Investigating the impact of schizophrenia traits on attention: the role of the theta band in a modified Posner cueing paradigm.

Joint attention is an indispensable tool for daily communication. Abnormalities in joint attention may be a key reason underlying social impairment in schizophrenia spectrum disorders. In this study, we aimed to explore the attentional orientation mechanism related to schizotypal traits in a social situation. Here, we employed a Posner cueing paradigm with social attentional cues. Subjects needed to detect the location of a target that is cued by gaze and head orientation. The power in the theta frequency band was used to examine the attentional process in the schizophrenia spectrum. There were four main findings. First, a significant association was found between schizotypal traits and attention orientation in response to invalid gaze cues. Second, individuals with schizotypal traits exhibited significant activation of neural oscillations and synchrony in the theta band, which correlated with their schizotypal tendencies. Third, neural oscillations and synchrony demonstrated a synergistic effect during social tasks, particularly when processing gaze cues. Finally, the relationship between schizotypal traits and attention orientation was mediated by neural oscillations and synchrony in the theta frequency band. These findings deepen our understanding of the impact of theta activity in schizotypal traits on joint attention and offer new insights for future intervention strategies.

Height matters: An EEG study of the relative height disadvantage promoting third-party punishment.

The study employed event-related potential (ERP), time-frequency analysis, and functional connectivity to comprehensively explore the influence of male's relative height on third-party punishment (TPP) and its underlying neural mechanism. The results found that punishment rate and more transfer amount are significantly greater when the height of the third-party is lower than that of the recipient, suggesting that male's height disadvantage promotes TPP. Neural results found that the height disadvantage induced a smaller N1. The height disadvantage also evoked greater P300 amplitude, more theta power, and more alpha power. Furthermore, a significantly stronger wPLI between the rTPJ and the posterior parietal and a significantly stronger wPLI between the DLPFC and the posterior parietal were observed when third-party was at the height disadvantage. These results imply that the height disadvantage causes negative emotions and affects the fairness consideration in the early processing stage; The third-party evaluates the blame of violators and makes an appropriate punishment decision later. Our findings indicate that anger and reputation concern caused by height disadvantage promote TPP. The current study holds significance as it underscores the psychological importance of height in males, broadens the perspective on factors influencing TPP, validates the promoting effect of personal disadvantages on prosocial behavior, enriches our understanding of indirect reciprocity theory, and extends the application of the evolution theory of Napoleon complex.

The phase coherence of the neurovascular unit is reduced in Huntington's disease.

Huntington's disease is a neurodegenerative disorder in which neuronal death leads to chorea and cognitive decline. Individuals with ≥40 cytosine-adenine-guanine repeats on the interesting transcript 15 gene develop Huntington's disease due to a mutated huntingtin protein. While the associated structural and molecular changes are well characterized, the alterations in neurovascular function that lead to the symptoms are not yet fully understood. Recently, the neurovascular unit has gained attention as a key player in neurodegenerative diseases. The mutant huntingtin protein is known to be present in the major parts of the neurovascular unit in individuals with Huntington's disease. However, a non-invasive assessment of neurovascular unit function in Huntington's disease has not yet been performed. Here, we investigate neurovascular interactions in presymptomatic (N = 13) and symptomatic (N = 15) Huntington's disease participants compared to healthy controls (N = 36). To assess the dynamics of oxygen transport to the brain, functional near-infrared spectroscopy, ECG and respiration effort were recorded. Simultaneously, neuronal activity was assessed using EEG. The resultant time series were analysed using methods for discerning time-resolved multiscale dynamics, such as wavelet transform power and wavelet phase coherence. Neurovascular phase coherence in the interval around 0.1 Hz is significantly reduced in both Huntington's disease groups. The presymptomatic Huntington's disease group has a lower power of oxygenation oscillations compared to controls. The spatial coherence of the oxygenation oscillations is lower in the symptomatic Huntington's disease group compared to the controls. The EEG phase coherence, especially in the α band, is reduced in both Huntington's disease groups and, to a significantly greater extent, in the symptomatic group. Our results show a reduced efficiency of the neurovascular unit in Huntington's disease both in the presymptomatic and symptomatic stages of the disease. The vasculature is already significantly impaired in the presymptomatic stage of the disease, resulting in reduced cerebral blood flow control. The results indicate vascular remodelling, which is most likely a compensatory mechanism. In contrast, the declines in α and γ coherence indicate a gradual deterioration of neuronal activity. The results raise the question of whether functional changes in the vasculature precede the functional changes in neuronal activity, which requires further investigation. The observation of altered dynamics paves the way for a simple method to monitor the progression of Huntington's disease non-invasively and evaluate the efficacy of treatments.

