frequency domain, especially in the short-term frequency band of one day to one week, with strong connectivity. After the launch of the national carbon market, the information spillover from the building materials market to the carbon pilots become stronger. Both the carbon pilots and the national carbon market have significant short- and long-term impacts on the building materials market. In addition, there are differences in the impact of carbon markets on various types of carbon intensive building materials.DiscussionCompliance cycles in carbon markets are likely to induce sharp fluctuations in spillovers between the two markets. Therefore, balancing industrial development and stabilizing carbon prices requires a refined policy design that considers the diversified impacts of carbon markets on different industries at across frequencies.

Study on the influence mechanism of gate oxide degradation on DM EMI signals in SiC MOSFET

This paper investigates gate oxide degradation and its influence on Differential Mode(DM) EMI signals. The study reveals that changes in the parasitic capacitance within the chip, resulting from gate oxide degradation, can modify the amplitude-frequency characteristics of DM EMI signals, leading to unexpected transmission outcomes. This paper subjects SiC MOSFET modules to periodic high-temperature gate bias stress, extracts the spectrum characteristics of DM EMI, and assesses the impact of junction temperature. The analysis explores the impact of gate oxide degradation on low and high frequency DM EMI signals. Experimental results reveal distinct sensitivities of amplitude-frequency characteristics in the DM EMI signals to temperature variations and responses to gate oxide degradation at various frequency bands. The extrapolation of the correlation between the frequency-domain characteristics of the DM EMI signals and the degree of gate oxide degradation introduces a novel approach to evaluate the lifespan of power electronic devices.

Negative impact of vibration on process pipelines of a compressor station with electrically driven gas pumping units

Relevance. In gas industry, the amount of equipment that has outlived its intended service life is increasing every year. In accordance with the law “On Industrial Safety of Hazardous Production Facilities,” compressor stations are dangerous objects, the reliable operation of which largely determines the condition of main gas pipelines. Technological pipelines of a compressor station are important objects of this system. They are subject to strict requirements due to the action of static and dynamic loads on them. Reliability of compressor stations is based on the results of a study of the condition of the object and the main factors affecting increased wear of equipment. The solution to wear problem is timely monitoring of equipment, in particular level measurement and determining the causes of pipeline vibrations using vibration diagnostics methods, and frequency and modal analysis methods. Aim. To study the causes of increased dynamic loads on the technological piping that arise during the operation of electric and gas pumping units at a compressor station using vibration monitoring methods. Methods. Vibration monitoring methods to analyze the causes of increased vibration values in the process piping of a compressor station with an electric gas pumping unit. Results and conclusions. The authors have measured vibration in a wide frequency band and assessed the technological condition of the main equipment of the compressor station with EGPA-6.3/8200-56/1.44-R based on the regulatory documentation STO Gazprom 2-2.3-324-2009. The main frequencies of the root-mean-square value of the vibration velocity, at which the highest vibration values were observed, were identified, and a modal analysis was performed using ANSYS software. Based on the results of natural studies and modal analysis, it was concluded about the occurrence of resonant high-frequency oscillations, multiple of the rotor rotation frequency.

A hybrid equivalent circuit model and plane wave spectrum method for decoupling surface designs in multi‐band shared‐aperture antenna arrays

AbstractShared‐aperture antenna arrays are attractive for 5G base stations, owing to their compact size and multi‐band frequency coverage. Decoupling surfaces are effective for suppressing crossband mutual coupling. However, their designs typically rely on full‐wave simulations of scattering parameters, which may not ensure a good radiation performance and is time‐consuming. A systematic methodology based on the hybrid equivalent circuit model and plane wave spectrum method is proposed for decoupling surface designs. The decoupling surface is represented by its equivalent circuit and the spectral expression converts the electromagnetic field into a circuit problem. Both the scattering parameter and radiation pattern can be solved with the circuit, offering an efficient design method that ensures consistent radiation patterns between the model and full‐wave simulations. A double‐layer decoupling surface with wide stopbands, high transmission property, and sharp roll‐off transition was realised. Furthermore, to reduce the overall profile height, the reflection phase of the decoupling surface was tuned. A triple‐band shared‐aperture base station antenna array was developed. Simulated and measured results demonstrate the array works properly in the 0.69–0.96 GHz, 1.7–2.7 GHz, and 3.3–3.8 GHz frequency bands with stable radiation patterns, and crossband mutual coupling is well suppressed, validating the effectiveness of the proposed methodology.

