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Academic literature on the topic 'EEJ electric field'

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Published: 6 September 2023

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Journal articles on the topic "EEJ electric field"

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TERAKURA, TAKUMA, KEI TAKANO, TAKANORI YASUOKA, SHIGEKAZU MORI, OSAMU HOSOKAWA, TAKASHI IWABUCHI, TAKASHI CHIGIRI, and SHIN YAMADA. "Transient Electric Field Analysis in Consideration of the Electric Field Dependence of Resistivity in the Converter Transformer." Electrical Engineering in Japan 198, no. 1 (September 13, 2016): 3–11. http://dx.doi.org/10.1002/eej.22878.

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Sato, Susumu, and Masahito Kushima. "Applications of nematic liquid crystals to electric-field sensors." Electrical Engineering in Japan 110, no. 7 (1990): 74–81. http://dx.doi.org/10.1002/eej.4391100708.

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Tashiro, Shinichi, and Masao Endo. "Electric field of surface discharge inception by electrification charge." Electrical Engineering in Japan 145, no. 2 (July 24, 2003): 1–9. http://dx.doi.org/10.1002/eej.10192.

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Hamada, Shoji, and Tadasu Takuma. "Electric field calculation in composite dielectrics by surface charge method based on electric flux continuity condition." Electrical Engineering in Japan 138, no. 4 (January 18, 2002): 10–17. http://dx.doi.org/10.1002/eej.1133.

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Hamada, Shoji, Osamu Yamamoto, and Tetsuo Kobayashi. "Analysis of electric field induced by ELF magnetic field utilizing generalized equivalent multipole-moment method." Electrical Engineering in Japan 156, no. 2 (2006): 1–14. http://dx.doi.org/10.1002/eej.20342.

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Hamada, Shoji, and Tadasu Takuma. "Electric field calculation in composite dielectrics by curved surface charge method based on electric flux continuity condition." Electrical Engineering in Japan 141, no. 3 (August 21, 2002): 9–16. http://dx.doi.org/10.1002/eej.2017.

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Denardini, C. M., M. A. Abdu, E. R. de Paula, C. M. Wrasse, and J. H. A. Sobral. "VHF radar observations of the dip equatorial E-region during sunset in the Brazilian sector." Annales Geophysicae 24, no. 6 (July 3, 2006): 1617–23. http://dx.doi.org/10.5194/angeo-24-1617-2006.

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Abstract. Using the RESCO 50 MHz backscatter radar (2.33° S, 44.2° W, DIP: –0.5), at São Luís, Brazil, we obtained Range Time Intensity (RTI) maps covering the equatorial electrojet heights during daytime and evening. These maps revealed a scattering region at an altitude of about 108 km during the sunset period. The type of 3-m irregularity region we present here has not been reported before in the literature, to our knowledge. It was mainly observed around the Southern Hemisphere summer-solstice period, under quiet magnetic activity condition. The occurrence of this echo region coincides in local time with the maximum intensity of an evening pre-reversal eastward electric field of the ionospheric F-region. A tentative explanation is proposed here in terms of the theory of the divergence of the equatorial electrojet (EEJ) current in the evening ionosphere presented by Haerendel and Eccles (1992), to explain the partial contribution of the divergence to the development of the pre-reversal electric field. The theory predicts an enhanced zonal electric field and hence a vertical electric field below 300 km as a consequence of the EEJ divergence in the evening. The experimental results of the enhanced echoes from the higher heights of the EEJ region seem to provide evidence that the divergence of the EEJ current can indeed be the driver of the observed scattering region.
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Nakano, Mitsuaki. "DC conduction associated with electric field-induced motion in mineral oils." Electrical Engineering in Japan 114, no. 4 (1994): 1–12. http://dx.doi.org/10.1002/eej.4391140401.

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Yamazaki, Keita, Kazuo Kato, Koichiro Kobayashi, Ken Kawamata, Akiyoshi Saga, Noboru Goto, Shigeki Minegishi, and Akira Haga. "Environmental low-frequency magnetic field due to direct-current electric railcars." Electrical Engineering in Japan 137, no. 3 (November 30, 2001): 10–21. http://dx.doi.org/10.1002/eej.1090.

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Kawamoto, Tadashi, Tadasu Takuma, Hisashi Goshima, Hiroyuki Shinkai, and Hideo Fujinami. "Triple-junction effect and its electric field relaxation in three dielectrics." Electrical Engineering in Japan 167, no. 1 (April 15, 2009): 1–8. http://dx.doi.org/10.1002/eej.20670.

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Dissertations / Theses on the topic "EEJ electric field"

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Koulouri, Alexandra. "Reconstruction of electric fields and source distributions in EEG brain imaging." Thesis, Imperial College London, 2014. http://hdl.handle.net/10044/1/25759.