Analysis of Vibration Characteristics of Bridge Structures under Seismic Excitation.

Bridges may undergo structural vibration responses when exposed to seismic waves. An analysis of structural vibration characteristics is essential for evaluating the safety and stability of a bridge. In this paper, a signal time-frequency feature extraction method (NTFT-ESVD) integrating standard time-frequency transformation, singular value decomposition, and information entropy is proposed to analyze the vibration characteristics of structures under seismic excitation. First, the experiment simulates the response signal of the structure when exposed to seismic waves. The results of the time-frequency analysis indicate a maximum relative error of only 1% in frequency detection, and the maximum relative errors in amplitude and time parameters are 5.9% and 6%, respectively. These simulation results demonstrate the reliability of the NTFT-ESVD method in extracting the time-frequency characteristics of the signal and its suitability for analyzing the seismic response of the structure. Then, a real seismic wave event of the Su-Tong Yangtze River Bridge during the Hengchun earthquake in Taiwan (2006) is analyzed. The results show that the seismic waves only have a short-term impact on the bridge, with the maximum amplitude of the vibration response no greater than 1 cm, and the maximum vibration frequency no greater than 0.2 Hz in the three-dimensional direction, indicating that the earthquake in Hengchun will not have any serious impact on the stability and security of the Su-Tong Yangtze River Bridge. Additionally, the reliability of determining the arrival time of seismic waves by extracting the time-frequency information from structural vibration response signals is validated by comparing it with results from seismic stations (SSE/WHN/QZN) at similar epicenter distances published by the USGS. The results of the case study show that the combination of dynamic GNSS monitoring technology and time-frequency analysis can be used to analyze the impact of seismic waves on the bridge, which is of great help to the manager in assessing structural seismic damage.

The human brain deals with violating general color or depth knowledge in different time courses.

Utilizing the high temporal resolution of event-related potentials (ERPs), we compared the time course of processing incongruent color versus 3D-depth information. Participants were asked to judge whether the food color (color condition) or 3D structure (3D-depth condition) was congruent or incongruent with their previous knowledge and experience. The behavioral results showed that the reaction times in the congruent 3D-depth condition were slower than those in the congruent color condition. The reaction times in the incongruent 3D-depth condition were slower than those in the incongruent color condition. The ERP results showed that incongruent color stimuli induced a larger N270, larger P300, and smaller N400 components in the fronto-central region than the congruent color stimuli. Incongruent 3D-depth stimuli induced a smaller N1 in the occipital region, larger P300 and smaller N400 in the parietal-occipital region than congruent 3D-depth stimuli. The time-frequency analysis found that incongruent color stimuli induced a larger theta band (360-580 ms) activation in the fronto-central region than congruent color stimuli. Incongruent 3D-depth stimuli induced larger alpha and beta bands (240-350 ms) activation in the parietal region than congruent 3D-depth stimuli. Our results suggest that the human brain deals with violating general color or depth knowledge in different time courses. We speculate that the depth perception conflict was dominated by solving the problem with visual processing, whereas the color perception conflict was dominated by solving the problem with semantic violation.

A Low-Cost Sensing Solution for SHM, Exploiting a Dedicated Approach for Signal Recognition.

