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Journal articles on the topic 'Bandpower'

Author: Grafiati

Published: 3 June 2025

Last updated: 31 July 2025

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1

Caviness, John N., Thomas G. Beach, Joseph G. Hentz, Holly A. Shill, Erika D. Driver-Dunckley, and Charles H. Adler. "Association Between Pathology and Electroencephalographic Activity in Parkinson’s Disease." Clinical EEG and Neuroscience 49, no. 5 (2017): 321–27. http://dx.doi.org/10.1177/1550059417696179.

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Introduction. The key mechanisms that connect Parkinson’s disease pathology with dementia are unclear. We tested the hypothesis that the quantitative spectral electroencephalographic measure, delta bandpower, correlates with Lewy type synucleinopathy on pathological examination in Parkinson’s disease. As a corollary hypothesis, we analyzed whether there would be delta bandpower electroencephalographic differences between Parkinson’s disease dementia cases with and without pathological criteria for Alzheimer’s disease. Methods. We used pathological examination results from 44 Parkinson’s diseas

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Jeakle, Eleanor N., Justin R. Abbott, Joshua O. Usoro, et al. "Chronic Stability of Local Field Potentials Using Amorphous Silicon Carbide Microelectrode Arrays Implanted in the Rat Motor Cortex." Micromachines 14, no. 3 (2023): 680. http://dx.doi.org/10.3390/mi14030680.

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Implantable microelectrode arrays (MEAs) enable the recording of electrical activity of cortical neurons, allowing the development of brain-machine interfaces. However, MEAs show reduced recording capabilities under chronic conditions, prompting the development of novel MEAs that can improve long-term performance. Conventional planar, silicon-based devices and ultra-thin amorphous silicon carbide (a-SiC) MEAs were implanted in the motor cortex of female Sprague–Dawley rats, and weekly anesthetized recordings were made for 16 weeks after implantation. The spectral density and bandpower between

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Doppelmayr, Michael, W. Klimesch, P. Sauseng, K. Hödlmoser, W. Stadler, and S. Hanslmayr. "Intelligence related differences in EEG-bandpower." Neuroscience Letters 381, no. 3 (2005): 309–13. http://dx.doi.org/10.1016/j.neulet.2005.02.037.

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Nelson, Thomas, Brian Coughlin, Daniel Cahill, et al. "CNSC-22. CANCER NEUROSCIENCE IN THE OR: ELECTROPHYSIOLOGIC INSIGHTS AT THE TIME OF BRAIN TUMOR RESECTION." Neuro-Oncology 25, Supplement_5 (2023): v27. http://dx.doi.org/10.1093/neuonc/noad179.0106.

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Abstract Pioneering works by Venkatesh and Venkataramani demonstrated that formation of functional neural-glial synapses fosters development and growth of glial neoplasms. Resultant peritumoral neural hyperexcitability associates with tumor progression, though limited in vivo human data have been evaluated to date. We sought to enrich our understanding of these mechanisms by obtaining thin film intraoperative micro-electrocorticography (µECoG) recordings from patients undergoing primary brain tumor resection. METHODS: Intraoperative µECoG data for 8 patients with low- (LGG) and high-grade glio

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Dahne, Sven, Felix Biessmann, Frank C. Meinecke, Jan Mehnert, Siamac Fazli, and Klaus-Robert Muller. "Integration of Multivariate Data Streams With Bandpower Signals." IEEE Transactions on Multimedia 15, no. 5 (2013): 1001–13. http://dx.doi.org/10.1109/tmm.2013.2250267.

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Dalila, Trad, Al-Ani Tarik, and Jemni Mohamed. "BRAIN Journal - Motor Imagery signal Classification for BCI System Using Empirical Mode Décomposition and Bandpower Feature Extraction." BRAIN - Broad Research in Artificial Intelligence and Neuroscience 7, no. 2 (2016): 5–16. https://doi.org/10.5281/zenodo.1044300.

