Frequency Band Research Articles

Implementation of machine learning for the design of spiral shaped multiband monopole antenna for MICS/IEEE802.11a/IEEE802.11b applications

This paper presents a method to reduce the size of a compact antenna for MICS and IEEE 802.11a/b applications. Initially a monopole antenna (45 × 40 × 1.6 mm³) is designed for the ISM band (2.4–2.5 GHz). A spiral meandered single patch is incorporated to lower the operational frequency to 600 MHz assuring significant antenna size reduction. Further enhancements include a two-sided spiral extension and a T-shaped arm around the microstrip feed, enabling operation across three frequency bands achieving an overall 84% size reduction with improved gain. The prototype meets FCC standards for Specific Absorption Rate (SAR). To optimize gain, machine learning models along-with LASSO, Ridge, and Random Forest regression algorithm are used. The LASSO model proves most effective, achieving gains of 6.6629 dBi at 400 MHz, 7.6225 dBi at 2.45 GHz, and 8.7569 dBi at 5.5 GHz, with fractional bandwidths of 22%, 27%, and 8% respectively.

Compensation of complex environmental magnetic field disturbances for biomagnetic field measurements

Abstract Background magnetic field compensation can effectively improve the signalto-noise ratio of biomagnetic field measurements. But for complex environmental disturbances, creating a stable near-zero magnetic field environment is a challenging topic. Therefore, this paper constructs a background magnetic field compensation system based on the Improved Linear Expanded State Observer (ILESO), which effectively reduces the magnetic field fluctuations. Firstly, the system model is obtained based on the input and output characteristics of the system. Then, an improved linear extended state observer is designed to observe and compensate the environmental disturbance, and the adaptive rate is established to balance the estimation capability of the observer and the measurement noise. Finally, the experimental results validate the effectiveness of the proposed method. For 1 Hz disturbances, the peak-to-peak magnetic field value was reduced from 3.71 nT to 0.33 nT. Under the influence of external environmental magnetic field disturbances with an amplitude of 3417 nT within the 0-15 Hz frequency band, the internal magnetic field within the MSR remains consistently stable within 1 nT. These results have a significant reference for obtaining an ultra-stable, near-zero environment.

Compact wide band quad element MIMO antenna with inbuilt isolator for 5G applications

Abstract A compact quad-element multiple-input multiple-output (MIMO) antenna with self-isolation and pattern diversity features for 5G applications has designed in this article. In the proposed MIMO antenna design, drop-shaped slots have been cut from the ground plane to attain wide bandwidth of 1 GHz in the operating band 3.3-4.3 GHz of near radio (NR-77) band for 5G applications. Further, a criss-cross shaped isolator is formed after integrating four single wideband antenna elements with different orientations in the ground plane. This shape is responsible for high self-isolation of 25 dB in the desired band of 5G. Further, the 2-D radiation pattern of the MIMO antenna has pattern diversity characteristics with a high peak gain of 6 dB in the working band, which validates the proposed work for the 5G MIMO application. The diversity performance of the proposed MIMO antenna has been validated with measured envelope correlation coefficient of 0.0002, diversity gain of 10 and channel capacity loss is below 0.5 bits/sec/Hz across the entire frequency band. Furthermore, simulation studies have been demonstrated for different practical applications such as housing, automotive and on body for practical utility of proposed prototype.

Shear-wave velocity structure from the near-surface to mid-crust beneath the southern Korean Peninsula using P-wave polarization analysis

Abstract The near-surface velocity structure provides critical information for seismic hazard assessment, ground motion simulation, and site amplification studies. However, accurate estimation typically requires costly core sampling from boreholes and/or seismic exploration. As an alternative, we estimated near-surface to mid-crustal shear-wave velocities in the southern Korean Peninsula using P-wave polarization analysis, which utilizes only the incident angles of direct P waves from earthquakes. We extended the polarization analysis by employing several different frequency bands to estimate shear-wave velocities from the surface down to the mid-crust. P waveforms were bandpass-filtered with three different frequency bands (0.05–0.3 Hz, 0.5–2 Hz, and 3–10 Hz) to determine shear-wave velocities at different depths. The P-wave polarization results show a strong correlation between geological features and velocity distributions across the frequency bands. The P-wave polarization results for the frequency bands of 0.05–0.3 Hz, 0.5–2 Hz, and 3–10 Hz indicate velocity distributions of 3.50 ± 0.35 km/s, 3.00 ± 0.69 km/s, and 2.26 ± 0.69 km/s, respectively. Comparison with velocity structures estimated from Rayleigh-wave ellipticity and site surveys reveals that these values from frequency bands of 0.05–0.3 Hz, 0.5–2 Hz, and 3–10 Hz correspond to average velocities from the surface to depths of 3–16 km, 0.1–3 km, and 5–100 m, respectively. The polarization analysis demonstrates superior performance in estimating near-surface shear-wave velocity compared to the Rayleigh-wave ellipticity method.