Health assessment and preventive maintenance of structures are mandatory to predict injuries and to schedule required interventions, especially in seismic areas. Structural health monitoring aims to provide a robust and effective approach to obtaining valuable information on structural conditions of buildings and civil infrastructures, in conjunction with methodologies for the identification and, sometimes, localization of potential risks. In this paper a low-cost solution for structural health monitoring is proposed, exploiting a customized embedded system for the acquisition and storing of measurement signals. Experimental surveys for the assessment of the sensing node have also been performed. The obtained results confirmed the expected performances, especially in terms of resolution in acceleration and tilt measurement, which are 0.55 mg and 0.020°, respectively. Moreover, we used a dedicated algorithm for the classification of recorded signals in the following three classes: noise floor (being mainly related to intrinsic noise of the sensing system), exogenous sources (not correlated to the dynamic behavior of the structure), and structural responses (the response of the structure to external stimuli, such as seismic events, artificially forced and/or environmental solicitations). The latter is of main interest for the investigation of structures' health, while other signals need to be recognized and filtered out. The algorithm, which has been tested against real data, demonstrates relevant features in performing the above-mentioned classification task.

Redefined giant somatosensory evoked potentials: Evoked epileptic complexes of excitatory and inhibitory components.

OBJECTIVE: Giant somatosensory evoked potentials (SEPs) are observed in patients with cortical myoclonus. Short-latency components (SLC), are regarded as evoked epileptic activities or paroxysmal depolarization shifts (PDSs). This study aimed to reveal the electrophysiological significance of the middle-latency component (MLC) P50 of the SEPs. METHODS: Twenty-two patients with cortical myoclonus having giant SEPs (patient group) and 15 healthy controls were included in this study. Waveform changes in SEPs before and after perampanel (PER) treatment were evaluated in the patient group. The wide range, time-frequency properties underlying the waveforms were compared between the groups. RESULTS: After PER treatment, SLC was prolonged and positively correlated with PER concentration, whereas MLC showed no correlation with PER concentration. Time-frequency analysis showed a power increase (156 Hz in all patients, 624 Hz in benign adult familial myoclonus epilepsy patients) underlying SLC and a power decrease (156 Hz, 624 Hz) underlying MLC in the patient group. CONCLUSIONS: The high-frequency power increase in SLCs and decrease in MLCs clearly reflected PDS and subsequent hyperpolarization, respectively. This relationship was similar to that of interictal epileptiform discharges, suggesting that giant SEPs evoke epileptic complexes of excitatory and inhibitory components. SIGNIFICANCE: MLCs of giant SEPs reflected inhibitory components.

Optimal Scale-Invariant Wavelet Representation and Filtering of Human Otoacoustic Emissions.

Otoacoustic emissions (OAEs) are generated in the cochlea and recorded in the ear canal either as a time domain waveform or as a collection of complex responses to tones in the frequency domain (Probst et al. J Account Soc Am 89:2027-2067, 1991). They are typically represented either in their original acquisition domain or in its Fourier-conjugated domain. Round-trip excursions to the conjugated domain are often used to perform filtering operations in the computationally simplest way, exploiting the convolution theorem. OAE signals consist of the superposition of backward waves generated in different cochlear regions by different generation mechanisms, over a wide frequency range. The cochlear scaling symmetry (cochlear physics is the same at all frequency scales), which approximately holds in the human cochlea, leaves its fingerprints in the mathematical properties of OAE signals. According to a generally accepted taxonomy (Sher and Guinan Jr, J Acoust Soc Am 105:782-798, 1999), OAEs are generated either by wave-fixed sources, moving with frequency according with the cochlear scaling (as in nonlinear distortion) or by place-fixed sources (as in coherent reflection by roughness). If scaling symmetry holds, the two generation mechanisms yield OAEs with different phase gradient delay: almost null for wave-fixed sources, and long (and scaling as 1/f) for place-fixed sources. Thus, the most effective representation of OAE signals is often that respecting the cochlear scale-invariance, such as the time-frequency domain representation provided by the wavelet transform. In the time-frequency domain, the elaborate spectra or waveforms yielded by the superposition of OAE components from different generation mechanisms assume a much clearer 2-D pattern, with each component localized in a specific and predictable region. The wavelet representation of OAE signals is optimal both for visualization purposes and for designing filters that effectively separate different OAE components, improving both the specificity and the sensitivity of OAE-based applications. Indeed, different OAE components have different physiological meanings, and filtering dramatically improves the signal-to-noise ratio.

