Relevant bibliographies by topics / EEG/MEG data

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'EEG/MEG data'

Author: Grafiati
Published: 7 December 2024

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Journal articles on the topic "EEG/MEG data"

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Litvak, Vladimir, Jérémie Mattout, Stefan Kiebel, Christophe Phillips, Richard Henson, James Kilner, Gareth Barnes, et al. "EEG and MEG Data Analysis in SPM8." Computational Intelligence and Neuroscience 2011 (2011): 1–32. http://dx.doi.org/10.1155/2011/852961.

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SPM is a free and open source software written in MATLAB (The MathWorks, Inc.). In addition to standard M/EEG preprocessing, we presently offer three main analysis tools: (i) statistical analysis of scalp-maps, time-frequency images, and volumetric 3D source reconstruction images based on the general linear model, with correction for multiple comparisons using random field theory; (ii) Bayesian M/EEG source reconstruction, including support for group studies, simultaneous EEG and MEG, and fMRI priors; (iii) dynamic causal modelling (DCM), an approach combining neural modelling with data analysis for which there are several variants dealing with evoked responses, steady state responses (power spectra and cross-spectra), induced responses, and phase coupling. SPM8 is integrated with the FieldTrip toolbox , making it possible for users to combine a variety of standard analysis methods with new schemes implemented in SPM and build custom analysis tools using powerful graphical user interface (GUI) and batching tools.
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Gramfort, Alexandre, Martin Luessi, Eric Larson, Denis A. Engemann, Daniel Strohmeier, Christian Brodbeck, Lauri Parkkonen, and Matti S. Hämäläinen. "MNE software for processing MEG and EEG data." NeuroImage 86 (February 2014): 446–60. http://dx.doi.org/10.1016/j.neuroimage.2013.10.027.

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Hong, J., and S. C. Jun. "Single-trial Analysis for MEG/EEG spatiotemporal data." NeuroImage 47 (July 2009): S145. http://dx.doi.org/10.1016/s1053-8119(09)71470-3.

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Maris, Eric, and Robert Oostenveld. "Nonparametric statistical testing of EEG- and MEG-data." Journal of Neuroscience Methods 164, no. 1 (August 2007): 177–90. http://dx.doi.org/10.1016/j.jneumeth.2007.03.024.

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Gjini, Klevest, Susan M. Bowyer, Frank Wang, and Nash N. Boutros. "Deficit Versus Nondeficit Schizophrenia: An MEG-EEG Investigation of Resting State and Source Coherence—Preliminary Data." Clinical EEG and Neuroscience 51, no. 1 (August 4, 2019): 34–44. http://dx.doi.org/10.1177/1550059419867561.

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This study investigated the magneto- and electroencephalography (MEG and EEG, respectively) resting state to identify the deviations closely associated with the deficit syndrome (DS) in schizophrenia patients. Ten subjects in each group (control, DS, and nondeficit schizophrenia [NDS]) were included. Subjects underwent MEG-EEG recordings during a resting state condition. MEG coherence source imaging (CSI) in source space and spectral analysis in sensor space were performed. Significant differences were found between the 2 patient groups: (1) MEG and EEG spectral analysis showed significantly higher power at low frequencies (delta band) at sensor space in DS compared with NDS patients; (2) source analysis revealed larger power in the DS compared with NDS group at low frequencies in the frontal region; (3) NDS patients showed significantly higher MEG signal relative power in beta bands in sensor space compared with DS patients; (4) both DS and NDS patients showed higher EEG absolute power at higher beta band compared to controls; and (5) patients with DS were found to have a significantly higher MEG CSI than controls in the beta frequency band. These data support the observation of increased power in the low-frequency EEG/MEG rhythms associated with the DS. Increased power in the beta rhythms was more associated with the NDS.
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Zhang, Junpeng, Sarang S. Dalal, Srikantan S. Nagarajan, and Dezhong Yao. "COHERENT MEG/EEG SOURCE LOCALIZATION IN TRANSFORMED DATA SPACE." Biomedical Engineering: Applications, Basis and Communications 22, no. 05 (October 2010): 351–65. http://dx.doi.org/10.4015/s1016237210002110.

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In some cases, different brain regions give rise to strongly-coherent electrical neural activities. For example, pure tone evoked activations of the bilateral auditory cortices exhibit strong coherence. Conventional 2nd order statistics-based spatio-temporal algorithms, such as MUSIC (MUltiple SIgnal Classification) and beamforming encounter difficulties in localizing such activities. In this paper, we proposed a novel solution for this case. The key idea is to map the measurement data into a new data space through a transformation prior to the localization. The orthogonal complement of the lead field matrix for the region to be suppressed is generated as the transformation matrix. Using a priori knowledge or another independent imaging method, such as sLORETA (standard LOw REsolution brain electromagnetic TomogrAphy), the coherent source regions can be primarily identified. And then, in the transformed data space a conventional spatio-temporal method, such as MUSIC, can be used to accomplish the localization of the remaining coherent sources. Repeatedly applying the method will achieve localization of all the coherent sources. The algorithm was validated by simulation experiments as well as by the reconstructions of real bilateral auditory cortical coherent activities.
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Jas, Mainak, Denis A. Engemann, Yousra Bekhti, Federico Raimondo, and Alexandre Gramfort. "Autoreject: Automated artifact rejection for MEG and EEG data." NeuroImage 159 (October 2017): 417–29. http://dx.doi.org/10.1016/j.neuroimage.2017.06.030.

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Trujillo-Barreto, N. J., E. Martínez-Montes, P. A. Valdés-Sosa, and L. Melie-García. "Bayesian model for EEG/MEG and fMRI data fusion." NeuroImage 13, no. 6 (June 2001): 270. http://dx.doi.org/10.1016/s1053-8119(01)91613-1.

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Roś, Beata P., Fetsje Bijma, Mathisca C. M. de Gunst, and Jan C. de Munck. "A three domain covariance framework for EEG/MEG data." NeuroImage 119 (October 2015): 305–15. http://dx.doi.org/10.1016/j.neuroimage.2015.06.020.

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Kozinska, D., F. Carducci, and K. Nowinski. "Automatic alignment of EEG/MEG and MRI data sets." Clinical Neurophysiology 112, no. 8 (August 2001): 1553–61. http://dx.doi.org/10.1016/s1388-2457(01)00556-9.

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Dissertations / Theses on the topic "EEG/MEG data"

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Zaremba, Wojciech. "Modeling the variability of EEG/MEG data through statistical machine learning." Habilitation à diriger des recherches, Ecole Polytechnique X, 2012. http://tel.archives-ouvertes.fr/tel-00803958.

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Brain neural activity generates electrical discharges, which manifest as electrical and magnetic potentials around the scalp. Those potentials can be registered with magnetoencephalography (MEG) and electroencephalography (EEG) devices. Data acquired by M/EEG is extremely difficult to work with due to the inherent complexity of underlying brain processes and low signal-to-noise ratio (SNR). Machine learning techniques have to be employed in order to reveal the underlying structure of the signal and to understand the brain state. This thesis explores a diverse range of machine learning techniques which model the structure of M/EEG data in order to decode the mental state. It focuses on measuring a subject's variability and on modeling intrasubject variability. We propose to measure subject variability with a spectral clustering setup. Further, we extend this approach to a unified classification framework based on Laplacian regularized support vector machine (SVM). We solve the issue of intrasubject variability by employing a model with latent variables (based on a latent SVM). Latent variables describe transformations that map samples into a comparable state. We focus mainly on intrasubject experiments to model temporal misalignment.
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Molins, Jiménez Antonio. "Multimodal integration of EEG and MEG data using minimum ℓ₂-norm estimates." Thesis, Massachusetts Institute of Technology, 2007. http://hdl.handle.net/1721.1/40528.