Machine learning optimized design of THz piezoelectric perovskite-based biosensor for the detection of formalin in aqueous environments

This investigation presents the development and characterization of an advanced piezoelectric perovskite-based biosensing platform optimized for formalin detection in aqueous media through the implementation of Locally Weighted Linear Regression (LWLR) machine learning algorithms. The sensor architecture operates within the terahertz spectral region and incorporates an advanced nanomaterial composite system comprising black phosphorus, gold nanostructures, graphene, and barium titanate to maximize detection sensitivity and operational performance metrics. The engineered platform integrates a circular graphene metasurfaces configuration with a gold-based H-resonator assembly and concentrically arranged circular ring resonators. Computational simulations demonstrate vigorous sensing capabilities across three discrete frequency bands, achieving remarkable sensitivity parameters of 444 GHzRIU⁻¹, accompanied by a quality factor of 5.970 and detection accuracy of 7.576. The integration of LWLR-based optimization protocols substantially enhances prediction accuracy while reducing computational time by ≥ 85% as well as cutting down the required resources. The proposed sensor architecture presents significant potential for environmental monitoring and clinical applications, offering a highly sensitive and efficient methodology for quantitative formalin detection in aqueous environments.

Pattern-Reconfigurable, Vertically Polarized, Wideband Electrically Small Huygens Source Antenna

A pattern-reconfigurable, vertically polarized (VP), electrically small (ES), Huygens source antenna (HSA) is demonstrated. A custom-designed reconfigurable inverted-F structure is embedded in a hollowed-out cylindrical dielectric resonator (DR). It radiates VP electric dipole fields that excite the DR’s HEM11δ mode, which in turn acts as an orthogonal magnetic dipole radiator. The HSA’s unidirectional properties are thus formed. It becomes low-profile and electrically small through a significant lowering of its operational frequency band by loading the DR’s top surface with a metallic disk. The entire 360° azimuth range is covered by each of the HSA’s four 90° reconfigurable states, emitting a unidirectional wide beam. A prototype was fabricated and tested. The measured results, which are in good agreement with their simulated values, demonstrate that the developed wideband Huygens source antenna, with its 0.085 λL low profile and its 0.20 λL × 0.20 λL compact transverse dimensions, hence, electrically small size with ka = 0.89, exhibits a wide 14.1% fractional impedance bandwidth and a 6.1 dBi peak realized gain in all four of its pattern-reconfigurable states.

A wide band circularly polarized antenna using meta surface for Wi-Fi applications

Abstract A circularly polarized, single-feed, low-profile patch antenna with enhanced gain and bandwidth for WI –FI applications is presented in this paper. The proposed antenna has a slot-loaded square patch (SLSP) is placed between reactive impedance structure (RIS)-based metasurface (MS) and the ground plane. A 6x6 circular RIS metasurface is used to improve the gain, impedance and axial ratio bandwidth. The antenna prototype’s overall dimensions are 60mm x 60mm x 3.6mm.The measured impedance bandwidth (IBW) of the proposed antenna is 26% (4.98GHz–6.48GHz) which covers the 5GHz (5.150-5.850 GHz) and 6GHz (5.950 – 7.125 GHz) frequency bands of wireless spectrum. The antenna exhibits right hand circular polarization with maximum measured gain of 6.5 dBic and the 3-dB ARBW is 5.23% (5.47GHz–5.77GHz).