Effects of parietal repetitive transcranial magnetic stimulation in prolonged disorders of consciousness: A pilot study.

Objective: Although the parietal cortex is related to consciousness, the dorsolateral prefrontal and primary motor cortices are the usual targets for repetitive transcranial magnetic stimulation (rTMS) for prolonged disorders of consciousness (pDoC). Herein, we applied parietal rTMS to patients with pDoC, to verify its neurobehavioral effects and explore a new potential rTMS target. Materials and methods: Twenty-six patients with pDoC were assigned to a rTMS or sham group. The rTMS group received 10 sessions of parietal rTMS; the sham group received 10 sessions of sham stimulation. The Coma Recovery Scale-Revised (CRS-R) and event-related potential (ERP) were collected before and after the 10 sessions or sham sessions. Results: After the 10 sessions, the rTMS group showed: a significant CRS-R score increase; ERP appearance of a P300 waveform and significantly increased Fz amplitudes; increased potentials on topographic mapping, especially in the left prefrontal cortex; and an increase in delta and theta band powers at Fz, Cz, and Pz. The sham group did not show such changes in CRS-R score or ERP results statistically. Conclusion: Parietal rTMS shows promise as a novel intervention in the recovery of consciousness in pDoC. It showed neurobehavioral enhancement of residual brain function and may promote frontal activity by enhancing frontal-parietal connections. The parietal cortex may thus be an alternative for rTMS therapy protocols.

Altered neural oscillations in classical galactosaemia during sentence production.

Classical galactosaemia (CG) is a hereditary disease in galactose metabolism that despite dietary treatment is characterized by a wide range of cognitive deficits, among which is language production. CG brain functioning has been studied with several neuroimaging techniques, which revealed both structural and functional atypicalities. In the present study, for the first time, we compared the oscillatory dynamics, especially the power spectrum and time-frequency representations (TFR), in the electroencephalography (EEG) of CG patients and healthy controls while they were performing a language production task. Twenty-one CG patients and 19 healthy controls described animated scenes, either in full sentences or in words, indicating two levels of complexity in syntactic planning. Based on previous work on the P300 event related potential (ERP) and its relation with theta frequency, we hypothesized that the oscillatory activity of patients and controls would differ in theta power and TFR. With regard to behavior, reaction times showed that patients are slower, reflecting the language deficit. In the power spectrum, we observed significant higher power in patients in delta (1-3 Hz), theta (4-7 Hz), beta (15-30 Hz) and gamma (30-70 Hz) frequencies, but not in alpha (8-12 Hz), suggesting an atypical oscillatory profile. The time-frequency analysis revealed significantly weaker event-related theta synchronization (ERS) and alpha desynchronization (ERD) in patients in the sentence condition. The data support the hypothesis that CG language difficulties relate to theta-alpha brain oscillations.

Prediction error in implicit adaptation during visually- and memory-guided reaching tasks.

Human movements are adjusted by motor adaptation in order to maintain their accuracy. There are two systems in motor adaptation, referred to as explicit or implicit adaptation. It has been suggested that the implicit adaptation is based on the prediction error and has been used in a number of motor adaptation studies. This study aimed to examine the effect of visual memory on prediction error in implicit visuomotor adaptation by comparing visually- and memory-guided reaching tasks. The visually-guided task is thought to be implicit learning based on prediction error, whereas the memory-guided task requires more cognitive processes. We observed the adaptation to visuomotor rotation feedback that is gradually rotated. We found that the adaptation and retention rates were higher in the visually-guided task than in the memory-guided task. Furthermore, the delta-band power obtained by electroencephalography (EEG) in the visually-guided task was increased immediately following the visual feedback, which indicates that the prediction error was larger in the visually-guided task. Our results show that the visuomotor adaptation is enhanced in the visually-guided task because the prediction error, which contributes update of the internal model, was more reliable than in the memory-guided task. Therefore, we suggest that the processing of the prediction error is affected by the task-type, which in turn affects the rate of the visuomotor adaptation.