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Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2007.
Includes bibliographical references (leaves 69-74).
The aim of this thesis was to study the effects of multimodal integration of electroencephalography (EEG) and magnetoencephalography (MEG) data on the minimum ℓ₂-norm estimates of cortical current densities. We investigated analytically the effect of including EEG recordings in MEG studies versus the addition of new MEG channels. To further confirm these results, clinical datasets comprising concurrent MEG/EEG acquisitions were analyzed. Minimum ℓ₂-norm estimates were computed using MEG alone, EEG alone, and the combination of the two modalities. Localization accuracy of responses to median-nerve stimulation was evaluated to study the utility of combining MEG and EEG.
by Antonio Molins Jiménez.
S.M.
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Papadopoulo, Théodore. "Contributions and perspectives to computer vision, image processing and EEG/MEG data analysis." Habilitation à diriger des recherches, Université Nice Sophia Antipolis, 2011. http://tel.archives-ouvertes.fr/tel-00847782.

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Dans une première partie, j'illustrerai quelques uns de mes travaux en visio n par ordinateur et traitement d'images. Ceux-ci portent notamment sur la géométrie multi-vues, l'utilisation du raisonnement géométrique pour intégrer des contraintes sur la scène, l'appariement et la segmentation d'images. Sans forcément rentrer dans les détails, j'exposerai les idées fondamentales qui sous-tendent ces travaux qui ont maintenant quelques années et proposerai quelques perspectives sur des extensions possibles. Une deuxième partie abordera certains problèmes liés à l'électro- et la magnéto-encéphalographie M/EEG, sujet auquel je me suis intéressé plus récemment. Je décrirai en particulier un algorithme de détection d'événements d'intérêts en essai par essai ainsi que certaines techniques que nous avons développé pour la modélisation du problème direct M/EEG. Comme pour la première partie, je tenterai de proposer quelques unes des évolutions possibles autour de cette thématique.
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Zavala, Fernandez Heriberto. "Evaluation and comparsion of the independent components of simultaneously measured MEG and EEG data /." Berlin : Univ.-Verl. der TU, 2009. http://www.ub.tu-berlin.de/index.php?id=2260#c9917.

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Ablin, Pierre. "Exploration of multivariate EEG /MEG signals using non-stationary models." Thesis, Université Paris-Saclay (ComUE), 2019. http://www.theses.fr/2019SACLT051.

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L'Analyse en Composantes Indépendantes (ACI) modèle un ensemble de signaux comme une combinaison linéaire de sources indépendantes. Cette méthode joue un rôle clé dans le traitement des signaux de magnétoencéphalographie (MEG) et électroencéphalographie (EEG). L'ACI de tels signaux permet d'isoler des sources de cerveau intéressantes, de les localiser, et de les séparer d'artefacts. L'ACI fait partie de la boite à outils de nombreux neuroscientifiques, et est utilisée dans de nombreux articles de recherche en neurosciences. Cependant, les algorithmes d'ACI les plus utilisés ont été développés dans les années 90. Ils sont souvent lents lorsqu'ils sont appliqués sur des données réelles, et sont limités au modèle d'ACI classique.L'objectif de cette thèse est de développer des algorithmes d'ACI utiles en pratique aux neuroscientifiques. Nous suivons deux axes. Le premier est celui de la vitesse : nous considérons le problème d'optimisation résolu par deux des algorithmes les plus utilisés par les praticiens: Infomax et FastICA. Nous développons une nouvelle technique se basant sur un préconditionnement par des approximations de la Hessienne de l'algorithm L-BFGS. L'algorithme qui en résulte, Picard, est conçu pour être appliqué sur données réelles, où l'hypothèse d’indépendance n'est jamais entièrement vraie. Sur des données de M/EEG, il converge plus vite que les implémentations `historiques'.Les méthodes incrémentales, qui traitent quelques échantillons à la fois au lieu du jeu de données complet, constituent une autre possibilité d’accélération de l'ACI. Ces méthodes connaissent une popularité grandissante grâce à leur faculté à bien passer à l'échelle sur de grands jeux de données. Nous proposons un algorithme incrémental pour l'ACI, qui possède une importante propriété de descente garantie. En conséquence, cet algorithme est simple d'utilisation, et n'a pas de paramètre critique et difficile à régler comme un taux d'apprentissage.En suivant un second axe, nous proposons de prendre en compte du bruit dans le modèle d'ACI. Le modèle resultant est notoirement difficile et long à estimer sous l'hypothèse standard de non-Gaussianité de l'ACI. Nous nous reposons donc sur une hypothèse de diversité spectrale, qui mène à un algorithme facile d'utilisation et utilisable en pratique, SMICA. La modélisation du bruit permet de nouvelles possibilités inenvisageables avec un modèle d'ACI classique, comme une estimation fine des source et l'utilisation de l'ACI comme une technique de réduction de dimension statistiquement bien posée. De nombreuses expériences sur données M/EEG démontrent l'utilité de cette nouvelle approche.Tous les algorithmes développés dans cette thèse sont disponibles en accès libre sur internet. L’algorithme Picard est inclus dans les librairies de traitement de données M/EEG les plus populaires en Python (MNE) et en Matlab (EEGlab)
Independent Component Analysis (ICA) models a set of signals as linear combinations of independent sources. This analysis method plays a key role in electroencephalography (EEG) and magnetoencephalography (MEG) signal processing. Applied on such signals, it allows to isolate interesting brain sources, locate them, and separate them from artifacts. ICA belongs to the toolbox of many neuroscientists, and is a part of the processing pipeline of many research articles. Yet, the most widely used algorithms date back to the 90's. They are often quite slow, and stick to the standard ICA model, without more advanced features.The goal of this thesis is to develop practical ICA algorithms to help neuroscientists. We follow two axes. The first one is that of speed. We consider the optimization problems solved by two of the most widely used ICA algorithms by practitioners: Infomax and FastICA. We develop a novel technique based on preconditioning the L-BFGS algorithm with Hessian approximation. The resulting algorithm, Picard, is tailored for real data applications, where the independence assumption is never entirely true. On M/EEG data, it converges faster than the `historical' implementations.Another possibility to accelerate ICA is to use incremental methods, which process a few samples at a time instead of the whole dataset. Such methods have gained huge interest in the last years due to their ability to scale well to very large datasets. We propose an incremental algorithm for ICA, with important descent guarantees. As a consequence, the proposed algorithm is simple to use and does not have a critical and hard to tune parameter like a learning rate.In a second axis, we propose to incorporate noise in the ICA model. Such a model is notoriously hard to fit under the standard non-Gaussian hypothesis of ICA, and would render estimation extremely long. Instead, we rely on a spectral diversity assumption, which leads to a practical algorithm, SMICA. The noise model opens the door to new possibilities, like finer estimation of the sources, and use of ICA as a statistically sound dimension reduction technique. Thorough experiments on M/EEG datasets demonstrate the usefulness of this approach.All algorithms developed in this thesis are open-sourced and available online. The Picard algorithm is included in the largest M/EEG processing Python library, MNE and Matlab library, EEGlab
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Abbasi, Omid [Verfasser], Georg [Gutachter] Schmitz, and Markus [Gutachter] Butz. "Retrieving neurophysiological information from strongly distorted EEG and MEG data / Omid Abbasi ; Gutachter: Georg Schmitz, Markus Butz." Bochum : Ruhr-Universität Bochum, 2017. http://d-nb.info/1140223119/34.