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In this thesis, three different approaches are developed for the estimation of focal brain activity using EEG measurements. The proposed approaches have been tested and found feasible using simulated data. First, we develop a robust solver for the recovery of focal dipole sources. The solver uses a weighted dipole strength penalty term (also called weighted L1,2 norm) as prior information in order to ensure that the sources are sparse and focal, and that both the source orientation and depth bias are reduced. The solver is based on the truncated Newton interior point method combined with a logarithmic barrier method for the approximation of the penalty term. In addition, we use a Bayesian framework to derive the depth weights in the prior that are used to reduce the tendency of the solver to favor superficial sources. In the second approach, vector field tomography (VFT) is used for the estimation of underlying electric fields inside the brain from external EEG measurements. The electric field is reconstructed using a set of line integrals. This is the first time that VFT has been used for the recovery of fields when the dipole source lies inside the domain of reconstruction. The benefit of this approach is that we do not need a mathematical model for the sources. The test cases indicated that the approach can accurately localize the source activity. In the last part of the thesis, we show that, by using the Bayesian approximation error approach (AEA), precise knowledge of the tissue conductivities and head geometry are not always needed. We deliberately use a coarse head model and we take the typical variations in the head geometry and tissue conductivities into account statistically in the inverse model. We demonstrate that the AEA results are comparable to those obtained with an accurate head model.
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Yurtkolesi, Mustafa. "Imaging Electrical Conductivity Distribution Of The Human Head Using Evoked Fields And Potentials." Master's thesis, METU, 2008. http://etd.lib.metu.edu.tr/upload/12609828/index.pdf.

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In the human brain, electrical activities are created due to the body functions. These electrical activities create potentials and magnetic fields which can be monitored elec- trically (Electroencephalography - EEG) or magnetically (Magnetoencephalography - MEG). Electrical activities in human brain are usually modeled by electrical dipoles. The purpose of Electro-magnetic source imaging (EMSI) is to determine the position, orientation and strength of dipoles. The first stage of EMSI is to model the human head numerically. In this study, The Finite Element Method (FEM) is chosen to han- dle anisotropy in the brain. The second stage of EMSI is to solve the potentials and magnetic fields for an assumed dipole configuration (forward problem). Realistic con- ductivity distribution of human head is required for more accurate forward problem solutions. However, to our knowledge, conductivity distribution for an individual has not been computed yet. The aim of this thesis study is to investigate the feasibility of a new approach to update the initially assumed conductivity distribution by using the evoked potentials and fields acquired during EMSI studies. This will increase the success of source localization problem, since more realistic conductivity distribution of the head will be used in the forward problem. This new method can also be used as a new imaging modality, especially for inhomogeneities where the conductivity value deviates. In this thesis study, to investigate the sensitivity of measurements to conductivity perturbations, a FEM based sensitivity matrix approach is used. The performance of the proposed method is tested using three different head models - homogeneous spherical, 4 layer concentric sphere and realistic head model. For spherical head models rectangular grids are preferred in the middle and curved elements are used nearby the head boundary. For realistic cases, head models are developed using uniform grids. Tissue boundary information is obtained by applying segmentation algorithms to the Magnetic Resonance (MR) images. A paralel computer cluster is employed to assess the feasibility of this new approach. PETSc library is used for forward problem calculations and linear system solutions. The performance of this novel approach depends on many factors such as the head model, number of dipoles and sensors used in the calculation, noise in the measure- ments, etc. In this thesis study, a number of simulations are performed to investigate the effects of each of these parameters. Increase in the number of elements in the head model leads to the increase in the number of unknows for linear system solu- tions. Then, accuracy of the solution is improved with increased number of dipoles or sensors. The performance of the adopted approach is investigated using noise-free measurements as well as noisy measurements. For EEG, measurement noise decreases the accuracy of the approach. For MEG, the effect of measurement noise is more pronounced and may lead to a larger error in tissue conductivity calculation.
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Γιαπαλάκη, Σοφία. "Μελέτη προτύπων ιατρικής φυσικής μέσω της επίλυσης προβλημάτων μαθηματικής νευροφυσιολογίας." Thesis, 2006. http://nemertes.lis.upatras.gr/jspui/handle/10889/1473.