A Synchrosqueezed Transform Method Based on Fast Kurtogram and Demodulation and Piecewise Aggregate Approximation for Bearing Fault Diagnosis.

Synchrosqueezed transform (SST) is a time-frequency analysis method that can improve energy aggregation and reconstruct signals, which has been applied in the fields of medical treatment, fault diagnosis, and seismic wave processing. However, when dealing with time-varying signals, SST suffers from poor time-frequency resolution and is unable to deal with long signals. In order to accurately extract the characteristic frequency of variable speed rolling bearing faults, this paper proposes a synchrosqueezed transform method based on fast kurtogram and demodulation and piecewise aggregate approximation (PAA). The method firstly filters and demodulates the original signal using fast kurtogram and Hilbert transform to reduce the influence of background noise and improve the time-frequency resolution. Then, it compresses the signal by using piecewise aggregate approximation, so that the SST can deal with long signals and, thus, extract the fault characteristic frequency. The experimental data verification results indicate that the method can effectively identify the fault characteristic frequency of variable-speed rolling bearings.

On a Closer Look of a Doppler Tolerant Noise Radar Waveform in Surveillance Applications.

The prevalence of Low Probability of Interception (LPI) and Low Probability of Exploitation (LPE) radars in contemporary Electronic Warfare (EW) presents an ongoing challenge to defense mechanisms, compelling constant advances in protective strategies. Noise radars are examples of LPI and LPE systems that gained substantial prominence in the past decade despite exhibiting a common drawback of limited Doppler tolerance. The Advanced Pulse Compression Noise (APCN) waveform is a stochastic radar signal proposed to amalgamate the LPI and LPE attributes of a random waveform with the Doppler tolerance feature inherent to a linear frequency modulation. In the present work, we derive closed-form expressions describing the APCN signal's ambiguity function and spectral containment that allow for a proper analysis of its detection performance and ability to remove range ambiguities as a function of its stochastic parameters. This paper also presents a more detailed address of the LPI/LPE characteristic of APCN signals claimed in previous works. We show that sophisticated Electronic Intelligence (ELINT) systems that employ Time Frequency Analysis (TFA) and image processing methods may intercept APCN and estimate important parameters of APCN waveforms, such as bandwidth, operating frequency, time duration, and pulse repetition interval. We also present a method designed to intercept and exploit the unique characteristics of the APCN waveform. Its performance is evaluated based on the probability of such an ELINT system detecting an APCN radar signal as a function of the Signal-to-Noise Ratio (SNR) in the ELINT system. We evaluated the accuracy and precision of the random variables characterizing the proposed estimators as a function of the SNR. Results indicate a probability of detection close to 1 and show good performance, even for scenarios with a SNR slightly less than -10 dB. The contributions in this work offer enhancements to noise radar capabilities while facilitating improvements in ESM systems.

STNet: A Time-Frequency Analysis-Based Intrusion Detection Network for Distributed Optical Fiber Acoustic Sensing Systems.

Distributed optical fiber acoustic sensing (DAS) is promising for long-distance intrusion-anomaly detection tasks. However, realistic settings suffer from high-intensity interference noise, compromising the detection performance of DAS systems. To address this issue, we propose STNet, an intrusion detection network based on the Stockwell transform (S-transform), for DAS systems, considering the advantages of the S-transform in terms of noise resistance and ability to detect disturbances. Specifically, the signal detected by a DAS system is divided into space-time data matrices using a sliding window. Subsequently, the S-transform extracts the time-frequency features channel by channel. The extracted features are combined into a multi-channel time-frequency feature matrix and presented to STNet. Finally, a non-maximum suppression algorithm (NMS), suitable for locating intrusions, is used for the post-processing of the detection results. To evaluate the effectiveness of the proposed method, experiments were conducted using a realistic high-speed railway environment with high-intensity noise. The experimental results validated the satisfactory performance of the proposed method. Thus, the proposed method offers an effective solution for achieving high intrusion detection rates and low false alarm rates in complex environments.

Unmanned Surface Vehicle Thruster Fault Diagnosis via Vibration Signal Wavelet Transform and Vision Transformer under Varying Rotational Speed Conditions.