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Dubarry, Anne-Sophie. "Linking neurophysiological data to cognitive functions : methodological developments and applications." Thesis, Aix-Marseille, 2016. http://www.theses.fr/2016AIXM5017.

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Un des enjeux majeurs de la Psychologie Cognitive est de décrire les grandes fonctions mentales, notamment chez l’humain. Du point de vue neuroscientifique, il s’agit de modéliser l’activité cérébrale pour en extraire les éléments et mécanismes spatio-temporels susceptibles d’être mis en correspondance avec les opérations cognitives. Le travail de cette thèse a consisté à définir et mettre en œuvre des stratégies originales permettant de confronter les modèles cognitifs existants à des données issues d’enregistrements neurophysiologiques chez l’humain. Dans une première étude nous avons démontré que la distinction entre les organisations classiques de la dénomination de dessin sériel-parallèle, doit être adressée au niveau des essais uniques et non sur la moyenne des signaux. Nous avons conçu et mené l’analyse des signaux SEEG de 15 patients pour montrer que l’organisation temporelle de la dénomination de dessin n’est pas, au sens strict, parallèle. Dans une deuxième étude nous avons combiné trois techniques d’enregistrements : SEEG, EEG et MEG pour clarifier l’organisation spatiale des sources d’activité neuronales. Nous avons établi la faisabilité de l’enregistrement sur un patient qui exécute une tâche de perception visuelle. Au delà des corrélations entre les signaux moyens des trois techniques, cette analyse a révélé des corrélations au niveau des essais uniques. À travers deux approches expérimentales, cette thèse propose de nombreux développements méthodologiques et conceptuels originaux et pertinents. Ces contributions ouvrent de nouvelles perspectives à partir desquelles les signaux neurophysiologiques pourront informer les théories des Neurosciences Cognitives
A major issue in Cognitive Psychology is to describe human cognitive functions. From the Neuroscientific perceptive, measurements of brain activity are collected and processed in order to grasp, at their best resolution, the relevant spatio-temporal features of the signal that can be linked with cognitive operations. The work of this thesis consisted in designing and implementing strategies in order to overcome spatial and temporal limitations of signal processing procedures used to address cognitive issues. In a first study we demonstrated that the distinction between picture naming classical temporal organizations serial-parallel, should be addressed at the level of single trials and not on the averaged signals. We designed and conducted the analysis of SEEG signals from 5 patients to show that the temporal organization of picture naming involves a parallel processing architecture to a limited degree only. In a second study, we combined SEEG, EEG and MEG into a simultaneous trimodal recording session. A patient was presented with a visual stimulation paradigm while the three types of signals were simultaneously recorded. Averaged activities at the sensor level were shown to be consistent across the three techniques. More importantly a fine-grained coupling between the amplitudes of the three recording techniques is detected at the level of single evoked responses. This thesis proposes various relevant methodological and conceptual developments. It opens up several perspectives in which neurophysiological signals shall better inform Cognitive Neuroscientific theories
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Ewald, Arne Verfasser], Klaus-Robert [Akademischer Betreuer] [Müller, Andreas [Akademischer Betreuer] Daffertshofer, and Guido [Akademischer Betreuer] Nolte. "Novel multivariate data analysis techniques to determine functionally connected networks within the brain from EEG or MEG data / Arne Ewald. Gutachter: Klaus-Robert Müller ; Andreas Daffertshofer ; Guido Nolte." Berlin : Technische Universität Berlin, 2014. http://d-nb.info/1067387773/34.

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Ewald, Arne [Verfasser], Klaus-Robert [Akademischer Betreuer] Müller, Andreas [Akademischer Betreuer] Daffertshofer, and Guido [Akademischer Betreuer] Nolte. "Novel multivariate data analysis techniques to determine functionally connected networks within the brain from EEG or MEG data / Arne Ewald. Gutachter: Klaus-Robert Müller ; Andreas Daffertshofer ; Guido Nolte." Berlin : Technische Universität Berlin, 2014. http://d-nb.info/1067387773/34.

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Carrara, Igor. "Méthodes avancées de traitement des BCI-EEG pour améliorer la performance et la reproductibilité de la classification." Electronic Thesis or Diss., Université Côte d'Azur, 2024. http://www.theses.fr/2024COAZ4033.