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ABSTRACT The idea that brain activity could be used as a communication channel has rapidly developed. Indeed, Electroencephalography (EEG) is the most common technique to measure the brain activity on the scalp and in real-time. In this study we examine the use of EEG signals in Brain Computer Interface (BCI). This approach consists of combining the Empirical Mode Decomposition (EMD) and band power (BP) for the extraction of EEG signals in order to classify motor imagery (MI). This new feature extraction approach is intended for non-stationary and non-linear characteristics MI EEG. The EMD met

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Hollenbenders, Y., and A. Reichenbach. "P-103 Robustness of electroencephalography biomarkers for major depressive disorder – An exemplary study with alpha bandpower." Clinical Neurophysiology 148 (April 2023): e55. http://dx.doi.org/10.1016/j.clinph.2023.02.120.

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Sugianela, Yuna, Qonita Luthfia Sutino, and Darlis Herumurti. "EEG CLASSIFICATION FOR EPILEPSY BASED ON WAVELET PACKET DECOMPOSITION AND RANDOM FOREST." Jurnal Ilmu Komputer dan Informasi 11, no. 1 (2018): 27. http://dx.doi.org/10.21609/jiki.v11i1.549.

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EEG (electroencephalogram) can detect epileptic seizures by neurophysiologists in clinical practice with visually scan long recordings. Epilepsy seizure is a condition of brain disorder with chronic noncommunicable that affects people of all ages. The challenge of study is how to develop a method for signal processing that extract the subtle information of EEG and use it for automating the detection of epileptic with high accuration, so we can use it for monitoring and treatment the epileptic patient. In this study we developed a method to classify the EEG signal based on Wavelet Packet Decomp

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Zych, Patryk, Kacper Filipek, Agata Mrozek-Czajkowska, and Piotr Kuwałek. "Classification of Electroencephalography Motor Execution Signals Using a Hybrid Neural Network Based on Instantaneous Frequency and Amplitude Obtained via Empirical Wavelet Transform." Sensors 25, no. 11 (2025): 3284. https://doi.org/10.3390/s25113284.

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Brain–computer interfaces (BCIs) have garnered significant interest due to their potential to enable communication and control for individuals with limited or no ability to interact with technologies in a conventional way. By applying electrical signals generated by brain cells, BCIs eliminate the need for physical interaction with external devices. This study investigates the performance of traditional classifiers—specifically, linear discriminant analysis (LDA) and support vector machines (SVMs)—in comparison with a hybrid neural network model for EEG-based gesture classification. The datase

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Binias, Bartosz, Dariusz Myszor, and Krzysztof A. Cyran. "A Machine Learning Approach to the Detection of Pilot’s Reaction to Unexpected Events Based on EEG Signals." Computational Intelligence and Neuroscience 2018 (2018): 1–9. http://dx.doi.org/10.1155/2018/2703513.

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This work considers the problem of utilizing electroencephalographic signals for use in systems designed for monitoring and enhancing the performance of aircraft pilots. Systems with such capabilities are generally referred to as cognitive cockpits. This article provides a description of the potential that is carried by such systems, especially in terms of increasing flight safety. Additionally, a neuropsychological background of the problem is presented. Conducted research was focused mainly on the problem of discrimination between states of brain activity related to idle but focused anticipa

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Pokol, G., G. Por, S. Zoletnik, and W7-AS team. "Application of a bandpower correlation method to the statistical analysis of MHD bursts in quiescent Wendelstein-7 AS stellarator plasmas." Plasma Physics and Controlled Fusion 49, no. 9 (2007): 1391–408. http://dx.doi.org/10.1088/0741-3335/49/9/003.

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Moinuddin, Kazi Ashraf, Felix Havugimana, Rakib Al-Fahad, Gavin M. Bidelman, and Mohammed Yeasin. "Unraveling Spatial-Spectral Dynamics of Speech Categorization Speed Using Convolutional Neural Networks." Brain Sciences 13, no. 1 (2022): 75. http://dx.doi.org/10.3390/brainsci13010075.