Neural encoding of melodic expectations in music across EEG frequency bands.

The human brain tracks regularities in the environment and extrapolates these to predict future events. Prior work on music cognition suggests that low-frequency (1-8Hz) brain activity encodes melodic predictions beyond the stimulus acoustics. Building on this work, we aimed to disentangle the frequency-specific neural dynamics linked to melodic prediction uncertainty (modelled as entropy) and prediction error (modelled as surprisal) for temporal (note onset) and content (note pitch) information. By using multivariate temporal response function (TRF) models, we re-analysed the electroencephalogram (EEG) from 20 subjects (10 musicians) who listened to Western tonal music. Our results show that melodic expectation metrics improve the EEG reconstruction accuracy in all frequency bands below the gamma range (< 30 Hz). Crucially, we found that entropy contributed more strongly to the reconstruction accuracy enhancement compared to surprisal in all frequency bands. Additionally, we found that the encoding of temporal, but not content, information metrics was not limited to low frequencies, rather it extended to higher frequencies (> 8Hz). An analysis of the TRF weights revealed that the temporal predictability of a note (entropy of note onset) may be encoded in the delta- (1-4Hz) and beta-band (12-30 Hz) brain activity prior to the stimulus, suggesting that these frequency bands associate with temporal predictions. Strikingly, we also revealed that melodic expectations selectively enhanced EEG reconstruction accuracy in the beta band for musicians, and in the alpha band (8-12 Hz) for non-musicians, suggesting that musical expertise influences the neural dynamics underlying predictive processing in music cognition.

Miniaturized circularly polarized wearable array antenna for medical device applications

Antenna miniaturization is essential for healthcare applications, and numerous studies have tackled the challenge of presenting miniaturized, reliable designs while maintaining performance. This paper presents a small circularly polarized (CP) sequential wearable array antenna with overall dimensions of only 110 mm × 95 mm × 1.8 mm. It comprises four novel designs of circular-shaped elements arranged sequentially in an array configuration measured only 24 mm×24 mm×1 mm. It is fed by a separate cascade feeding network incorporating a single rat-race and two branch-line couplers. The antenna is designed for medical device applications within the 2.4 GHz Industrial Scientific Medical (ISM) frequency band. The proposed design is fabricated and experimentally tested, demonstrating wide impedance bandwidths of 21.24% (2.2–2.72 GHz) and an Axial Ratio (AR) bandwidth (AR < 3 dB) covering the entire 2.4 GHz ISM frequency band. At 2.43 GHz, the antenna achieves a gain of -14.9 dBi. Both simulation and experimental results confirm excellent performance in impedance matching, gain pattern, and circular polarization, making it promising for wearable wireless communication in medical applications. The link margin was calculated, and the specific absorption rate of the antenna was analyzed, the result revealing that it aligns with the safety limits of IEEE C95.1-1999 standards.

Review of QoS-aware resource allocation schemes for 5g networks

Resource management in wireless networks refers to the processes and techniques used to allocate, control, and optimize the various resources in a wireless communication system to ensure efficient and reliable operation. These resources typically include spectrum (frequency bands), power, time slots, and network infrastructure (e.g., base stations, access points). Effective resource management is crucial for maximizing network performance, ensuring Quality of Service (QoS), and providing a good user experience, especially in environments with limited resources and high demand. Quality of Service (QoS) refers to the ability of the network to provide different levels of service quality to various types of traffic, applications, and users. QoS in 5G is essential due to the diverse range of services the network is designed to support, each with its own specific requirements for performance metrics such as latency, reliability, bandwidth, and availability. In this paper, we study how the QoS-aware resource allocation procedure is handled in wireless networks. Different resource management techniques were studied and also the paper looked at some of the existing QoS-aware resources allocation schemes by highlighting the operation, strength and weakness and the QoS parameters considered by each of the schemes.