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Η Ηλεκτροεγκεφαλογραφία (ΗΕΓ) και η Μαγνητοεγκεφαλογραφία (ΜΕΓ) αποτελούν δύο από τις πλέον ευρέως χρησιμοποιούμενες μη επεμβατικές μεθόδους μελέτης της λειτουργίας του ανθρώπινου εγκεφάλου, κατά τις οποίες καταγράφονται εξωτερικά του κρανίου, το ηλεκτρικό και το μαγνητικό πεδίο, που οφείλονται στη διέργεση εγκεφαλικών νευρώνων. Oι κύριες βιοηλεκτρικές πηγές των πεδίων που καταγράφονται σ’ αυτά, είναι ομάδες νευρώνων, που προτυποποιούνται με ένα ηλεκτρικό δίπολο. Αρχικά επιλέγεται το πλέον ρεαλιστικό πρότυπο των τριών φλοιών. Δηλαδή ως αγωγός θεωρείται ολόκληρο το κρανίο, συμπεριλαμβανομένου του δέρματος, των κρανιακών οστών, του εγκεφαλονωτιαίου υγρού και του εγκεφαλικού ιστού – περιοχές διαφορετικής ηλεκτρικής αγωγιμότητας – και υπολογίζεται το ηλεκτρικό δυναμικό και το μαγνητικό πεδίο, επιλύεται δηλαδή τόσο το ευθύ πρόβλημα ΗΕΓ, όσο και το αντίστοιχο ΜΕΓ, στη σφαιρική και στην ελλειψοειδή γεωμετρία. Το δεύτερο πρότυπο αφορά στην επίλυση του ευθέος προβλήματος ΗΕΓ για την περίπτωση όπου ο εγκεφαλικός ιστός θεωρηθεί ως ένα σφαιρικός αγωγός, στο εσωτερικό του οποίου βρίσκεται είτε ομόκεντρα μια σφαιρική περιοχή υγρού, οπότε χρησιμοποιείται για την επίλυση το σφαιρικό σύστημα συντεταγμένων, είτε έκκεντρα, οπότε χρησιμοποιείται αντίστοιχα το δισφαιρικό. Τέλος, ως αγωγός θεωρείται μια ομογενής σφαίρα, περίπτωση όπου η ακριβής και πλήρης αναλυτική λύση για το πρόβλημα του Βιομαγνητισμού είναι γνωστή. Η συνεισφορά όμως της διατριβής για το πρότυπο αυτό είναι στη δημιουργία χρήσιμων εργαλείων για την μετατροπή των αναπτυγμάτων των λύσεων σε σειρές, στις αντίστοιχες κλειστές μορφές μέσω της άθροισης των σειρών, καθώς και στην εξαγωγή συμπερασμάτων σχετικά με το αντίστροφο πρόβλημα ΗΕΓ, τα οποία προκύπτουν από τη γραφική επεξεργασία της κλειστής λύσης του ηλεκτρικού δυναμικού, όπως αυτή προέκυψε από τη μέθοδο των ειδώλων.
Electroenchephalography (EEG) and Magnetoenchephalophy (MEG) are common non invansive methods for studying the function of the human brain. Considering that the data of the generated electric potential (Electroencephalogram) and the magnetic field (Magnetoenchephalogram), takes place on or in the surrounding the head, the entire head, including the skin, the bones, the cerebrospinal fluid and the cerebral, regions which are characterizing by different electric conductivity are including. For this model, the direct Bioelectromagnetism problem is solved in both spherical and ellipsoidal geometry. Specifically, the leading terms of the electric potential in the exterior of the conductor and everywhere in the interior, as well as the leading quadrupolic term of the multipole expansion of the exterior magnetic induction field in the ellipsoidal geometry, are obtained. The reduction of the the ellipsoidal results to the corresponding spherical case, which has brought up useful conclusions concerning these two geometrical models, is also presented. The direct EEG problem is described, for the case where the entire cerebral is considered as a spherical conductor, which surrounds a fluid spherical region of different conductivity. When the two spherical regions are concentric, the problem is solved with the spherical geometry, but when these are eccentric the problem is solved with the bispherical geometry. Finally, the exact and complete analytic solution for the forward EEG problem is produced by the Image Theory for the homogeneous spherical conductor and is elaborated graphically. In particular, some electric potential distributions are produced on the surface of the spherical brain, where the equipotential curves are represented by circles. Considering these distributions, a parametric analysis of the position and the orientation o the moment dipole is accomplished for the current dipole that has considered in this thesis. Consequently, when the source is near the surface, the orientation of the moment is directed vertically to the zero equipotential circle to the increase potential, since the position vector of the source tends to become vertical to the maximum equipotential curves. The existence of special position and orientation of the source, for which the contribution in the external magnetic field is zero - and for the spherical case, where the position and the orientation of the sources are parallel - corresponds to parallel equipotential curves.
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Books on the topic "EEJ electric field"

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Ramesh, Srinivasan, ed. Electric fields of the brain: The neurophysics of EEG. 2nd ed. New York: Oxford University Press, 2006.

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Michel, Christoph M., and Bin He. EEG Mapping and Source Imaging. Edited by Donald L. Schomer and Fernando H. Lopes da Silva. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780190228484.003.0045.