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The process of categorizing sounds into distinct phonetic categories is known as categorical perception (CP). Response times (RTs) provide a measure of perceptual difficulty during labeling decisions (i.e., categorization). The RT is quasi-stochastic in nature due to individuality and variations in perceptual tasks. To identify the source of RT variation in CP, we have built models to decode the brain regions and frequency bands driving fast, medium and slow response decision speeds. In particular, we implemented a parameter optimized convolutional neural network (CNN) to classify listeners’ b

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Shanbhag, Vignesh Vishnudas, Thomas Meyer, Leo Caspers, and Rune Schlanbusch. "Diagnostics of seal and rod degradation in hydraulic cylinders using acoustic emissions." PHM Society European Conference 5, no. 1 (2020): 8. http://dx.doi.org/10.36001/phme.2020.v5i1.1173.

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External leakage from hydraulic cylinders is of a major concern for the offshore oil and gas industry. This occurs mainly as a result of physical damage to the piston rod or due to degradation of the piston rod seals. Numerous studies have been conducted to diagnose leakage from hydraulic cylinders due to seal failure using fluid, pressure, accelerometer or acoustic emission-based condition monitoring techniques. However, very few attempts have been made to diagnose multiple faults in hydraulic cylinders at the same time. Therefore, in this study, acoustic emission-based condition monitoring t

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MacCrann, Niall, Blake D. Sherwin, Frank J. Qu, et al. "The Atacama Cosmology Telescope: Mitigating the Impact of Extragalactic Foregrounds for the DR6 Cosmic Microwave Background Lensing Analysis." Astrophysical Journal 966, no. 1 (2024): 138. http://dx.doi.org/10.3847/1538-4357/ad2610.

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Abstract We investigate the impact and mitigation of extragalactic foregrounds for the cosmic microwave background (CMB) lensing power spectrum analysis of Atacama Cosmology Telescope (ACT) data release 6 (DR6) data. Two independent microwave sky simulations are used to test a range of mitigation strategies. We demonstrate that finding and then subtracting point sources, finding and then subtracting models of clusters, and using a profile bias-hardened lensing estimator together reduce the fractional biases to well below statistical uncertainties, with the inferred lensing amplitude, A lens, b

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Shanbhag, Vignesh V., Bernard F. Rolfe, and Michael P. Pereira. "Investigation of Galling Wear Using Acoustic Emission Frequency Characteristics." Lubricants 8, no. 3 (2020): 25. http://dx.doi.org/10.3390/lubricants8030025.

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In the sheet metal stamping process, during sliding contact between the tool and sheet, it is expected that severe events such as tool wear or fracture on the sheet generate acoustic emission (AE) burst waveforms. Attempts have been made in the literature to correlate the AE burst waveform with the wear mechanisms. However, there is a need for additional studies to understand the frequency characteristics of the AE burst waveform due to the severity and progression of the galling wear. This paper will determine the AE frequency characteristics that can be used to monitor galling wear, independ

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Sailer, Noah, Joshua Kim, Simone Ferraro, et al. "Cosmological constraints from the cross-correlation of DESI Luminous Red Galaxies with CMB lensing from Planck PR4 and ACT DR6." Journal of Cosmology and Astroparticle Physics 2025, no. 06 (2025): 008. https://doi.org/10.1088/1475-7516/2025/06/008.

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Abstract We infer the growth of large scale structure over the redshift range 0.4 ≲ z ≲ 1 from the cross-correlation of spectroscopically calibrated Luminous Red Galaxies (LRGs) selected from the Dark Energy Spectroscopic Instrument (DESI) legacy imaging survey with CMB lensing maps reconstructed from the latest Planck and ACT data. We adopt a hybrid effective field theory (HEFT) model that robustly regulates the cosmological information obtainable from smaller scales, such that our cosmological constraints are reliably derived from the (predominantly) linear regime. We perform an extensive se

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Gambrel, A. E., A. S. Rahlin, X. Song, et al. "The XFaster Power Spectrum and Likelihood Estimator for the Analysis of Cosmic Microwave Background Maps." Astrophysical Journal 922, no. 2 (2021): 132. http://dx.doi.org/10.3847/1538-4357/ac230b.