Analysis of 3D Printed Dielectric Resonator Antenna Arrays for Millimeter-Wave 5G Applications

This paper explores the potential use of fused deposition modeling (FDM) technology for manufacturing microwave and millimeter-wave dielectric resonator antennas (DRAs) for 5G and beyond communication systems. DRAs operating at microwave and millimeter-wave (mmWave) frequency bands were simulated, fabricated, and analyzed in terms of manufacturing quality and radio frequency (RF) performance. Samples were manufactured using a 3D printer and PREPERM® ABS1000 filament, which offers a stable dielectric constant (εr = 10 ± 0.35) and low losses (tan δ = 0.003) over wide frequency and temperature ranges. Surface profile tests and microscope measurements revealed discrepancies in the dimensions in the xy-plane and along the z-axis, consistent with the observed shift in resonant frequency. Despite these variations, reasonably good agreement between RF-simulated and measured results was achieved, and the DRA array successfully covered the intended mmWave band. However, challenges in achieving high precision may restrict applications at higher mmWave bands. Nevertheless, compared with conventional methods, FDM techniques offer a highly accessible and flexible solution with a wide range of materials for home and micro-manufacturing of mmWave DRAs for modern 5G systems.

Differential modulations of theta and beta oscillations by audiovisual congruency in letter-speech sound integration.

The integration of visual letters and speech sounds is a crucial part of learning to read. Previous studies investigating this integration have revealed a modulation by audiovisual (AV) congruency, commonly known as the congruency effect. To investigate the cortical oscillations of the congruency effects across different oscillatory frequency bands, we conducted a Japanese priming task in which a visual letter was followed by a speech sound. We analyzed the power and phase properties of oscillatory activities in the theta and beta bands between congruent and incongruent letter-speech sound (L-SS) pairs. Our results revealed stronger theta-band (5-7Hz) power in the congruent condition and cross-modal phase resetting within the auditory cortex, accompanied by enhanced inter-trial phase coherence (ITPC) in the auditory-related areas in response to the congruent condition. The observed congruency effect of theta-band power may reflect increased neural activities in the left auditory region during L-SS integration. Additionally, theta ITPC findings suggest that visual letters amplify neuronal responses to the following corresponding auditory stimulus, which may reflect the differential cross-modal influences in the primary auditory cortex. In contrast, decreased beta-band (20-35 Hz) oscillatory power was observed in the right centroparietal regions for the congruent condition. The reduced beta power seems to be unrelated to the processing of AV integration, but may be interpreted as the brain response to predicting auditory sounds during language processing. Our data provide valuable insights by indicating that oscillations in different frequency bands contribute to the disparate aspects of L-SS integration.

Fusing convolutional learning and attention-based Bi-LSTM networks for early Alzheimer’s diagnosis from EEG signals towards IoMT

The Internet of Medical Things (IoMT) is poised to play a pivotal role in future medical support systems, enabling pervasive health monitoring in smart cities. Alzheimer’s disease (AD) afflicts millions globally, and this paper explores the potential of electroencephalogram (EEG) data in addressing this challenge. We propose the Convolutional Learning Attention-Bidirectional Time-Aware Long-Short-Term Memory (CL-ATBiLSTM) model, a deep learning approach designed to classify different AD phases through EEG data analysis. The model utilizes Discrete Wavelet Transform (DWT) to decompose EEG data into distinct frequency bands, allowing for targeted analysis of AD-related brain activity patterns. Additionally, the data is segmented into smaller windows to handle the dynamic nature of EEG signals, and these segments are transformed into spectrogram images, visually depicting brain activity distribution over time and frequency. The CL-ATBiLSTM model incorporates convolutional layers to capture spatial features, attention mechanisms to emphasize crucial data, and BiLSTM networks to explore temporal relationships within the sequences. To optimize the model’s performance, Bayesian optimization is employed to fine-tune the hyperparameters of the ATBiLSTM network, enhancing its ability to generalize and accurately classify AD stages. Incorporating Bayesian learning ensures the most effective model configuration, improving sensitivity and specificity for identifying AD-related patterns. Our model extracts discriminative features from EEG data to differentiate between AD, Mild Cognitive Impairment (MCI), and healthy controls (CO), offering a more comprehensive approach than existing two-class detection algorithms. By including the MCI category, our method facilitates earlier identification and potentially more impactful therapy interventions. Achieving a 96.52% accuracy on Figshare datasets containing AD, MCI, and CO groups, our approach demonstrates strong potential for practical use, accelerating AD identification, enhancing patient care, and contributing to the development of targeted treatments for this debilitating condition.