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L'électroencéphalographie (EEG) mesure de manière non invasive l'activité électrique du cerveau par le biais de champs électromagnétiques générés par l'activité synchronisée de millions de neurones. Cela permet de collecter des données temporelles multivariées qui constituent une trace de l'activité électrique du cerveau mesurée au niveau du cuir chevelu. À tout instant, les mesures enregistrées par ces capteurs sont des combinaisons linéaires des activités électriques provenant d'un ensemble de sources sous-jacentes situées dans le cortex cérébral. Ces sources interagissent entre elles selon un modèle biophysique complexe qui reste mal compris. Dans certaines applications, telles que la planification chirurgicale, il est crucial de reconstruire avec précision ces sources électriques corticales, une tâche connue sous le nom de résolution du problème inverse de reconstruction de sources. Bien qu'intellectuellement satisfaisante et potentiellement plus précise, cette approche nécessite le développement et l'application d'un modèle spécifique au sujet, ce qui est à la fois coûteux et techniquement difficile à réaliser. Il est cependant souvent possible d'utiliser directement les mesures EEG au niveau des capteurs et d'en extraire des informations sur l'activité cérébrale. Cela réduit considérablement la complexité de l'analyse des données par rapport aux approches au niveau des sources. Ces mesures peuvent être utilisées pour une variété d'applications comme par exemple la surveillance des états cognitifs, le diagnostic des conditions neurologiques ou le développement d'interfaces cerveau-ordinateur (BCI). De fait, même sans avoir une compréhension complète des signaux cérébraux, il est possible de créer une communication directe entre le cerveau et un appareil externe à l'aide de la technologie BCI. Le travail décrit dans ce document est centré sur les interfaces cerveau-ordinateur basées sur l'EEG, qui ont plusieurs applications dans divers domaines médicaux, comme la réadaptation et la communication pour les personnes handicapées, ou dans des domaines non médicaux, notamment les jeux et la réalité virtuelle. La première contribution de cette thèse va dans ce sens, avec la proposition d'une méthode basée sur une matrice de covariance augmentée (ACM). Sur cette base, la méthode de covariance augmentée Block-Toeplitz (BT-ACM) représente une évolution notable, améliorant l'efficacité de calcul tout en conservant son efficacité et sa versatilité. Enfin, ce travail se poursuit avec la proposition d'un réseau de neurones artificiel Phase-SPDNet qui permet l'intégration de ces méthodologies dans une approche de Deep Learning et qui est particulièrement efficace même avec un nombre limité d'électrodes. Nous avons en outre proposé le cadre pseudo-on-line pour mieux caractériser l'efficacité des méthodes BCI et la plus grande étude de reproductibilité BCI basée sur l'EEG en utilisant le benchmark MOABB (Mother of all BCI Benchmarks). Cette recherche vise à promouvoir une plus grande reproductibilité et fiabilité des études BCI. En conclusion, nous relevons dans cette thèse deux défis majeurs dans le domaine des interfaces cerveau-ordinateur (BCI) basées sur l'EEG : l'amélioration des performances par le développement d'algorithmes avancés au niveau des capteurs et l'amélioration de la reproductibilité au sein de la communauté BCI
Electroencephalography (EEG) non-invasively measures the brain's electrical activity through electromagnetic fields generated by synchronized neuronal activity. This allows for the collection of multivariate time series data, capturing a trace of the brain electrical activity at the level of the scalp. At any given time instant, the measurements recorded by these sensors are linear combinations of the electrical activities from a set of underlying sources located in the cerebral cortex. These sources interact with one another according to a complex biophysical model, which remains poorly understood. In certain applications, such as surgical planning, it is crucial to accurately reconstruct these cortical electrical sources, a task known as solving the inverse problem of source reconstruction. While intellectually satisfying and potentially more precise, this approach requires the development and application of a subject-specific model, which is both expensive and technically demanding to achieve.However, it is often possible to directly use the EEG measurements at the level of the sensors and extract information about the brain activity. This significantly reduces the data analysis complexity compared to source-level approaches. These measurements can be used for a variety of applications, including monitoring cognitive states, diagnosing neurological conditions, and developing brain-computer interfaces (BCI). Actually, even though we do not have a complete understanding of brain signals, it is possible to generate direct communication between the brain and an external device using the BCI technology. This work is centered on EEG-based BCIs, which have several applications in various medical fields, like rehabilitation and communication for disabled individuals or in non-medical areas, including gaming and virtual reality.Despite its vast potential, BCI technology has not yet seen widespread use outside of laboratories. The primary objective of this PhD research is to try to address some of the current limitations of the BCI-EEG technology. Autoregressive models, even though they are not completely justified by biology, offer a versatile framework to effectively analyze EEG measurements. By leveraging these models, it is possible to create algorithms that combine nonlinear systems theory with the Riemannian-based approach to classify brain activity. The first contribution of this thesis is in this direction, with the creation of the Augmented Covariance Method (ACM). Building upon this foundation, the Block-Toeplitz Augmented Covariance Method (BT-ACM) represents a notable evolution, enhancing computational efficiency while maintaining its efficacy and versatility. Finally, the Phase-SPDNet work enables the integration of such methodologies into a Deep Learning approach that is particularly effective with a limited number of electrodes.Additionally, we proposed the creation of a pseudo online framework to better characterize the efficacy of BCI methods and the largest EEG-based BCI reproducibility study using the Mother of all BCI Benchmarks (MOABB) framework. This research seeks to promote greater reproducibility and trustworthiness in BCI studies.In conclusion, we address two critical challenges in the field of EEG-based brain-computer interfaces (BCIs): enhancing performance through advanced algorithmic development at the sensor level and improving reproducibility within the BCI community
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Books on the topic "EEG/MEG data"

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Giebels, Ludy. Jacob Israël de Haan in het Palestijnse labyrint, 1919-1924. Nieuwe Prinsengracht 89 1018 VR Amsterdam Nederland: Amsterdam University Press, 2024. http://dx.doi.org/10.5117/9789048563838.

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Jacob Israël de Haan was romanschrijver, dichter en jurist. In Nederland was hij bekend als schrijver van Pijpelijntjes (1904), een roman waarin openlijk homoseksualiteit werd beschreven. Hij bekeerde zich tot het zionisme en emigreerde in 1919 naar Palestina als correspondent voor het Algemeen Handelsblad. Zijn feuilletons schetsen een levendig beeld van de politieke situatie en het leven van alledag in het nieuwe Joods Nationaal Tehuis, dat zich dankzij de Balfour Declaratie in Palestina ontwikkelde. In Jeruzalem sloot hij zich echter al snel aan bij de anti- zionistische ultraorthodoxe gemeenschap van rabbijn Chaim Sonnenfeld en bij de Agoedat Israël, de internationale organisatie van orthodoxe Joden. Hij werd hun juridisch adviseur en politiek woordvoerder in hun conflict met de zionistische organisatie. De Haan leverde onder andere kritiek op deze organisatie omdat zij bij de verwezenlijking van het Joods Nationaal Tehuis te weinig rekening hield met de Palestijnse Arabieren, die negentig procent van de bevolking uitmaakten. In juni 1924 werd hij door een zionistische commando vermoord.
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Hari, MD, PhD, Riitta, and Aina Puce, PhD. MEG-EEG Primer. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780190497774.001.0001.

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This book provides newcomers and more experienced researchers with the very basics of magnetoencephalography (MEG) and electroencephalography (EEG)—two noninvasive methods that can inform about the neurodynamics of the human brain on a millisecond scale. These two closely related methods are addressed side by side, starting from their physical and physiological bases and then advancing to methods of data acquisition, analysis, visualization, and interpretation. Special attention is paid to careful experimentation, guiding the readers to differentiate brain signals from various biological and non-biological artifacts and to ascertain that the collected data are reliable. The strengths and weaknesses of MEG and EEG are presented relative to each other and to other available brain-imaging methods. Necessary instrumentation and laboratory set-ups, as well as potential pitfalls in data collection and analysis are discussed. Spontaneous brain rhythms and evoked responses to sensory and multisensory stimulation are covered and examined both in healthy individuals and in various brain disorders, such as epilepsy. MEG/EEG signals related to motor, cognitive, and social events are discussed as well. The integration of MEG and EEG information with other methods to assess human brain function is discussed with respect to the current state-of-the art in the field. The book ends with a look to future developments in equipment design, and experimentation, emphasizing the role of accurate temporal information for human brain function.
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Hamalainen, Matti, Risto Ilmoniemi, and Lauri Parkkonen. Fundamentals of MEG and EEG: Biophysics, Instrumentation, and Data Analysis. Elsevier Science & Technology Books, 2020.

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Butkov, Nic. Polysomnography. Edited by Sudhansu Chokroverty, Luigi Ferini-Strambi, and Christopher Kennard. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780199682003.003.0007.

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This chapter provides an overview of the sleep recording process, including the application of electrodes and sensors to the patient, instrumentation, signal processing, digital polysomnography (PSG), and artifact recognition. Topics discussed include indications for PSG, standard recording parameters, patient preparation, electrode placement for recording the electroencephalogram (EEG), electrooculogram (EOG), electromyogram (EMG), and electrocardiogram (ECG), the use of respiratory transducers, oximetry, signal processing, filters, digital data display, electrical safety, and patient monitoring. This chapter also includes record samples of the various types of recording artifacts commonly found in sleep studies, with a detailed description of their causes, preventative measures, and recommended corrective actions.
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Kam, Julia W. Y., and Todd C. Handy. Electroencephalogram Recording in Humans. Oxford University Press, 2015. http://dx.doi.org/10.1093/med/9780199939800.003.0006.