Flexible circularly polarized CPW antenna for X-band: design, simulation, and experimental validation

Abstract This paper presents the design and analysis of a circularly polarized coplanar waveguide (CPW) antenna specifically tailored for X-band applications. The antenna, with compact dimensions of 27 mm x 28 mm, is fabricated on a flexible 0.1 mm thick polyimide substrate. It operates effectively within the frequency range of 10.5 GHz to 12 GHz. To enhance performance metrics such as gain and radiation characteristics, two complementary split-ring resonators (CSRRs) are integrated into the radiating patch. Comprehensive simulations were conducted, followed by the fabrication and experimental testing of the antenna. The results indicate a high degree of agreement between the simulated and measured performance parameters. The antenna achieves a gain of up to 6 dBi, with an axial ratio maintained below 3 dB across the operating frequency band, signifying excellent circular polarization characteristics. These findings underscore the potential of the proposed CPW antenna design as a highly efficient solution for X-band applications, offering notable improvements in gain and axial ratio performance. This work contributes to the development of advanced antenna technologies suitable for modern communication systems operating in the X-band spectrum.

Optimal Selection of Frequency Band and Wavelet Scattering for Feature Extraction in Multi-class EEG Classification

Optimal Selection of Frequency Band and Wavelet Scattering for Feature Extraction in Multi-class EEG Classification

Rectifier Fault Diagnosis Based on Euclidean Norm Fusion Multi-Frequency Bands and Multi-Scale Permutation Entropy

With the emphasis on energy conversion and energy-saving technologies, the single-phase pulse width modulation (PWM) rectifier method is widely used in urban rail transit because of its advantages of bidirectional electric energy conversion and higher power factor. However, due to the complex control and harsh environment, it can easily fail. Faults can cause current and voltage distortion, harmonic increases and other problems, which can threaten the safety of the power system and the train. In order to ensure the stable operation of the rectifier, incidences of faults should be reduced. A fault diagnosis technique based on Euclidean norm fusion multi-frequency bands and multi-scale permutation entropy is proposed. Firstly, by the optimal wavelet function, information on the optimal multi-frequency bands of the fault signal is selected after wavelet packet decomposition. Secondly, the multi-scale permutation entropy of each frequency band is calculated, and multiple fault feature vectors are obtained for each frequency band. To reduce the classifier’s computational cost, the Euclidean norm is used to fuse the multi-scale permutation entropy into an entropy value, so that each frequency band uses an entropy value to characterize the fault information features. Finally, the optimal multi-frequency bands and multi-scale permutation entropy after fusion are used as the fault feature vector. In the simulation system, it is shown that the method’s average accuracy is 78.46%, 97.07%, and 99.45% when the SNR is 5 dB, 10 dB, and 15 dB, respectively. And the fusion of multi-scale permutation entropy can improve the accuracy, recall rate, precision, and F1 score and reduce the False Alarm Rate (FAR) and the Missing Alarm Rate (MAR). The results show that the fault diagnosis method has high diagnosis accuracy, is a simple feature fusion method, and has good robustness to working conditions and noise.

Differences in EEG complexity of cognitive activities among subtypes of schizophrenia

IntroductionThe neural mechanisms that underpin cognitive impairments in patients with schizophrenia remain unclear. Previous studies have typically treated patients as a homogeneous group, despite the existence of distinct symptom presentations between deficit and non-deficit subtypes. This approach has been found to be inadequate, necessitating separate investigation.MethodsThis study was conducted at Daizhuang Hospital in Jining City, China, from January 2022 to October 2023. The study sample comprised 30 healthy controls, 19 patients with deficit schizophrenia, and 19 patients with non-deficit schizophrenia, all aged between 18 and 45 years. Cognitive abilities were evaluated using a change detection task. The NeuroScan EEG/ERP System, comprising 64 channels and utilising standard 10-20 electrode placements, was employed to record EEG signals. The multiscale entropy and sample entropy of the EEG signals were calculated.ResultsThe healthy controls demonstrated superior task performance compared to both the non-deficit (p < 0.001) and deficit groups(p < 0.001). Significant differences in multiscale entropy between the three groups were observed at multiple electrode sites. In the task state, there are significant differences in the sample entropy of the β frequency band among the three groups of subjects. Under simple conditions of difficulty, the performance of the healthy controls exhibited a positive correlation with alpha band sample entropy(r = 0.372) and a negative correlation with beta band sample entropy (r = -0.411). Deficit patients demonstrated positive correlations with alpha band sample entropy (r = 0.370), whereas non-deficit patients exhibited negative correlations with both alpha and beta band sample entropy (r = -0.451, r = -0.362). Under difficult conditions of difficulty, the performance of healthy controls demonstrated a positive correlation with beta band sample entropy (r = 0.486). Deficit patients exhibited a positive correlation with alpha band sample entropy (r = 0.351), while non-deficit patients demonstrated a negative correlation with beta band sample entropy (r = -0.331).ConclusionThe results of this study indicate that cognitive impairment in specific subtypes of schizophrenia may have distinct physiological underpinnings, underscoring the need for further investigation.

