Power Spectral Density Method Research Articles

A high-precision two-parameter road roughness grading and identification methodology across multiple frequency intervals

Based on a dual track road shape meter, various road roughness was measured. It was discovered that the road grading method in ISO 8608 assumes a constant waviness value of W = 2 with a one-straight line approximation, but it could not accurately fit and grade the measured road roughness. A high-precision piecewise fitting function for the low-frequency and high-frequency parts of the road roughness power spectral density (PSD) is established. A high-precision two-parameter grade method in multiple frequency bands for road roughness is proposed ( G q( n0), the geometric average of the road roughness index at n0, W, the waviness, is the slope of the road roughness PSD fitting expression in the double logarithmic coordinate system). According to the vehicle driving performance obtained by simulation, the grading method is verified. For Paved and Undulating roads, compared to the road roughness classification method in ISO 8608, the performance simulated based on the grade results of the two parameters grading method are closer to the performance simulated based on the measured road roughness. The grading method proposed in this paper can more accurately fit and classify the measured road roughness. The influence of these parameters at each frequency interval on vehicle driving performance is studied. A two-parameter identification method for road roughness PSD at variable intervals over a large frequency range is proposed. In the double logarithmic coordinate system, this method divides the selected spatial frequency into three sub-intervals along the abscissa, in each sub-interval, the parameters G q( n0) and W are identified separately. This method can provide effective control inputs for the vehicle’s active or semi-active suspension system.

Accurate drift-invariant single-molecule force calibration using the Hadamard variance

Single-molecule force spectroscopy (SMFS) techniques play a pivotal role in unraveling the mechanics and conformational transitions of biological macromolecules under external forces. Among these techniques, multiplexed magnetic tweezers (MT) are particularly well suited to probe very small forces, ≤1 pN, critical for studying noncovalent interactions and regulatory conformational changes at the single-molecule level. However, to apply and measure such small forces, a reliable and accurate force-calibration procedure is crucial. Here, we introduce a new approach to calibrate MT based on thermal motion using the Hadamard variance (HV). To test our method, we perform bead-tether Brownian dynamics simulations that mimic our experimental system and compare the performance of the HV method against two established techniques: power spectral density (PSD) and Allan variance (AV) analyses. Our analysis includes an assessment of each method’s ability to mitigate common sources of additive noise, such as white and pink noise, as well as drift, which often complicate experimental data analysis. We find that the HV method exhibits overall similar or higher precision and accuracy, yielding lower force estimation errors across a wide range of signal/noise ratios (SNRs) and drift speeds compared with the PSD and AV methods. Notably, the HV method remains robust against drift, maintaining consistent uncertainty levels across the entire studied SNR and drift speed spectrum. We also explore the HV method using experimental MT data, where we find overall smaller force estimation errors compared with PSD and AV approaches. Overall, the HV method offers a robust method for achieving sub-pN resolution and precision in multiplexed MT measurements. Its potential extends to other SMFS techniques, presenting exciting opportunities for advancing our understanding of mechanosensitivity and force generation in biological systems. To make our methods widely accessible to the research community, we provide a well-documented Python implementation of the HV method as an extension to the Tweezepy package.

An enhanced stochastic error modeling using multi-Gauss–Markov processes for GNSS/INS integration system

Angular random walk (ARW), rate random walk (RRW), and bias instability (BI) are the main noise types in inertial measurement units (IMUs) and thus determine the navigation performance of IMUs. BI is the flicker noise, which determines the noise level of an inertial sensor. The traditional error modeling approach involves modeling the ARW and BI processes as RRW or Gauss–Markov (GM) processes, and this approach is applied as a suboptimal filter in the global navigation satellite system (GNSS)/inertial navigation system (INS) extended Kalman filter (EKF). In this paper, the random error identification processes for white noise and colored noise for inertial sensors are separated using the Allan variance and power spectral density methods and the equivalence of the stochastic process differential equations of bias instability and a combination of multiple first-order GM processes are derived. A colored noise compensation method is proposed based on the enhanced EKF model. Experimental results demonstrate that, compared to traditional error models, our proposed model reduces positional drift error in dynamic testing from 195 to 49 m, enhancing positional accuracy by 40.2%. These findings confirm the potential and superiority of our method in complex navigation environments.