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This chapter provides an elementary introduction to the theory and practical application of electroencephalogram (EEG) recording for the purpose of studying neurocognitive processes. It is aimed at readers who have had little or no experience in EEG data collection, and would like to gain a better understanding of scientific papers employing this methodology or start their own EEG experiment. We begin with a definition of EEG, and a summary of the strengths and limitations of EEG-based techniques. Following this is a description of the basic theory concerning the cellular mechanisms underlying EEG, as well as two types of data generated by EEG recording. We then present a brief summary of the equipment necessary for EEG data acquisition and important considerations for presentation software. Finally, we provide an overview of the protocol for data acquisition and processing, as well as methods for quantifying both EEG and event-related potentials data.
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Sutter, Raoul, Peter W. Kaplan, and Donald L. Schomer. Historical Aspects of Electroencephalography. Edited by Donald L. Schomer and Fernando H. Lopes da Silva. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780190228484.003.0001.

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Electroencephalography (EEG), a dynamic real-time recording of electrical neocortical brain activity, began in the 1600s with the discovery of electrical phenomena and the concept of an “action current.” The galvanometer was introduced in the 1800s and the first bioelectrical observations of human brain signals were made in the 1900s. Certain EEG patterns were associated with brain disorders, increasing the clinical and scientific use of EEG. In the 1980s, technical advances allowed EEGs to be digitized and linked with videotape recording. In the 1990s, digital data storage increased and computer networking enabled remote real-time EEG reading, which made possible continuous EEG (cEEG) monitoring. Manual cEEG analysis became increasingly labor-intensive, calling for methods to assist this process. In the 2000s, complex algorithms enabling quantitative EEG analyses were introduced, with a new focus on shared activity between rhythms, including phase and magnitude synchrony. The automation of spectral analysis enabled studies of spectral content.
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Thomas, James, and Tanya Monaghan. Clinical data interpretation. Oxford University Press, 2018. http://dx.doi.org/10.1093/med/9780199593972.003.0019.

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Vanhatalo, Sampsa, and J. Matias Palva. Infraslow EEG Activity. Edited by Donald L. Schomer and Fernando H. Lopes da Silva. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780190228484.003.0032.

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Infraslow electroencephalographic (EEG) activity refers to frequencies below the conventional clinical EEG range that starts at about 0.5 Hz. Evidence suggests that salient EEG signals in the infraslow range are essential parts of many physiological and pathological conditions. In addition, brain is known to exhibit multitude of infraslow processes, which may be observed directly as fluctuations in the EEG signal amplitude, as infraslow fluctuations or intermittency in other neurophysiological signals, or as fluctuations in behavioural performance. Both physiological and pathological EEG activity may range from 0.01 Hz to several hundred Hz. In the clinical context, infraslow activity is commonly observed in the neonatal EEG, during and prior to epileptic seizures, and during sleep and arousals. Laboratory studies have demonstrated the presence of spontaneous infraslow EEG fluctuations or very slow event-related potentials in awake and sleeping subjects. Infraslow activity may not only arise in cortical and subcortical networks but is also likely to involve non-neuronal generators such as glial networks. The full, physiologically relevant range of brain mechanisms can be readily recorded with wide dynamic range direct-current (DC)-coupled amplifiers or full-band EEG (FbEEG). Due to the different underlying mechanisms, a single FbEEG recording can even be perceived as a multimodal recording where distinct brain modalities can be studied simultaneously by performing data analysis for different frequency ranges. FbEEG is likely to become the standard approach for a wide range of applications in both basic science and in the clinic.
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Osman, Gamaleldin M., James J. Riviello, and Lawrence J. Hirsch. EEG in the Intensive Care Unit. Edited by Donald L. Schomer and Fernando H. Lopes da Silva. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780190228484.003.0022.

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The field of continuous electroencephalographic monitoring (cEEG) in the intensive care unit has dramatically expanded over the past two decades. Expansion of cEEG programs led to recognition of the frequent occurrence of electrographic seizures, and complex rhythmic and periodic patterns in various critically ill populations. The majority of electrographic seizures are of nonconvulsive nature, hence the need for cEEG for their identification. Guidelines on when and how to perform cEEG and standardized nomenclature for description of rhythmic and periodic patterns are now available. Quantitative EEG analysis methods depict EEG data in a compressed (hours on one screen) colorful graphical representation, facilitating early identification of key events, recognition of slow, long-term trends, and timely therapeutic intervention. Integration of EEG with other invasive and noninvasive modalities of monitoring brain function provides critical information about the development of secondary neuronal injury, providing a valuable window of opportunity for intervention before irreversible damage ensues.
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Vespa, Paul M. Electroencephalogram monitoring in the critically ill. Oxford University Press, 2016. http://dx.doi.org/10.1093/med/9780199600830.003.0221.

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Electroencephalography monitoring provides a method for monitoring brain function, which can complement other forms of monitoring, such as monitoring of intracranial pressure and derived parameters, such as cerebral perfusion pressure. Continuous electroencephalogram (EEG) monitoring can be helpful in seizure detection after brain injury and coma. Seizures can be detected by visual inspection of the raw EEG and/or processed EEG data. Treatment of status epilepticus can be improved by rapid identification and abolition of seizures using continuous EEG. Quantitative EEG can also be used to detect brain ischaemia and seizures, to monitor sedation and aid prognosis.
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Book chapters on the topic "EEG/MEG data"

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Iversen, John R., and Scott Makeig. "MEG/EEG Data Analysis Using EEGLAB." In Magnetoencephalography, 1–16. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-319-62657-4_8-1.

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Iversen, John R., and Scott Makeig. "MEG/EEG Data Analysis Using EEGLAB." In Magnetoencephalography, 199–212. Berlin, Heidelberg: Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-642-33045-2_8.

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Iversen, John R., and Scott Makeig. "MEG/EEG Data Analysis Using EEGLAB." In Magnetoencephalography, 391–406. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-00087-5_8.

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Sorrentino, Alberto, and Michele Piana. "Inverse Modeling for MEG/EEG Data." In Mathematical and Theoretical Neuroscience, 239–53. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-68297-6_15.

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Sellers, Kristin K., Joline M. Fan, Leighton B. N. Hinkley, and Heidi E. Kirsch. "Preprocessing Electrophysiological Data: EEG, iEEG, and MEG Data." In Statistical Methods in Epilepsy, 25–50. Boca Raton: Chapman and Hall/CRC, 2024. http://dx.doi.org/10.1201/9781003254515-2.

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Pflieger, M. E., G. V. Simpson, S. P. Ahlfors, and R. J. Ilmoniemi. "Superadditive Information from Simultaneous MEG/EEG Data." In Biomag 96, 1154–57. New York, NY: Springer New York, 2000. http://dx.doi.org/10.1007/978-1-4612-1260-7_282.

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Pitolli, Francesca. "Neuroelectric Current Localization from Combined EEG/MEG Data." In Curves and Surfaces, 562–74. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-27413-8_37.