Volitional Regulation and Transferable Patterns of Midbrain Oscillations.

Dopaminergic brain areas are crucial for cognition and their dysregulation is linked to neuropsychiatric disorders typically treated with pharmacological interventions. These treatments often have side effects and variable effectiveness, underscoring the need for alternatives. We introduce the first demonstration of neurofeedback using local field potentials (LFP) from the ventral tegmental area (VTA). This approach leverages the real-time temporal resolution of LFP and ability to target deep brain. In our study, two male rhesus macaque monkeys (Macaca mulatta) learned to regulate VTA beta power using a customized normalized metric to stably quantify VTA LFP signal modulation. The subjects demonstrated flexible and specific control with different strategies for specific frequency bands, revealing new insights into the plasticity of VTA neurons contributing to oscillatory activity that is functionally relevant to many aspects of cognition. Excitingly, the subjects showed transferable patterns, a key criterion for clinical applications beyond training settings. This work provides a foundation for neurofeedback-based treatments, which may be a promising alternative to conventional approaches and open new avenues for understanding and managing neuropsychiatric disorders.Significance statement This study demonstrates, for the first time, that neurofeedback using local field potentials (LFP) from the ventral tegmental area (VTA) is feasible in non-human primates. By leveraging the temporal resolution and ability to target deep brain regions, this approach provides a novel way to modulate brain activity linked to dopamine-related functions. The findings reveal that subjects can flexibly control VTA LFP signals and transfer learned strategies to new settings, offering potential for developing neurofeedback-based treatments. This research opens new avenues for managing neuropsychiatric disorders, presenting an alternative to traditional pharmacological interventions that often have side effects and limited effectiveness. The study highlights the plasticity of VTA neurons and their relevance to cognition and mood regulation.

Integrated Electrically Small Monopole and Slotted Array Antenna Design for Next-Generation Microwave and mm-wave Wireless Systems

Abstract This paper presents a novel co-design approach for wireless applications, integrating mm-wave and microwave technologies. The design features two antennas where a slotted waveguide array is fabricated using 3D-printing technology with an AlMg10Si alloy resonating at mm-wave frequency band, while a printed monopole antenna resonating at microwave frequency band shares a common ground plane with the slotted waveguide array. The electrically small printed monopole is realized on an FR4 substrate, is suitable for WLAN bands, and achieves ease of integration with the antenna at the Ka-band owing to the low profile. The co-design approach is particularly advantageous for wireless applications as it enables the seamless integration of different frequency bands, enhancing system versatility and performance. The antenna achieves dual frequency band operation covering the Impedance bandwidth of 15.27% (5.08-5.92) and 14.56% (26.48-30.64) at microwave and Ka-band frequencies, respectively. The innovative use of materials and advanced fabrication techniques also underscores the design's potential for diverse high-performance wireless applications.

Glioma-induced neural functional remodeling in the hand motor cortex: precise mapping with ECoG grids during awake craniotomy—experimental research

Background: The dilemma of achieving ‘onco-functional balance’ in gliomas affecting the motor cortex highlights the importance of functionally-guided resection strategies. While accurate mapping of eloquent areas often requires frequent electrical stimulation, this practice can lead to side effects like seizures and postoperative deficits. To enhance safety in functional mapping, we studied how gliomas impact hand movement areas and assessed the effectiveness of cortical electrical activity for functional mapping in this setting. Materials and Methods: We recruited patients with gliomas affecting the motor cortex and individuals with an unaffected motor cortex for awake craniotomy. During the procedures, electrocorticography (ECoG) grids were employed to record signals under three conditions: resting state, finger movements, and wrist movements. We then quantified the distances from the positively stimulated sites to the specific anatomical landmarks. Additionally, we analyzed the relationship between the ECoG power features and the stimulation responses. Results: The cortical layout for finger activity in the MCG group was more dispersed and overlapped, typically clustering near the central sulcus and Sylvian fissure. The predictive performance of ECoG mapping exhibited significant variability across different frequency bands and clinical scenarios. Specifically, the area under the curve (AUC) for the Non-MCG group during the resting state reached its peak, with a value of 0.802 for Gamma3 (95% CI = 0.729-0.875) and 0.865 for broadband (95% CI = 0.804-0.926). In contrast, the MCG group achieved the highest AUC during wrist movements, with Gamma3 at 0.785 (95% CI = 0.719-0.849) and broadband at 0.824 (95% CI = 0.753-0.890). Conclusion: Gliomas in the motor cortex disrupt the distribution of hand activity, complicating intraoperative functional mapping. As a novel and reliable approach, ECoG technique can complement and guide direct cortical stimulation for precise mapping, potentially reducing its frequency, minimizing the risk of functional deficits, and achieving a balance between maximal tumor resection and neurological preservation.