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Abstract We present the XFaster analysis package, a fast, iterative angular power spectrum estimator based on a diagonal approximation to the quadratic Fisher matrix estimator. It uses Monte Carlo simulations to compute noise biases and filter transfer functions and is thus a hybrid of both Monte Carlo and quadratic estimator methods. In contrast to conventional pseudo-C ℓ –based methods, the algorithm described here requires a minimal number of simulations and does not require them to be precisely representative of the data to estimate accurate covariance matrices for the bandpowers. The form

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Owusu, Kwaku Boateng, William Skinner, and Richmond K. Asamoah. "Acoustic Sensing and Supervised Machine Learning for In Situ Classification of Semi-Autogenous (SAG) Mill Feed Size Fractions Using Different Feature Extraction Techniques." Powders 2, no. 2 (2023): 299–322. http://dx.doi.org/10.3390/powders2020018.

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The harsh and hostile internal environment of semi-autogenous (SAG) mills renders real-time monitoring of some critical variables practically unmeasured. Typically, feed size fractions are known to cause mill fluctuations and impede the consistent processing behaviour of ores. There is, therefore, the need for continuous monitoring of mill parameters for optimal operation. In this paper, an acoustic-based sensing method is employed to estimate, in real time, a snapshot of the different feed size fractions presented to a laboratory-scale SAG mill. Employing the MATLAB 2020b programme, the mill

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Cho, Sue M., Henry H. Joo, Pranathi Golla, et al. "Tremor Assessment in Robot‐Assisted Microlaryngeal Surgery Using Computer Vision‐Based Tool Tracking." Otolaryngology–Head and Neck Surgery, March 15, 2024. http://dx.doi.org/10.1002/ohn.714.

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AbstractObjectiveUse microscopic video‐based tracking of laryngeal surgical instruments to investigate the effect of robot assistance on instrument tremor.Study DesignExperimental trial.SettingTertiary Academic Medical Center.MethodsIn this randomized cross‐over trial, 36 videos were recorded from 6 surgeons performing left and right cordectomies on cadaveric pig larynges. These recordings captured 3 distinct conditions: without robotic assistance, with robot‐assisted scissors, and with robot‐assisted graspers. To assess tool tremor, we employed computer vision‐based algorithms for tracking su

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Angerbauer, Raphael, Ambra Stefani, Jennifer Zitser, et al. "Temporal progression of sleep electroencephalography features in isolated rapid eye movement sleep behaviour disorder." Journal of Sleep Research, September 25, 2024. http://dx.doi.org/10.1111/jsr.14351.

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SummaryPrevious studies indicated that patients with isolated rapid eye movement (REM) sleep behaviour disorder (iRBD) exhibit alterations in spectral electroencephalographic (EEG), spindle, and slow‐wave features. As it is currently unknown how these EEG features evolve over time, this study aimed to evaluate their temporal progression in patients with iRBD in comparison to controls. We included 23 patients with iRBD and 23 controls. Two polysomnographies (baseline and follow‐up) were recorded with a mean (standard deviation) interval of 4.0 (2.5) years and were automatically analysed for sle

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Beck, Dominic, Ari Cukierman, and W. L. Kimmy Wu. "Bias on tensor-to-scalar ratio inference with estimated covariance matrices." Monthly Notices of the Royal Astronomical Society, June 27, 2022. http://dx.doi.org/10.1093/mnras/stac1775.