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Louis, Alfred K., Uwe Schmitt, Felix Darvas, Helmut Büchner, and Manfred Fuchs. "Spatio-Temporal Current Density Reconstruction from EEG-/MEG-Data." In Mathematics — Key Technology for the Future, 472–82. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-642-55753-8_38.

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Esch, Lorenz, Christoph Dinh, Eric Larson, Denis Engemann, Mainak Jas, Sheraz Khan, Alexandre Gramfort, and Matti S. Hämäläinen. "MNE: Software for Acquiring, Processing,and Visualizing MEG/EEG Data." In Magnetoencephalography, 1–17. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-319-62657-4_59-1.

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Esch, Lorenz, Christoph Dinh, Eric Larson, Denis Engemann, Mainak Jas, Sheraz Khan, Alexandre Gramfort, and M. S. Hämäläinen. "MNE: Software for Acquiring, Processing, and Visualizing MEG/EEG Data." In Magnetoencephalography, 355–71. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-00087-5_59.

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Conference papers on the topic "EEG/MEG data"

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Vlasenko, Daniil, Alexey Zaikin, and Denis Zakharov. "Ensemble methods for representation of fMRI, EEG/MEG data in graph form for classification of brain states." In 2024 8th Scientific School Dynamics of Complex Networks and their Applications (DCNA), 258–61. IEEE, 2024. http://dx.doi.org/10.1109/dcna63495.2024.10718443.

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Cai, Chang, Mithun Diwakar, Kensuke Sekihara, and Srikantan S. Nagarajan. "Robust Bayesian algorithm for distributed source reconstructions MEG/EEG data." In 2019 9th International IEEE/EMBS Conference on Neural Engineering (NER). IEEE, 2019. http://dx.doi.org/10.1109/ner.2019.8716990.

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Gramfort, Alexandre, and Maureen Clerc. "Low Dimensional Representations of MEG/EEG Data Using Laplacian Eigenmaps." In 2007 Joint Meeting of the 6th International Symposium on Noninvasive Functional Source Imaging of the Brain and Heart and the International Conference on Functional Biomedical Imaging. IEEE, 2007. http://dx.doi.org/10.1109/nfsi-icfbi.2007.4387717.

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NOLTE, G. "DETECTING AND LOCALIZING TRUE BRAIN INTERACTIONS FROM EEG/MEG DATA." In Proceedings of the Seventh International Workshop. WORLD SCIENTIFIC, 2006. http://dx.doi.org/10.1142/9789812773197_0036.

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Ardila Franco, Camilo Ernesto, Jose David Lopez Hincapie, and Jairo Jose Espinosa. "Neural activity reconstruction with MEG/EEG data considering noise regularization." In 2012 XVII Symposium of Image, Signal Processing, and Artificial Vision (STSIVA). IEEE, 2012. http://dx.doi.org/10.1109/stsiva.2012.6340551.

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Huang, Gan, and Zhiguo Zhang. "Improving sensitivity of cluster-based permutation test for EEG/MEG data." In 2017 8th International IEEE/EMBS Conference on Neural Engineering (NER). IEEE, 2017. http://dx.doi.org/10.1109/ner.2017.8008279.

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Lopez, Jose David, Angela Sucerquia, and Gareth Barnes. "Simultaneous estimation of brain structure and function with MEG/EEG data." In 2017 International Conference on Innovations in Electrical Engineering and Computational Technologies (ICIEECT). IEEE, 2017. http://dx.doi.org/10.1109/icieect.2017.7916582.

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Belaoucha, Brahim, and Theodore Papadopoulo. "Large brain effective network from EEG/MEG data and dMR information." In 2017 International Workshop on Pattern Recognition in Neuroimaging (PRNI). IEEE, 2017. http://dx.doi.org/10.1109/prni.2017.7981511.

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Gutiérrez, David, Gerardo Herrera Corral, and Luis Manuel Montaño Zentina. "Using EEG∕MEG Data of Cognitive Processes in Brain-Computer Interfaces." In MEDICAL PHYSICS: Tenth Mexican Symposium on Medical Physics. AIP, 2008. http://dx.doi.org/10.1063/1.2979300.

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Jia, Wenyan, Robert J. Sclabassi, Lin-Sen Pon, Mark L. Scheuer, and Mingui Sun. "Spike Separation from EEG/MEG Data Using Morphological Filter and Wavelet Transform." In Conference Proceedings. Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE, 2006. http://dx.doi.org/10.1109/iembs.2006.259695.

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Reports on the topic "EEG/MEG data"

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Mosher, J. C., M. Huang, R. M. Leahy, and M. E. Spencer. Modeling versus accuracy in EEG and MEG data. Office of Scientific and Technical Information (OSTI), July 1997. http://dx.doi.org/10.2172/554813.

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Van der Maas, Peter, Jesse Wagenaar, Ilse Ubels, and Anne Helbig. Circulair water in de wijk : verkennend onderzoek naar de potenties en belemmering van decentrale systemen voor behandeling en hergebruik van afvalwater en regenwater in woonwijken. Hogeschool van Hall Larenstein, June 2024. http://dx.doi.org/10.31715/2024.6.