Non-Intrusive System for Honeybee Recognition Based on Audio Signals and Maximum Likelihood Classification by Autoencoder.

Artificial intelligence and Internet of Things are playing an increasingly important role in monitoring beehives. In this paper, we propose a method for automatic recognition of honeybee type by analyzing the sound generated by worker bees and drone bees during their flight close to an entrance to a beehive. We conducted a wide comparative study to determine the most effective preprocessing of audio signals for the detection problem. We compared the results for several different methods for signal representation in the frequency domain, including mel-frequency cepstral coefficients (MFCCs), gammatone cepstral coefficients (GTCCs), the multiple signal classification method (MUSIC) and parametric estimation of power spectral density (PSD) by the Burg algorithm. The coefficients serve as inputs for an autoencoder neural network to discriminate drone bees from worker bees. The classification is based on the reconstruction error of the signal representations produced by the autoencoder. We propose a novel approach to class separation by the autoencoder neural network with various thresholds between decision areas, including the maximum likelihood threshold for the reconstruction error. By classifying real-life signals, we demonstrated that it is possible to differentiate drone bees and worker bees based solely on audio signals. The attained level of detection accuracy enables the creation of an efficient automatic system for beekeepers.

An Identification Method for Road Hypnosis Based on Human EEG Data.

The driver in road hypnosis has not only some external characteristics, but also some internal characteristics. External features have obvious manifestations and can be directly observed. Internal features do not have obvious manifestations and cannot be directly observed. They need to be measured with specific instruments. Electroencephalography (EEG), as an internal feature of drivers, is the golden parameter for drivers' life identification. EEG is of great significance for the identification of road hypnosis. An identification method for road hypnosis based on human EEG data is proposed in this paper. EEG data on drivers in road hypnosis can be collected through vehicle driving experiments and virtual driving experiments. The collected data are preprocessed with the PSD (power spectral density) method, and EEG characteristics are extracted. The neural networks EEGNet, RNN, and LSTM are used to train the road hypnosis identification model. It is shown from the results that the model based on EEGNet has the best performance in terms of identification for road hypnosis, with an accuracy of 93.01%. The effectiveness and accuracy of the identification for road hypnosis are improved in this study. The essential characteristics for road hypnosis are also revealed. This is of great significance for improving the safety level of intelligent vehicles and reducing the number of traffic accidents caused by road hypnosis.

Direct numerical simulation of shock wave/boundary layer interaction controlled by steady microjet in a compression ramp

Shock wave/boundary layer interaction in a 24° turning angle of the compression ramp at Mach number 2.9 controlled by steady microjet is investigated using direct numerical simulation. Three different jet spacings which are termed as sparse, moderate and dense are considered, and the induced vortex system and shock structures are compared. A moderate jet spacing configuration is found to generate counter-rotating vortex pairs that transport high-momentum fluid towards the vicinity of wall and strengthen the boundary layer to resist separation, reducing the separation region. The dense jet spacing configuration creates a larger momentum deficit region, reducing the friction downstream of the corner. Analysis of pressure and pressure gradient reveals that dense jet spacing configuration reduces the intensity of separation shock. The impact of varying jet spacings on the turbulent kinetic energy transport mechanism is also investigated by decomposing the budget terms in the transport equation. Furthermore, the spectral characteristics of the separation region are studied using power spectral density and dynamic mode decomposition methods, revealing that moderate jet spacing configuration suppresses low-frequency fluctuations in the separation region.

The Development of the Stress-Free Polishing System Based on the Positioning Error Analysis for the Deterministic Polishing of Jet Electrochemical Machining.