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Abstract We investigate simulation-based bandpower covariance matrices commonly used in cosmological parameter inferences such as the estimation of the tensor-to-scalar ratio r. We find that upper limits on r can be biased low by tens of percent. The underestimation of the upper limit is most severe when the number of simulation realizations is similar to the number of observables. Convergence of the covariance-matrix estimation can require a number of simulations an order of magnitude larger than the number of observables, which could mean $\mathcal {O}(10\ 000)$ simulations. This is found to

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Ferreira, Luan Oliveira, Bruna Gerrits Mattos, Vanessa Jóia de Mello, et al. "Increased Relative Delta Bandpower and Delta Indices Revealed by Continuous qEEG Monitoring in a Rat Model of Ischemia-Reperfusion." Frontiers in Neurology 12 (April 7, 2021). http://dx.doi.org/10.3389/fneur.2021.645138.

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The present study describes the electroencephalographic changes that occur during cerebral ischemia and reperfusion in animals submitted to transient focal cerebral ischemia by middle cerebral artery occlusion (MCAO) for 30 min. For this, male Wistar rats were divided into two groups (n = 6 animals/group): (1) sham (control) group, and (2) ischemic/reperfusion group. The quantitative electroencephalography (qEEG) was recorded during the ischemic and immediate reperfusion (acute) phases, and then once a day for 7 days after the MCAO (subacute phase). The acute phase was characterized by a marke

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Gill, Jay L., Julia A. Schneiders, Matthias Stangl, et al. "A pilot study of closed-loop neuromodulation for treatment-resistant post-traumatic stress disorder." Nature Communications 14, no. 1 (2023). http://dx.doi.org/10.1038/s41467-023-38712-1.

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AbstractThe neurophysiological mechanisms in the human amygdala that underlie post-traumatic stress disorder (PTSD) remain poorly understood. In a first-of-its-kind pilot study, we recorded intracranial electroencephalographic data longitudinally (over one year) in two male individuals with amygdala electrodes implanted for the management of treatment-resistant PTSD (TR-PTSD) under clinical trial NCT04152993. To determine electrophysiological signatures related to emotionally aversive and clinically relevant states (trial primary endpoint), we characterized neural activity during unpleasant po

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Segers, Joost, Saeid Hedayatrasa, Gaétan Poelman, Wim Van Paepegem, and Mathias Kersemans. "Broadband nonlinear elastic wave modulation spectroscopy for damage detection in composites." Structural Health Monitoring, April 5, 2021, 147592172110025. http://dx.doi.org/10.1177/14759217211002562.

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A non-destructive testing procedure is proposed for damage detection in composites using full wavefield measurement obtained using a scanning laser Doppler vibrometer. Vibrations are excited using two low-power piezoelectric actuators leading to nonlinear elastic wave modulation at the defect. One actuator is supplied with a broadband chirp signal and the other actuator is supplied with a single-frequency sine signal. First, a time–frequency filtering method is proposed to extract specific nonlinear components of interest (e.g. second higher harmonic and first modulation sideband) without the

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Höller, Yvonne, Sara Teresa Jónsdóttir, Anna Hjálmveig Hannesdóttir, and Ragnar Pétur Ólafsson. "EEG-responses to mood induction interact with seasonality and age." Frontiers in Psychiatry 13 (August 9, 2022). http://dx.doi.org/10.3389/fpsyt.2022.950328.

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The EEG is suggested as a potential diagnostic and prognostic biomarker for seasonal affective disorder (SAD). As a pre-clinical form of SAD, seasonality is operationalized as seasonal variation in mood, appetite, weight, sleep, energy, and socializing. Importantly, both EEG biomarkers and seasonality interact with age. Inducing sad mood to assess cognitive vulnerability was suggested to improve the predictive value of summer assessments for winter depression. However, no EEG studies have been conducted on induced sad mood in relation to seasonality, and no studies so far have controlled for a

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Hermans, Tim, Katherine Carkeek, Anneleen Dereymaeker, et al. "Partial wavelet coherence as a robust method for assessment of neurovascular coupling in neonates with hypoxic ischemic encephalopathy." Scientific Reports 13, no. 1 (2023). http://dx.doi.org/10.1038/s41598-022-27275-8.