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In Nederland en omringende landen zijn in de afgelopen jaren en decennia verschillende projecten gerealiseerd rond decentrale innovatieve concepten voor afvalwaterbehandeling en gebruik van re-genwater. Om gemeenten, waterschappen en andere belanghebbenden een rationele grondslag te bieden voor keuzes m.b.t. de inrichting van de stedelijke waterketen (wel of niet decentraal, wel of niet brongescheiden), is in dit KIEM project de potentie en beperkingen onderzocht van nieuwe en circulaire sanitatieconcepten, zoals brongescheiden sanitatie en lokaal (her)gebruik van regenwater op woonwijk schaal. De vraag is wat we kunnen leren van ervaringen bij gerealiseerde projecten, en welke rationele basis er is om, met name bij nieuwbouwplannen, een trendbreuk teweeg te brengen in de richting decentrale oplossingen voor waterzuivering en waterhergebruik op wijkniveau, als al-ternatief voor de huidige, centrale systemen. Daartoe zijn negen verschillende gerealiseerde pro-jecten, operationeel op praktijkschaal, verkend aan de hand van literatuurstudie, data-analyse, inter-views, enquêtes en scenarioberekeningen. Verschillende prestatie-indicatoren, o.a. met betrekking tot terugwinning van grondstoffen, waterkwaliteit, hergebruik en kosten zijn inzichtelijk gemaakt. Bo-vendien is onderzoek gedaan naar de acceptatie van burgers m.b.t. governance structuren (top-down versus bottom-up) als het gaat om de stedelijke waterketen en diensten m.b.t. waterlevering en wa-terbehandeling.Uit dit verkennende onderzoek is gebleken dat alternatieve systemen (brongescheiden sanitatie met vacuümriolering en lokaal gebruik van regenwater) voor toiletspoeling, evt. tuin en wasmachine tot substantieel minder gebruik van drinkwater leiden. Bovendien wordt met separate inzameling en be-handeling van zwart- en grijswater de terugwinning van nutriënten (N, P, C) gestimuleerd en is er bij decentrale behandeling van grijswater jaarrond aanvoer van schoon water wat met name in droge periodes meerwaarde heeft. Daarentegen leiden systemen op wijkschaal, mede vanwege de relatief kleine schaal, tot relatief hoge financiële kosten, d.w.z. in vergelijking met de kosten voor aanleg en beheer van reguliere systemen. Daarbij wordt benadrukt dat vergelijking van kleine, decentrale sys-temen met de huidige, grootschalige centrale (afval)watersystemen lastig is vanwege de relatief ge-ringe hoeveelheid data die beschikbaar is m.b.t. prestatie-indicatoren van decentrale systemen. We kunnen daarom slechts voorlopige en minder harde uitspraken doen over een aantal prestaties van decentrale concepten, bijv. m.b.t. waterkwaliteit. Bovendien is de beoordeling van prestatie-indicato-ren problematisch vanwege ongelijksoortigheid. De huidige grootschalige systemen zijn goeddeels uit-ontwikkeld (innovatie was gericht op kostenefficiency), terwijl decentrale, nieuwe vormen van sani-tatie nog volop in ontwikkeling zijn, met duurzaamheid als drijfveer.Aandachtspunten en vragen liggen met name op het gebied van governance. In de huidige inrichting en organisatie van de waterketen zijn de verantwoordelijkheden, beleidsontwikkeling en operatie in-stitutioneel geborgd en sectoraal verdeeld (waterbedrijf, gemeente en waterschap). Nieuwe vormen van sanitatie en gebruik van regenwater op wijkschaal brengen de noodzaak tot vergaande samen-werking en nieuwe vraagstukken met zich mee.Om de prestaties van grootschalige, centrale systemen m.b.t. afvalwaterbehandeling en watervoor-ziening beter te kunnen vergelijken met decentrale systemen op wijkschaal wordt aanbevolen om gelijktijdig te innoveren op beide schalen, waarbij de innovatie (ook op grote, centrale schaal) gericht is op klimaatadaptatie en aansluiting bij de circulaire economie. Belangrijk daarbij is langjarige data-verzameling en monitoring, zodat de integrale prestaties van concepten en systemen kunnen worden gevolgd, beoordeeld en verbeterd, in de context van integrale duurzaamheid. Daarnaast wordt aan-bevolen om, indien mogelijk, decentrale (afvalwater)systemen op wijkniveau op te schalen naar een grootte van minimaal 3.000 inwoners, om het (op berekeningen gebaseerde) veronderstelde break-evenpoint (kosten decentraal vergelijkbaar met grootschalige, centrale systemen) in de praktijk te ve-rifiëren. Gerealiseerde projecten, bijv. Reitdiep in Groningen of Waterschoon in Sneek, kunnen wor-den benut voor verdere innovatie gericht op kringloopsluiting en circulaire economie.
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Agrawal, Asha Weinstein, Evelyn Blumenberg, Anastasia Loukaitou-Sideris, and Brittney Lu. Understanding Workforce Diversity in the Transit Industry: Establishing a Baseline of Diversity Demographics. Mineta Transportation Institute, February 2024. http://dx.doi.org/10.31979/mti.2024.2213.

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This study provides baseline data on the status of the racial/ethnic and gender diversity of the transit agency workforce in the U.S. and identifies potential barriers and promising practices for diversifying this workforce. Public transit agencies function best when the diversity of their workforce represents the communities they serve, yet previous research finds an underrepresentation of women and minorities in senior and managerial roles, along with an overconcentration of men and workers of color—particularly Black workers—in operational roles (e.g., drivers, janitors). The study updates those earlier studies with newer data drawn from five discrete research tasks: 1) review of the scholarly and professional literature on the topic; 2) review of the websites of the 50 largest transit operators; 3) analysis of employee demographic data submitted by 152 transit operators as part of Equal Employment Opportunity (EEO) program plans; 4) analysis of responses to an original survey sent to the human resources personnel of transit agencies (92 responses from staff at 68 agencies), and 5) interviews with 12 professionals selected for their expertise in transportation workforce diversity monitoring, management, and/or advocacy.
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Guérin, Laurence, Patrick Sins, Lida Klaver, and Juliette Walma van der Molen. Onderzoeksrapport Samen werken aan Bèta Burgerschap. Saxion, 2021. http://dx.doi.org/10.14261/ff0c6282-93e2-41a7-b60ab9bceb2a4328.

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In het TechYourfuture project ‘Samen werken aan Bèta Burgerschap’, dat plaats vond in de periode maart 2015 - maart 2020, gaven de onderzoekers samen met scholen en bedrijven concreet invulling aan burgerschapsonderwijs. De maatschappij en maatschappelijke vraagstukken worden steeds complexer. Politieke, technologische, economische, sociaal-culturele of ecologische aspecten van een vraagstuk zijn met elkaar verweven. Daarnaast spelen ook globale en lokale dimensies een rol. Er zijn alleen hierdoor al meerdere antwoorden mogelijk op een vraagstuk. Gedurende het project hebben basisschoolleerlingen (wereldwijde) maatschappelijk-technologische vraagstukken geanalyseerd, bediscussieerd en daar oplossingen voor bedacht. Leraren hebben in het project geleerd bèta burgerschap activiteiten te ontwikkelen, uit te voeren en te evalueren. In de kern gaat het er in Bèta Burgerschap om dat leerlingen door groepsgewijs vraagstukken op te lossen burgerschapscompetenties ontwikkelen. Het gaat hier om drie hoofdcompetenties: (1.) Collectieve argumentatievaardigheden, (2.) Attituden ten opzichte van maatschappelijk technologische vraagstukken en, (3.) Bèta- en techniekkennis. In het onderzoek ‘Samen werken aan Bèta Burgerschap’ is gekeken naar de ontwikkeling van deze drie hoofdcompetenties bij leerlingen die deelnamen aan Bèta Burgerschap activiteiten, alsook naar de effecten van de training en video-coaching die de leerkrachten in het project gevolgd hebben. De resultaten hiervan zijn in het onderzoeksrapport te lezen. Het onderzoek laat zien dat Bèta Burgerschap een aanpak is die leerlingen mogelijkheden biedt om te oefenen met groepsgewijs probleem oplossen als burgerschapscompetentie. Door op school met maatschappelijk-technologische vraagstukken aan de slag te gaan, doen leerlingen meer kennis op over deze vraagstukken en worden zij zich meer bewust van wat er in de wereld speelt en van hoe zij zich verhouden tot deze vraagstukken. Om met Bèta Burgerschap aan de slag te gaan en het netwerk denken en de discussie doeltreffend te begeleiden, blijkt het professionaliseringstraject van toegevoegde waarde te zijn.
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Blankestijn, Wouter, Walter Verspui, Jan Fliervoet, and Loes Witteveen. Motivaties rondom Tuinvergroening onderzoek Tuinverhalen : resultaten enquête onder deelnemers van het project Pientere Tuinen. Lectoraat Communicatie, Participatie & Sociaal-Ecologisch Leren (CoPSEL), January 2024. http://dx.doi.org/10.31715/2024.3.