Design of wide bandwidth metamaterial for biosensor and wireless application

Abstract We present a new design and study of metamaterial (MTM) structure for wide bandwidth for biosensor and wireless applications. The geometrical parameters were analyzed and optimized for a triple-band operation in the frequency range of 0.1–16 GHz. The propagation characteristics were obtained using Finite element method. The proposed MTM provides negative permittivity at 1.4 GHz and negative permeability in the 9–16 GHz region. The proposed design exhibits left-handed characteristics in L, C, and Ku microwave region’s frequency band. The electric field (E), magnetic field (H), and surface current distribution of the proposed MTM unit cell have been studied at three different resonance frequencies. The proposed MTM design has a wide bandwidth of 2.2 GHz in C-band and a high effective medium ratio (EMR) of 13.37. The performance of the sensor is evaluated for different biomedical samples in the refractive index range of 1.00 to 1.39. The results indicate that the proposed biosensor has a high sensitivity in triple band of microwave region. The present research work can be highly suitable for Wi-Fi and satellite applications due to its overall performance, including wide bandwidth in the C-band, high EMR, and triple band operation.

Design and Analysis of Dual-Band Metasurface Filter for Pulse Waves Based on Capacitive Nonlinear Circuits

In this paper, a novel dual-band metasurface filter (MSF) designed for accurately differentiating pulse waves (PWs) and continuous waves (CWs) is proposed, which is based on a complementary cross resonator (CSR) structure adhered on a dielectric substrate integrated with a capacitive nonlinear circuit. The unit cell of the designed dual-band MSF comprises two identical CSR structures: one of the capacitive nonlinear circuits is configured in parallel with a capacitor (C1) within one CSR structure. These structures loaded with nonlinear circuits are fabricated on a dielectric substrate. The simulation outcomes reveal that, for normally incident CWs with an input power of 10 dBm, the transmittance of the designed dual-band MSF reaches as high as 97.1% at 2.0 GHz and 93.9% at 3.45 GHz. In contrast, when it comes to 50 ns short PWs, the transmittance remains consistently below 6% throughout the entire frequency range from 1 GHz to 5 GHz. In addition, the transmittance of the dual-band MSF for normally incident PWs increases significantly as the pulse width widens at the aforementioned two discrete frequencies. The ensuing simulation data corroborates that within the input power range of −15 to 15 dBm, the transmittance difference between CWs and PWs of the dual-band MSF first rises and then falls as the input power increases. Specifically, when the input power is specified as 10 dBm and the angle of oblique incidence ranges from 0° to 60°, in the context of TE and TM modes, the transmittance of CWs exceeds 80% around both 2.0 GHz and 3.45 GHz, while that of PWs remains below 15%. Finally, the effects of resistance and capacitance on the transmittance of the dual-band MSF for the incident PWs and CWs are also studied. The dual-band MSF proposed herein showcases its potential applications in wireless communication as well as in the realm of anti-electromagnetic interference. The electromagnetic (EM) waveform modulation in the frequency band of 1–5 GHz has great development prospects in low-frequency working fields such as radar antennas and EM protection.