A compact dual‐band omnidirectional antenna with folded microstrip lines and slotlines for 5G applications

AbstractA compact dual‐band omnidirectional antenna with folded microstrip lines and slotlines is proposed. The antenna is composed of a substrate and a superstrate. The superstrate is employed to broaden the antenna bandwidth. The radiating patch is situated on the upper surface of the substrate and consists of folded microstrip lines. The performance of the high‐frequency band can be optimized based on the analysis of the current distribution intensity. The bottom of the substrate is composed of a ground plane with slotlines. The introduction and improvement of the low‐frequency band are achieved by modifying the ground plane structure with characteristic modal analysis. The antenna demonstrates omnidirectional radiation characteristics across both frequency bands. The dimensions of the antenna are π × 0.178 λ0 × 0.178 λ0 × 0.049 λ0 (π × 22 × 22 × 6 mm). The antenna achieves a maximum gain of 3.37 dBi at 2.5 GHz and 2.74 dBi at 5.8 GHz. The measured impedance bandwidths for the two frequency bands are 17.3% and 21.7%, respectively.

Advanced Frequency Analysis of Signals with High-Frequency Resolution

In today’s era, it is important to analyze and utilize various signals in industrial or laboratory applications. Measured signals provide critical information about the controlled system, which can be contained precisely within a narrow frequency range. Many methods and algorithms exist to process such signals in both the time and frequency domains. In particular, signal processing in the frequency domain is primary in industrial practice because dominant components within a specific narrow frequency band are sought. The discrete Fourier transformation (DFT) algorithm is the tool used in practice to find these frequency components. The DFT algorithm provides the full frequency spectrum with a higher number of calculation steps, and its spectrum frequency resolution is low. Therefore, research has focused on finding a method to achieve high-frequency spectrum resolution. An important factor in selecting the technique was that such an algorithm should be implementable on a microprocessor-based system under harsh industrial conditions. Research results showed that the DFT ZOOM method meets these requirements. The frequency zoom has many advantages but requires some modification. It is implemented in high-performance analyzers, but a thorough and detailed description of the respective algorithm is lacking in technical articles and literature. This article mathematically and theoretically describes the modified frequency zoom algorithm in detail. The steps of the frequency zoom, from creating an analytical signal through frequency shifting and decimation to the frequency analysis of the signal, are realized. The algorithm allows for the analysis of a signal with high-frequency resolution in a limited frequency band. A significant modification of DFT ZOOM is that of using the Hilbert transform to create an analytic signal. This resolves the aliasing issue caused by the overlap between fundamental and sideband spectra. Results from processing deterministic and stochastic signals using the modified DFT ZOOM are presented. The presented experimental results contribute to a more detailed frequency analysis of the signal. As part of this scientific research, the issues of frequency zoom were thoroughly addressed, solving the partial problems of this algorithm, both in theory and in the context of signal theory.

Quantitative EEG biomarkers for STXBP1-related disorders.

EEG patterns and quantitative EEG (qEEG) features have been poorly explored in monogenic epilepsies. Herein, we investigate regional differences in EEG frequency composition in patients with STXBP1 developmental and epileptic encephalopathy (STXBP1-DEE). We conducted a retrospective study collecting electroclinical data of patients with STXBP1-DEE and two control groups of patients with DEEs of different etiologies and typically developing individuals matched for age and sex. We performed a (1) visual EEG assessment, (b) qEEG analysis, and (c) electrical source imaging (ESI). We quantified the relative power (RP) of four frequency bands (α β, θ, δ), in two electrode groups (anterior/posterior), and compared their averages and dynamics (standard deviation [SD] over time). The ESI was performed by applying the standard Distributed Source Modeling algorithm. We analyzed 42 EEG studies in 19 patients with STXBP1-DEE (10 female), with a median age at recordings of 9.6 years (range 9 months to 29 years). The δRP was higher in recordings of STXBP1-DEE (p < .001) compared to both control groups, suggesting the pathogenicity and STXBP1-specificity of these findings. In STXBP1-DEE, the δRP was significantly higher in the anterior electrode group compared to the posterior one (p = .003). There was no correlation between the anterior δRP and the epilepsy focus, age at recordings, and concomitant medications The ESI modeling of this activity showed a widespread involvement of the dorsomesial frontal cortex, suggesting a large corticosubcortical pathologic network. Finally, we identified two groups of recordings: cluster.1 with higher anterior δRP and low dynamics and cluster.2 with lower δRP and higher dynamics. Patients in cluster.1 had a more severe epilepsy and neurological phenotype compared to patients in cluster 2. The qEEG analysis showed a predominant frontal slow activity as a specific STXBP1 feature that correlates with the severity of the phenotype and may represent a biomarker for prospective longitudinal studies of STXBP1-DEE.