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AbstractIn neonates with hypoxic ischemic encephalopathy, the computation of wavelet coherence between electroencephalogram (EEG) power and regional cerebral oxygen saturation (rSO2) is a promising method for the assessment of neurovascular coupling (NVC), which in turn is a promising marker for brain injury. However, instabilities in arterial oxygen saturation (SpO2) limit the robustness of previously proposed methods. Therefore, we propose the use of partial wavelet coherence, which can eliminate the influence of SpO2. Furthermore, we study the added value of the novel NVC biomarkers for ide

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Hermans, Tim, Katherine Carkeek, Anneleen Dereymaeker, et al. "Partial wavelet coherence as a robust method for assessment of neurovascular coupling in neonates with hypoxic ischemic encephalopathy." Scientific Reports 13, Article number: 457 (2023). https://doi.org/10.1038/s41598-022-27275-8.

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In neonates with hypoxic ischemic encephalopathy, the computation of wavelet coherence between electroencephalogram (EEG) power and regional cerebral oxygen saturation (rSO2) is a promising method for the assessment of neurovascular coupling (NVC), which in turn is a promising marker for brain injury. However, instabilities in arterial oxygen saturation (SpO2) limit the robustness of previously proposed methods. Therefore, we propose the use of partial wavelet coherence, which can eliminate the influence of SpO2. Furthermore, we study the added value of the novel NVC biomarkers for identificat

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Ferreira, Luan Oliveira, Esther Padilha da Silveira, Clarissa A. Paz, et al. "Decreasing brain activity caused by acute administration of ketamine and alcohol – A randomized, controlled, observer-blinded experimental study." Frontiers in Pharmacology 15 (October 16, 2024). http://dx.doi.org/10.3389/fphar.2024.1456009.

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IntroductionSubstance abuse is a major public health problem. In recent years, ketamine, which is a parenteral anesthetic, has been consumed increasingly as an illicit drug together with alcohol, although little is known of how this association alters brain activity. The present study investigated the influence of progressive doses of ketamine, associated with alcohol, on electrophysiological activity.MethodsFor this, 72 late-adolescent (8–10-week-old) male Wistar rats received either ketamine only, at low (10 mg/kg), intermediate (20 mg/kg) or high (30 mg/kg) doses via intraperitoneal injecti

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Reischke, Robert, Sandra Unruh, Marika Asgari, et al. "KiDS-Legacy: Covariance validation and the unified OneCovariance framework for projected large-scale structure observables." Astronomy & Astrophysics, May 13, 2025. https://doi.org/10.1051/0004-6361/202452592.

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We introduce OneCovariance an open-source software designed to accurately compute covariance matrices for an arbitrary set of two-point summary statistics across a variety of large-scale structure tracers. Utilising the halo model, we estimated the statistical properties of matter and biased tracer fields, incorporating all Gaussian, non-Gaussian, and super-sample covariance terms. The flexible configuration permits user-specific parameters, such as the complexity of survey geometry, the halo occupation distribution employed to define each galaxy sample, or the form of the real-space and/or Fo

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Jaramillo‐Jimenez, Alberto, Yorguin Jose Mantilla‐Ramos, Diego Tovar, et al. "Reproducible Python workflow for multi‐site resting‐state EEG analysis: From raw data to group classification." Alzheimer's & Dementia 19, S14 (2023). http://dx.doi.org/10.1002/alz.076353.

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AbstractBackgroundAmong promising markers for neurodegenerative disorders, electroencephalogram (EEG) represents a non‐invasive and potentially portable alternative. Standardized methods for preprocessing may contribute to less inter‐observer variability, while pooling multi‐centric EEG data may provide more generalizable results. We aim to formulate a reproducible pipeline for group‐level classification (of Parkinson’s Disease ‐PD‐) based on various features of resting‐state EEG recordings (rsEEG) collected in multi‐site studies.MethodsWe used four datasets (acquired in Colombia, Finland, and

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