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In dit rapport worden de resultaten gepresenteerd van de enquête die in december 2023 is uitgezet bij de deelnemers van Pientere Tuinen, een participatief en burgerwetenschappelijk project met als doel meer Nederlandse tuinen te vergroenen. Deze enquête is onderdeel van het onderzoek Tuinverhalen dat in samenwerking met Pientere Tuinen wordt uitgevoerd door het lectoraat Communicatie, Participatie en Sociaal-Ecologisch Leren (CoPSEL) bij Hogeschool Van Hall Larenstein. Binnen dit onderzoek worden de motivaties en belemmeringen die tuineigenaren ervaren om veranderingen op het gebied van vergroening centraal gesteld. Deze enquête fungeert als eerste stap (nulmeting) voordat er dieper op deze motivaties en belemmeringen wordt ingegaan door middel van kwalitatieve interviews met zowel deelnemers van Pientere Tuinen als bij case studies zoals in Almere Regenboogbuurt. Daarbij is het relevant om te weten hoe een participatief en burgerwetenschappelijk project als Pientere Tuinen invloed heeft op kennis en eventuele vaardigheden rondom vergroening.
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Strietman, W. J., M. J. van den Heuvel-Greve, A. M. van den Brink, G. A. de Groot, M. Skirtun, E. L. Bravo Rebolledo, and K. J. Koffeman. Resultaten bronanalyse zwerfafval Griend : Resultaten van een gedetailleerde bronanalyse van zwerfafval dat op het Waddeneiland Griend verzameld is en samen met lokale stakeholders tijdens een Litter-ID-sessie in oktober 2019 onderzocht is. Wageningen: Wageningen Economic Research, 2020. http://dx.doi.org/10.18174/528599.

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Nelson, Gena. A Systematic Review of the Quality of Reporting in Mathematics Meta-Analyses for Students with or at Risk of Disabilities Coding Protocol. Boise State University, July 2021. http://dx.doi.org/10.18122/sped138.boisestate.

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The purpose of this document is to provide readers with the coding protocol that authors used to code 22 meta-analyses focused on mathematics interventions for students with or at-risk of disabilities. The purpose of the systematic review was to evaluate reporting quality in meta-analyses focused on mathematics interventions for students with or at risk of disabilities. To identify meta-analyses for inclusion, we considered peer-reviewed literature published between 2000 and 2020; we searched five education-focused electronic databases, scanned the table of contents of six special education journals, reviewed the curriculum vitae of researchers who frequently publish meta-analyses in mathematics and special education, and scanned the reference lists of meta-analyses that met inclusion criteria. To be included in this systematic review, meta-analyses must have reported on the effectiveness of mathematics-focused interventions, provided a summary effect for a mathematics outcome variable, and included school-aged participants with or at risk of having a disability. We identified 22 meta-analyses for inclusion. We coded each meta-analysis for 53 quality indicators (QIs) across eight categories based on recommendations from Talbott et al. (2018). Overall, the meta-analyses met 61% of QIs and results indicated that meta-analyses most frequently met QIs related to providing a clear purpose (95%) and data analysis plan (77%), whereas meta-analyses typically met fewer QIs related to describing participants (39%) and explaining the abstract screening process (48%). We discuss the variation in QI scores within and across the quality categories and provide recommendations for future researchers so that reporting in meta-analyses may be enhanced. Limitations of the current study are that grey literature was not considered for inclusion and that only meta-analyses were included; this limits the generalizability of the results to other research syntheses (e.g., narrative reviews, systematic reviews) and publication types (e.g., dissertations).
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8

Nelson, Gena. A Systematic Review of the Quality of Reporting in Mathematics Meta-Analyses for Students with or at Risk of Disabilities Coding Protocol. Boise State University, Albertsons Library, July 2021. http://dx.doi.org/10.18122/sped.138.boisestate.

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Abstract:
The purpose of this document is to provide readers with the coding protocol that authors used to code 22 meta-analyses focused on mathematics interventions for students with or at-risk of disabilities. The purpose of the systematic review was to evaluate reporting quality in meta-analyses focused on mathematics interventions for students with or at risk of disabilities. To identify meta-analyses for inclusion, we considered peer-reviewed literature published between 2000 and 2020; we searched five education-focused electronic databases, scanned the table of contents of six special education journals, reviewed the curriculum vitae of researchers who frequently publish meta-analyses in mathematics and special education, and scanned the reference lists of meta-analyses that met inclusion criteria. To be included in this systematic review, meta-analyses must have reported on the effectiveness of mathematics-focused interventions, provided a summary effect for a mathematics outcome variable, and included school-aged participants with or at risk of having a disability. We identified 22 meta-analyses for inclusion. We coded each meta-analysis for 53 quality indicators (QIs) across eight categories based on recommendations from Talbott et al. (2018). Overall, the meta-analyses met 61% of QIs and results indicated that meta-analyses most frequently met QIs related to providing a clear purpose (95%) and data analysis plan (77%), whereas meta-analyses typically met fewer QIs related to describing participants (39%) and explaining the abstract screening process (48%). We discuss the variation in QI scores within and across the quality categories and provide recommendations for future researchers so that reporting in meta-analyses may be enhanced. Limitations of the current study are that grey literature was not considered for inclusion and that only meta-analyses were included; this limits the generalizability of the results to other research syntheses (e.g., narrative reviews, systematic reviews) and publication types (e.g., dissertations).
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Rupke, Andrew, and Taylor Boden. Lithium Brine Analytical Database of Utah: Second Edition. Utah Geological Survey, November 2023. http://dx.doi.org/10.34191/ofr-758.

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The accompanying database is a compilation of analytical data from brine or water samples in Utah that includes lithium concentrations. The data were collected from published and unpublished sources. This second edition is an update that includes data from the first edition (Rupke and Boden, 2020) and adds additional records. We also added some minor detail to previous records, particularly in relation to how units (e.g., ppm or mg/L) were recorded in source documents. Our intent is to continue to add additional analyses to this database as time allows and more data are discovered or become available. Users of this database should be aware that the quality of the analyses from source to source is likely to be variable and the data are presented “as is”; potential low-quality data were not removed. The database is in spreadsheet (Microsoft Excel and csv) and geodatabase formats.
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Sloan, Steven, Shelby Peterie, Richard Miller, Julian Ivanov, J. Schwenk, and Jason McKenna. Detecting clandestine tunnels by using near-surface seismic techniques. Engineer Research and Development Center (U.S.), April 2021. http://dx.doi.org/10.21079/11681/40419.

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Geophysical detection of clandestine tunnels is a complex problem that has been met with limited success. Multiple methods have been applied spanning several decades, but a reliable solution has yet to be found. This report presents shallow seismic data collected at a tunnel test site representative of geologic settings found along the southwestern U.S. border. Results demonstrate the capability of using compressional wave diffraction and surface-wave backscatter techniques to detect a purpose-built subterranean tunnel. Near-surface seismic data were also collected at multiple sites in Afghanistan to detect and locate subsurface anomalies (e.g., data collected over an escape tunnel discovered in 2011 at the Sarposa Prison in Kandahar, Afghanistan, which allowed more than 480 prisoners to escape, and data from another shallow tunnel recently discovered at an undisclosed location). The final example from Afghanistan is the first time surface-based seismic methods have detected a tunnel whose presence and location were not previously known. Seismic results directly led to the discovery of the tunnel. Interpreted tunnel locations for all examples were less than 2 m of the actual location. Seismic surface wave backscatter and body-wave diffraction methods show promise for efficient data acquisition and processing for locating purposefully hidden tunnels within unconsolidated sediments.
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