Deterministic polishing based on jet electrochemical machining (Jet-ECM) is a stress-free machining method for low-rigidity and ultra-precision workpieces. The nozzle is equivalent to a special tool in deterministic polishing, and the workpiece material is removed using the mechanism of electrochemical dissolution at the position where the nozzle passes. By precisely regulating the nozzle's movement speed and dwell time, the quantity of material removed from the workpiece at a designated position can be finely adjusted. With this mechanism, the improvement of the workpiece shape accuracy can be achieved by planning the nozzle trajectory and nozzle movement speed. However, due to the positioning errors of the polishing device, the actual position of the nozzle may deviate from the theoretical position, resulting in errors in material removal amount, which affects the accuracy and stability of the polishing process. This study established a mathematical model to analyze the influence of nozzle positioning errors in deterministic polishing based on Jet-ECM. This model has been used to design a specific deterministic polishing device based on Jet-ECM. With the proposed deterministic polishing device, the surface shape of the workpiece is converged. The surface peak-to-valley (PV) value of the φ 50 mm workpiece (valid dimensions = 90% of the central region) indicated that the shape error of the surface was reduced from 2.67 μm to 1.24 μm in 34 min. The power spectral density (PSD) method was used to evaluate the height distribution and height characteristics of the workpiece surface. The results show that the low frequency spatial error is reduced significantly after processing. This study improves the accuracy of the stress-free deterministic polishing methods and further expands the use of deterministic polishing in industry.

Structural EEG signal analysis for sleep apnea classification.

Diagnosing the sleep apnea can be critical in preventing the person having sleep disorder from unhealthy results. The aim of this study is to obtain a sleep apnea scoring approach by comparing parametric and non-parametric power spectral density (PSD) estimation methods from EEGsignals recorded from different brain regions (C4-M1 and O2-M1) for transient signal analysis of sleep apnea patients. Power Spectral Density (PSD) methods (Burg, Yule-Walker, periodogram, Welch and multi-taper) are examined for the detection of apnea transition states including pre-apnea, intra-apnea and post-apnea together with statistical methods. In the experimental studies, EEG recordings available in the database were analyzed with PSD methods. Results showed that there are statistically significant differences between parametric and non-parametric methods applied for PSD analysis of apnea transition states in delta, theta, alpha and beta bands. Moreover, it was also revealed that PSD of EEG signals obtained from C4-M1 and O2-M1 channels were also found statistically different as proved by classification using the K-nearest neighbour (KNN) method. It was concluded that not only applying different PSD methods, but also EEG signals from different brain regions provided different statistical results in terms of apnea transition states as obtained from KNN classification.

Response Mechanism of the Residual Strength to the Mesostructure of the Shear Surface in the Gravelly Slip Zone of Ancient Landslides

AbstractAncient landslides tend to reactivate along pre‐existing slip zones that have reached a residual state. On the eastern margin of the Tibetan Plateau, previous research has indicated that the slip zone of ancient landslides is primarily composed of clayey soil with gravel, known as gravelly slip zone soil. However, the relationship between the macromechanical behavior of gravelly slip zones and the mesostructure of the shear surfaces affected by gravel is still unclear. Herein, ring shear tests and reversal direct shear tests were performed on gravelly slip zone soil, and the 3D morphology and shear surface roughness were quantitatively characterized by using 3D laser scanning technology and the power spectral density method. The results showed a significant correlation between the friction coefficient of the shear surface and its roughness. Gravel played a crucial role in influencing the macromechanical behavior of slip zones by altering the mesomorphology of the shear surfaces. By analyzing the mechanical properties of the contact unit on the shear surface, the residual strength of the gravelly slip zone was found to be jointly controlled by the basic strength of the fine‐grained soil and the undulations caused by the gravel. Finally, a residual strength model was developed for the gravelly slip zone considering both the strength of the fine‐grained soil and the shear surface roughness caused by the gravel.