Among unmanned surface vehicle (USV) components, underwater thrusters are pivotal in their mission execution integrity. Yet, these thrusters directly interact with marine environments, making them perpetually susceptible to malfunctions. To diagnose thruster faults, a non-invasive and cost-effective vibration-based methodology that does not require altering existing systems is employed. However, the vibration data collected within the hull is influenced by propeller-fluid interactions, hull damping, and structural resonant frequencies, resulting in noise and unpredictability. Furthermore, to differentiate faults not only at fixed rotational speeds but also over the entire range of a thruster's rotational speeds, traditional frequency analysis based on the Fourier transform cannot be utilized. Hence, Continuous Wavelet Transform (CWT), known for attributions encapsulating physical characteristics in both time-frequency domain nuances, was applied to address these complications and transform vibration data into a scalogram. CWT results are diagnosed using a Vision Transformer (ViT) classifier known for its global context awareness in image processing. The effectiveness of this diagnosis approach was verified through experiments using a USV designed for field experiments. Seven cases with different fault types and severity were diagnosed and yielded average accuracy of 0.9855 and 0.9908 at different vibration points, respectively.

Automatic Wake and Deep-Sleep Stage Classification Based on Wigner-Ville Distribution Using a Single Electroencephalogram Signal.

This research paper outlines a method for automatically classifying wakefulness and deep sleep stage (N3) based on the American Academy of Sleep Medicine (AASM) standards. The study employed a single-channel EEG signal, leveraging the Wigner-Ville Distribution (WVD) for time-frequency analysis to determine EEG energy per second in specific frequency bands (δ, θ, α, and entire band). Particle Swarm Optimization (PSO) was used to optimize thresholds for distinguishing between wakefulness and stage N3. This process aims to mimic a sleep technician's visual scoring but in an automated fashion, with features and thresholds extracted to classify epochs into correct sleep stages. The study's methodology was validated using overnight PSG recordings from 20 subjects, which were evaluated by a technician. The PSG setup followed the 10-20 standard system with varying sampling rates from different hospitals. Two baselines, T1 for the wake stage and T2 for the N3 stage, were calculated using PSO to ascertain the best thresholds, which were then used to classify EEG epochs. The results showed high sensitivity, accuracy, and kappa coefficient, indicating the effectiveness of the classification algorithm. They suggest that the proposed method can reliably determine sleep stages, being aligned closely with the AASM standards and offering an intuitive approach. The paper highlights the strengths of the proposed method over traditional classifiers and expresses the intentions to extend the algorithm to classify all sleep stages in the future.

EEG rhythm separation and time-frequency analysis of fast multivariate empirical mode decomposition for motor imagery BCI.

Motor imagery electroencephalogram (EEG) is widely employed in brain-computer interface (BCI) systems. As a time-frequency analysis method for nonlinear and non-stationary signals, multivariate empirical mode decomposition (MEMD) and its noise-assisted version (NA-MEMD) has been widely used in the preprocessing step of BCI systems for separating EEG rhythms corresponding to specific brain activities. However, when applied to multichannel EEG signals, MEMD or NA-MEMD often demonstrate low robustness to noise and high computational complexity. To address these issues, we have explored the advantages of our recently proposed fast multivariate empirical mode decomposition (FMEMD) and its noise-assisted version (NA-FMEMD) for analyzing motor imagery data. We emphasize that FMEMD enables a more accurate estimation of EEG frequency information and exhibits a more noise-robust decomposition performance with improved computational efficiency. Comparative analysis with MEMD on simulation data and real-world EEG validates the above assertions. The joint average frequency measure is employed to automatically select intrinsic mode functions that correspond to specific frequency bands. Thus, FMEMD-based classification architecture is proposed. Using FMEMD as a preprocessing algorithm instead of MEMD can improve the classification accuracy by 2.3% on the BCI Competition IV dataset. On the Physiobank Motor/Mental Imagery dataset and BCI Competition IV Dataset 2a, FMEMD-based architecture also attained a comparable performance to complex algorithms. The results indicate that FMEMD proficiently extracts feature information from small benchmark datasets while mitigating dimensionality constraints resulting from computational complexity. Hence, FMEMD or NA-FMEMD can be a powerful time-frequency preprocessing method for BCI.