Machine Learning-Based In Situ Identification of the High-Dimensional Parameter Space for an Equivalent Circuit Model

Abstract Accurate online identification of equivalent circuit model (ECM) parameters of lithium-ion battery electrochemical impedance spectroscopy (EIS) remains a challenge, particularly with high-dimensional parameter spaces. Here, 11-dimensional adapted Randles ECM (AR-ECM) is reduced to two low-dimensional models, and the EIS frequency ranges for each AR-ECM parameter were determined by distinguishing the frequency bands representing different electrochemical processes. A multi-step parameter in-situ identification methodology was developed to minimize onboard training costs by selecting an optimal training set for machine learning based on the Euclidean distance between the collected and generated EIS data. A Gaussian process regression model was constructed by correlating the AR-ECM parameters and EIS to estimate the AR-ECM parameters. Model performance was validated using 12 cells at different temperatures. Experimental results show that a simulated database covering the main EIS and AR-ECM characteristics can be established, whose scale is on the order of 1e^6, much smaller than the order of 1e^11 resulting from each AR-ECM parameter being varied among 10 values. The estimation errors of the key AR-ECM parameters are approximately 5% at different temperatures. The maximum estimation error of all parameters is as low as 9.03%, 13.96% lower than that based on the complex nonlinear least squares method.

Aberrant Functional Connectivity and Brain Network Organization in High-Schizotypy Individuals: An Electroencephalography Study.

Oscillatory synchrony plays a crucial role in establishing functional connectivity across distinct brain regions. Within the realm of schizophrenia, suggested to be a neuropsychiatric disconnection syndrome, discernible aberrations arise in the organization of brain networks. We aim to investigate whether the resting-state functional network is already altered in healthy individuals with high schizotypy traits, highlighting the pivotal influence of brain rhythms in driving brain network alterations. Two-minute resting-state electroencephalography recordings were conducted on healthy participants with low and high schizotypy scores. Subsequently, spectral Granger causality was used to compute functional connectivity in theta, alpha, beta, and gamma frequency bands, and graph theory metrics were employed to assess global and local brain network features. Results highlighted that high-schizotypy individuals exhibit a lower local efficiency in theta and alpha frequencies and a decreased global efficiency across theta, alpha, and beta frequencies. Moreover, high schizotypy is characterized by a lower nodes' centrality and a frequency-specific decrease of functional connectivity, with a reduced top-down connectivity mostly in slower frequencies and a diminished bottom-up connectivity in faster rhythms. These results show that healthy individuals with a higher risk of developing psychosis exhibit a less efficient functional brain organization, coupled with a systematic decrease in functional connectivity impacting both bottom-up and top-down processing. These frequency-specific network alterations provide robust support for the dimensional model of schizophrenia, highlighting distinctive neurophysiological signatures in high-schizotypy individuals.

Specific endophenotypes in EEG microstates for methamphetamine use disorder

BackgroundElectroencephalogram (EEG) microstates, which reflect large-scale resting-state networks of the brain, have been proposed as potential endophenotypes for methamphetamine use disorder (MUD). However, current endophenotypes lack refinement at the frequency band level, limiting their precision in identifying key frequency bands associated with MUD.MethodsIn this study, we investigated EEG microstate dynamics across various frequency bands and different tasks, utilizing machine learning to classify MUD and healthy controls.ResultsDuring the resting state, the highest classification accuracy for detecting MUD was 85.5%, achieved using microstate parameters in the alpha band. Among these, the coverage of microstate class A contributed the most, suggesting it as the most promising endophenotype for specifying MUD.DiscussionWe accurately categorize the endophenotype of MUD into different sub-frequency bands, thereby providing reliable biomarkers.

Schizophrenia recognition based on three-dimensional adaptive graph convolutional neural network

Previous deep learning-based brain network research has made significant progress in understanding the pathophysiology of schizophrenia. However, it ignores the three-dimensional spatial characteristics of EEG signals and cannot dynamically learn the interactions between nodes. To address this issue, a schizophrenia classification model based on a three-dimensional adaptive graph convolutional neural network (3D-AGCN) is proposed. Each subject’s EEG data is divided into various segment lengths and frequency bands for the experiment. The attention mechanism is then used to integrate the node features in the spatial, feature, and frequency band dimensions. The resulting adaptive brain functional network features are then constructed and fed into the GAT + GCN model. This adaptive approach eliminates the human-specified criteria for feature selection and brain network construction. The trial results demonstrated that, when using a 6-second segment length and time-domain and frequency-domain features, patients with first-episode schizophrenia achieved the highest classification accuracy of 87.64% This method outperforms other feature selection and brain network modeling approaches, providing new insights and directions for the early diagnosis and recognition of schizophrenia.