Analysis of Acoustic Signals of Footsteps from the Piezoelectric Sensor

Some materials can change their electrical polarization under the influence of a mechanical stress due to the piezoelectric effect. This stress-induced change in polarization produces a potential difference across the material. For this reason, the piezoelectric material generates an electrical signal when it is subjected to a pressure from acoustic energy. In this study, analysis of acoustic signals of footsteps from the piezoe-lectric sensor, especially for human footsteps, have been studied by considering the analytical relationship between the electrical signal generated from the sensor and the acoustic signal that provides the effect. We analyzed the acoustic signal data by assuming that the electrical output voltage of the piezoelectric sensor completely coincides with the frequency of the acoustic signals. The original signal was pre-processed using filtering systems and analyzed by the fast Fourier transform and power spectral density methods to extract descriptive spectral features of the signal. This preliminary study proposed a method as a sensor based piezoelectric security system to detect the acoustic signals that can indicate possible dangers to the safety of people or property. The source of the acoustic signal can be determined by matching it with the existing database using machine learning algorithms like face recognition systems for future goals.

Damage identification for pile foundation in high-piled wharf using composite energy factors driven by dynamic response under wave impact excitation

Exploring dynamic response-based damage identification under wave excitation is important for establishing a health monitoring system. In our previous study, we analyzed the characteristics of the rigid-body dynamic response and successfully employed them to identify the damage location of a pile foundation. Nevertheless, some recent researches show that the local flexural response resulting from wave impact has stronger energy performance. Therefore, this paper constructed a new damage identification method by making full use of energy and phase slope of the local flexural response. Firstly, a novel frequency domain clustering algorithm based on the Sub-peaks Suppression of Power Spectral Density Method (SSPSDM) was proposed to address the challenge of mode mixing in signal decomposition. Subsequently, the new composite energy factors were developed by combining both energy and phase of the local flexural response to enhance the sensitivity and robustness of damage identification. Finally, the method was validated using the acceleration signals from a laboratorial high-piled wharf model. The results show that the method can effectively extract the damage feature sub-signal and the composite energy factor based on phase slope enables better identification of the damage location and degree, enhancing the accuracy of the damage assessment process.

Enhancing EEG-based emotion recognition using PSD-Grouped Deep Echo State Network

Emotions are a crucial aspect of daily life and play a vital role in shaping human inter-actions. The purpose of this paper is to introduce a novel approach to recognize human emotions through the use of electroencephalogram (EEG) signals. To recognize these signals for emotion prediction, we employ a paradigm of Reservoir Computing (RC), called Echo State Network (ESN). In our analysis, we focus on two specific classes of emotion recognition: H/L Arousal and H/L Valence. We suggest using the Deep ESN model in conjunction with the Welch Power Spectral Density (Wlech PSD) method for emotion classification and feature extraction. Furthermore, we feed the selected features to a grouped ESN for recognizing emotions. Our approach is validated on the well-known DEAP benchmark, which includes the EEG data from 32 participants. The proposed model achieved 89.32% accuracy for H/L Arousal and 91.21% accuracy for H/L Valence on the DEAP dataset. The obtained results demonstrate the effectiveness of our approach, which yields good performance compared to existing models of emotion analysis based on EEG.

Dynamic Response Analysis of JPCP with Different Roughness Levels under Moving Axle Load Using a Numerical Methodology