Theta and alpha oscillatory signatures of auditory sensory and cognitive loads during complex listening.

The neuronal signatures of sensory and cognitive load provide access to brain activities related to complex listening situations. Sensory and cognitive loads are typically reflected in measures like response time (RT) and event-related potentials (ERPs) components. It's, however, strenuous to distinguish the underlying brain processes solely from these measures. In this study, along with RT- and ERP-analysis, we performed time-frequency analysis and source localization of oscillatory activity in participants performing two different auditory tasks with varying degrees of complexity and related them to sensory and cognitive load. We studied neuronal oscillatory activity in both periods before the behavioral response (pre-response) and after it (post-response). Robust oscillatory activities were found in both periods and were differentially affected by sensory and cognitive load. Oscillatory activity under sensory load was characterized by decrease in pre-response (early) theta activity and increased alpha activity. Oscillatory activity under cognitive load was characterized by increased theta activity, mainly in post-response (late) time. Furthermore, source localization revealed specific brain regions responsible for processing these loads, such as temporal and frontal lobe, cingulate cortex and precuneus. The results provide evidence that in complex listening situations, the brain processes sensory and cognitive loads differently. These neural processes have specific oscillatory signatures and are long lasting, extending beyond the behavioral response.

An unsupervised water quality anomaly detection method based on a combination of time-frequency analysis and clustering.

Anomalies in water quality, which frequently arise due to pollution, constitute a substantial menace to human health. The preservation of public welfare critically entails the timely recognition of abnormal water quality. Conventional techniques for detecting water quality anomalies face obstacles such as the necessity of expert knowledge, limited accuracy in detection, and delays in identification. In this paper, we proposed an original unsupervised technique for identifying water quality anomalies combined with time-frequency analysis and clustering (TCAD). We chose time-frequency analysis because it effectively evaluates water quality changes, generating distinct multi-band signals that reflect different aspects of water quality dynamics. We also proposed a clustering technique which can identify water quality markers and amalgamate data from multi-band signals for accurate anomaly detection. We seek to clarify the reasoning behind our methodology by portraying how time-frequency analysis and clustering address the deficiencies of conventional methods. Our experiments evaluated various indicators of water quality, and the effectiveness of our proposed approach was supported by comparative analyses with commonly used models for detecting anomalies in water quality.

Detecting Autism Spectrum Disorder and Attention Deficit Hyperactivity Disorder Using Multimodal Time-Frequency Analysis with Machine Learning Using the Electroretinogram from Two Flash Strengths.

PURPOSE: Autism spectrum disorder (ASD) and attention deficit hyperactivity disorder (ADHD) are conditions that similarly alter cognitive functioning ability and challenge the social interaction, attention, and communication skills of affected individuals. Yet these are distinct neurological conditions that can exhibit diverse characteristics which require different management strategies. It is desirable to develop tools to assist with early distinction so that appropriate early interventions and support may be tailored to an individual's specific requirements. The current diagnostic procedures for ASD and ADHD require a multidisciplinary approach and can be lengthy. This study investigated the potential of electroretinogram (ERG), an eye test measuring retinal responses to light, for rapid screening of ASD and ADHD. METHODS: Previous studies identified differences in ERG amplitude between ASD and ADHD, but this study explored time-frequency analysis (TFS) to capture dynamic changes in the signal. ERG data from 286 subjects (146 control, 94 ASD, 46 ADHD) was analyzed using two TFS techniques. RESULTS: Key features were selected, and machine learning models were trained to classify individuals based on their ERG response. The best model achieved 70% overall accuracy in distinguishing control, ASD, and ADHD groups. CONCLUSION: The ERG to the stronger flash strength provided better separation and the high frequency dynamics (80-300 Hz) were more informative features than lower frequency components. To further improve classification a greater number of different flash strengths may be required along with a discrimination comparison to participants who meet both ASD and ADHD classifications and carry both diagnoses.