In-service Portland cement concrete (PCC) pavements are subject to repeated dynamic loads from moving vehicles; thus, the actual stress generated in a PCC pavement may significantly differ from the static stress, which is normally used in the design and evaluation of pavement performance. Calculating the stress in PCC pavements under moving vehicle loads is of importance to assess their actual service condition, particularly for pavements with different surface roughness levels as the deteriorated roughness might cause large stress in PCC pavement subject to dynamic loads. In this paper, a method is proposed to compute the dynamic response in terms of loads and stresses generated in jointed plain concrete pavements (JPCPs) under a moving axle load, considering the effects of the pavement surface roughness, the vehicle parameters (including vehicle speeds and axle weights), and the pavement structure parameters (including thickness and elastic modulus of different layers and the existence of dowel bars). The dynamic axle load is firstly generated based on the quarter-car model, running through three successive slabs of which the surface roughness is determined by the power spectral density method, and the critical locations in slabs where the largest tensile stresses occur are identified. The combined effects of various pavement surface roughness levels, vehicle speeds, axle weights, and pavement structure parameters are evaluated in terms of the stress and the dynamic factor defined as the ratio of the tensile stress under dynamic load to the tensile stress under static load. For the roughness level D, the tensile stress can reach a maximum value of 3.13 MPa, and the dynamic factor can reach a maximum value of 2.46, which is much larger than the dynamic factor of 1.15 or 1.2 currently used in design guidebooks. Increasing the thicknesses of pavement slab or the subbase layer is an effective way to reduce the tensile stress in JPCP, while increasing the thickness of base layer is not effective. The results of this study can benefit future pavement design and pavement performance evaluation by providing the actual stress and the useful dynamic factor values for various conditions of field pavements. Moreover, preventive maintenance, particularly the improvement of pavement surface roughness, can be planned by referring to the results of this study, to avoid large tensile stress generated in JPCPs.

Correlation of balance posturographic parameters during quiet standing with the berg balance scale in patients with parkinson’s disease

BackgroundParkinson’s disease (PD) is often clinically associated with posture instability and more easily falling. The Berg balance scale is a clinical indicator commonly used to subjectively evaluate a patient’s balance ability. Meanwhile, computerized force platforms have been used in research on postural control. The various parameters obtained from posturography are interpreted to assess balance ability. The present study aims to explore the correlations between posturographic variables and the BBS, and furthermore to efficiently evaluate postural instability and fall risk of early and moderate PD patients.MethodsA total of 46 PD patients were involved in the experiment. Patients were asked to perform BBS tests and force platform tests under eye open (EO) and eye closed (EC) conditions. The recorded COP signal was analyzed with the time domain statistical method, the frequency domain method of Power Spectral Density (PSD), and structural methods of Stabilogram Diffusion Analysis (SDA), Sway Density Plot (SDP) to retrieve different posturographic variables. The correlation between posturographic variables under EO and EC conditions with BBS was compared statistically. The significantly correlated posturographic parameters were then applied to analyze posturographic differences between different groups: faller vs. non-faller (patients with/without a history of falls in the past 12 months).ResultsAmong the different posturographic parameters, the prediction ellipse area, the slope of the regression line at a high-frequency band of PSD in the medial-lateral (ML) direction, the crossover point of the regression lines of SDA in the anterior-posterior (AP) direction, and the distance between successive peaks of SDP had significant correlations with BBS. These selected BBS-related parameters also showed significant differences between faller and non-faller. The selected posturographic parameters can be used as effective indicators to evaluate the balance ability of Parkinson’s disease patients.

On-Board Random Vibration-Based Robust Detection of Railway Hollow Worn Wheels under Varying Traveling Speeds

The problem of the prompt detection of early-stage hollow worn wheels in railway vehicles via on-board random vibration measurements under normal operation and varying speeds is investigated. This is achieved based on two unsupervised statistical time series (STS) methods which are founded on a multiple-model (MM) framework for the representation of healthy vehicle dynamics. The unsupervised MM power spectral density (U-MM-PSD) method employs Welch-based PSD estimates for wheel wear detection and the unsupervised MM autoregressive (U-MM-AR) method for the parameter vectors of multiple AR models. Both methods are assessed via two case studies using thousands of test cases. The first case study includes Monte Carlo simulations using a SIMPACK-based detailed railway vehicle model, while the second is based on field tests with an Athens Metro train. Wheel wear detection is pursued using lateral or vertical vibration signals from the bogie or the carbody of a trailed vehicle traveling with three different speeds (60, 70, 80 km/h) using wheels under healthy conditions or with early stage hollow wear. Both methods exhibit remarkable performance with the U-MM-AR method to achieve the best overall results, reaching correct detection rates of even 100% with false alarm rates below 5% based on a single accelerometer either on the carbody or bogie.

Monitoring of Local Earthquakes in Haiti Using Low-Cost, Citizen-Hosted Seismometers and Regional Broadband Stations

Abstract Seismic monitoring in Haiti is currently provided by a mixed network of low-cost Raspberry Shake (RS) seismic stations hosted by citizens, and short-period and broadband stations located mainly in neighboring countries. The level of earthquake detection is constantly improving for a better spatio-temporal distribution of seismicity as the number of RS increases. In this article, we analyze the impact of the quality of the signals recorded by the RS—low-cost seismometers with the smallest magnitude that the network can detect by studying the ambient noise level at these stations. Because the RS stations are installed as part of a citizen-science project, their ambient noise estimated by the power spectral density (PSD) method often shows a high-noise level at frequencies above 1 Hz. In the near field (<50 km), we show that the network detects seismic events of local magnitude on the order of 2.2 with signal-to-noise ratios (SNRs) greater than 4. Improving the network detection threshold requires densifying the network with more RS stations in locations that are less noisy, if possible. In spite of these limitations, this mixed network has provided near-field data essential to rapidly understand the mechanism of the mainshock of the 14 August 2021 Mw 7.2 earthquake, to monitor its sequence of aftershocks in near-real time, and to monitor background seismicity in Haiti on a routine basis.

Spectral features of non-nutritive suck dynamics in extremely preterm infants.

Non-nutritive suck (NNS) is used to promote ororhythmic patterning and assess oral feeding readiness in preterm infants in the neonatal intensive care unit (NICU). While time domain measures of NNS are available in real time at cribside, our understanding of suck pattern generation in the frequency domain is limited. The aim of this study is to model the development of NNS in the frequency domain using Fourier and machine learning (ML) techniques in extremely preterm infants (EPIs). A total of 117 EPIs were randomized to a pulsed or sham orocutaneous intervention during tube feedings 3 times/day for 4 weeks, beginning at 30 weeks post-menstrual age (PMA). Infants were assessed 3 times/week for NNS dynamics until they attained 100% oral feeding or NICU discharge. Digitized NNS signals were processed in the frequency domain using two transforms, including the Welch power spectral density (PSD) method, and the Yule-Walker PSD method. Data analysis proceeded in two stages. Stage 1: ML longitudinal cluster analysis was conducted to identify groups (classes) of infants, each showing a unique pattern of change in Welch and Yule-Walker calculations during the interventions. Stage 2: linear mixed modeling (LMM) was performed for the Welch and Yule-Walker dependent variables to examine the effects of gestationally-aged (GA), PMA, sex (male, female), patient type [respiratory distress syndrome (RDS), bronchopulmonary dysplasia (BPD)], treatment (NTrainer, Sham), intervention phase [1, 2, 3], cluster class, and phase-by-class interaction. ML of Welch PSD method and Yule-Walker PSD method measures revealed three membership classes of NNS growth patterns. The dependent measures peak_Hz, PSD amplitude, and area under the curve (AUC) are highly dependent on PMA, but show little relation to respiratory status (RDS, BPD) or somatosensory intervention. Thus, neural regulation of NNS in the frequency domain is significantly different for each identified cluster (classes A, B, C) during this developmental period. Efforts to increase our knowledge of the evolution of the suck central pattern generator (sCPG) in preterm infants, including NNS rhythmogenesis will help us better understand the observed phenotypes of NNS production in both the frequency and time domains. Knowledge of those features of the NNS which are relatively invariant vs. other features which are modifiable by experience will likewise inform more effective treatment strategies in this fragile population.

Experimental‐based study of gear vibration characteristics incorporating the fractal topography of tooth surface

AbstractMicroscopic roughness is inevitable on the gear meshing surface, which is also a key parameter affecting the dynamic response. The surface roughness exhibits self‐affine characteristics across multiscales. To explore the influence of surface fractal topography on the vibration amplitude of the gear system under different rotational speeds and loads, an experimental setup of spur gear transmission is devised. The fractal dimension and fractal roughness of the meshing surface are calculated by the power spectral density method. The relationships between gear response and fractal parameters are revealed experimentally. Results indicate that a rougher tooth surface, that is, a smaller fractal dimension or larger fractal roughness, corresponds to an intense vibration amplitude. The sensitivity of dynamic response to the tooth surface topography varies at different rotational speeds and loads. Under low speed and light load conditions, the fractal dimension and fractal roughness have a more obvious influence on the dynamic response of the gear transmission system. With the increase of speed and load, the macroworking conditions gradually become the main factor attributed to vibration amplitude.

Performance evaluation of a short-distance transition subgrade with pile-plank structures in high-speed railway

ABSTRACT The transition subgrade is widely applied between the general subgrade and rigid structures (such as abutments, culverts, and tunnels) to reduce the degradation rate of track structures in high-speed railways. To address the problem that the common-type transition subgrade cannot be applied in mountainous areas due to the short distance between the tunnel and the subgrade (or abutment), a new type of pile-plank structure (PPS) transition subgrade for short-distance transition was proposed, and the PPS subgrade was successfully applied to a bridge-tunnel transition zone in the Shanghai-Kunming high-speed railway, China. A series of field tests, including vibration acceleration and velocity tests, were carried out in the bridge-tunnel transition zone to evaluate the transition performance of the PPS subgrade using the effective value and power spectral density method. In addition, an FE model that considers the coupled vehicle-track-subgrade-foundation interaction was established to fully reveal the dynamic performance of the PPS subgrade by characterizing the change rate of rail deflection, the dynamic response distribution of the track structure along the transition zone, and the running quality of the train comfort. The results show that the proposed PPS transition subgrade has a good transition performance and meets the requirements for excellent train comfort.

EEG power spectrum analysis for Tunisian schizophrenic patients

IntroductionSchizophrenia (SCZ) is a common and disabling psychiatric condition. Its diagnosis is entirely clinical. Many researchers have looked into electroencephalogram (EEG) power spectrum analysis for SCZ to find specific abnormalities.Objectives The objective of this study was to analyze the EEG of patients with SCZ using power spectral density and to compare them with those of healthy controls, in order to look for specificities.Methods This was a cross-sectional, descriptive, and analytical case-control study conducted with patients followed for SCZ in the psychiatry "C" department at the Hedi Chaker hospital in Sfax. Healthy controls were included. Patients were assessed by the Positive and Negative schizophrenia scale (PANSS). All participants benefited from an EEG at rest condition at the service of the functional exploration at the Habib Bourguiba hospital in Sfax. We have measured the powers of each band using Welch power spectral density method called absolute power (AP). Statistical analyses were carried out.ResultsFifteen schizophrenics and 15 controls, all male, were included. The average age of schizophrenics and healthy controls was 40 years ±12.72 years and 47.93 ± 15.61 years respectively. There were no significant differences in age between patients and controls. Schizophrenics had a mean PANSS of 64.6±22.7.At the quantitative EEG, differences appeared to be insignificant. There was an overall decrease in AP for the alpha band particularly in the parietal and occipital lobes in schizophrenics (53,16 ± 48,83 μV2 and 75,17 ± 56,28 μV2 respectively) compared to controls (335,15 ± 994,73 μV2 and 400,24 ± 1109,95 μV2 respectively). There was also an overall decrease in AP for the different frequency bands in schizophrenics compared to controls. However, values persisted high in the temporal lobe for all frequency bands.ConclusionsIn conclusion, this decrease in AP for the alpha band in the parietal and occipital lobes in schizophrenics can be a sensitive biomarker for diagnosing SCZ.Disclosure of InterestNone Declared