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This chapter describes methods to analyze the scalp electric field recorded with multichannel electroencephalography (EEG). With advances in high-density EEG, systems now allow fast and easy recording from 64 to 256 channels simultaneously. Pattern-recognition algorithms can characterize the topography of scalp electric fields and detect changes in topography over time and between experimental or clinical conditions. Methods for estimating the sources underlying the recorded scalp potential maps have increased the spatial resolution of EEG. The use of anatomical information in EEG source reconstruction has increased the precision of EEG source localization. Algorithms of functional connectivity applied to the source space allow determination of communication between large-scale brain networks in certain frequencies and identification of the directionality of this information flow and detection of crucial drivers in these networks. These methods have boosted the use of EEG as a functional neuroimaging method in experimental and clinical applications.
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Reed, Sean, Sonia Jego, and Antoine Adamantidis. Electroencephalography and Local Field Potentials in Animals. Oxford University Press, 2015. http://dx.doi.org/10.1093/med/9780199939800.003.0007.

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This chapter discusses the history, practice, and application of electroencephalography (EEG) and local field potential (LFP) recordings, with a particular focus on animal models. EEG measures the fluctuations of electrical activity resulting from ionic currents in the brain. These measurements are often taken from electrodes placed on the surface of the scalp, or in animal models, directly on the skull. LFP recordings are more invasive, measuring electrical current from all nearby dendritic synaptic activity within a volume of tissue. These two techniques are useful in determining how neural activity can synchronize during different behavioral or motivational states.
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Wadman, Wytse J., and Fernando H. Lopes da Silva. Biophysical Aspects of EEG and MEG Generation. Edited by Donald L. Schomer and Fernando H. Lopes da Silva. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780190228484.003.0004.

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This chapter reviews the essential physical principles involved in the generation of electroencephalographic (EEG) and magnetoencephalographic (MEG) signals. The general laws governing the electrophysiology of neuronal activity are analyzed within the formalism of the Maxwell equations that constitute the basis for understanding electromagnetic fields in general. Three main topics are discussed. The first is the forward problem: How can one calculate the electrical field that results from a known configuration of neuronal sources? The second is the inverse problem: Given an electrical field as a function of space and time mostly recorded at the scalp (EEG/MEG), how can one reconstruct the underlying generators at the brain level? The third is the reverse problem: How can brain activity be modulated by external electromagnetic fields with diagnostic and/or therapeutic objectives? The chapter emphasizes the importance of understanding the common biophysical framework concerning these three main topics of brain electrical and magnetic activities.
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Nunez, Paul L., and Ramesh Srinivasan. Electric Fields of the Brain: The Neurophysics of EEG. Oxford University Press, USA, 2005.

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Amzica, Florin, and Fernando H. Lopes da Silva. Cellular Substrates of Brain Rhythms. Edited by Donald L. Schomer and Fernando H. Lopes da Silva. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780190228484.003.0002.

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The purpose of this chapter is to familiarize the reader with the basic electrical patterns of the electroencephalogram (EEG). Brain cells (mainly neurons and glia) are organized in multiple levels of intricate networks. The cellular membranes are semipermeable media between extracellular and intracellular solutions, populated by ions and other electrically charged molecules. This represents the basis of electrical currents flowing across cellular membranes, further generating electromagnetic fields that radiate to the scalp electrodes, which record changes in the activity of brain cells. This chapter presents these concepts together with the mechanisms of building up the EEG signal. The chapter discusses the various behavioral conditions and neurophysiological mechanisms that modulate the activity of cells leading to the most common EEG patterns, such as the cellular interactions for alpha, beta, gamma, slow, delta, and theta oscillations, DC shifts, and some particular waveforms such as sleep spindles and K-complexes and nu-complexes.
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Schomer, Donald L., and Fernando H. Lopes da Silva, eds. Niedermeyer's Electroencephalography. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780190228484.001.0001.

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This book deals with the field of Electroencephalography in the widest possible sense, from the cellular foundations of the electric activities of the brain to a vast number of clinical applications. The basic science sections were up-dated to include advanced computer modeling approaches. The chapters on normal and pathological EEG findings in premature infants, newborns and children were thoroughly revised to keep up with the advances that have taken place recently in studying brain developmental issues. Major advances have taken place in neurophysiological findings in a variety of neurodegenerative disorders, which led to thoroughly revised chapters. Other rapidly changing subjects related to EEG recording/monitoring in ICU's, EMUs, and operating rooms, in patients with epilepsy, head injuries, infectious disorders and those undergoing surgical procedures, led to radically updating a number of chapters and to the addition of a chapter dedicated to invasive recordings for the treatment of patients with movement disorders. A previously missing chapter on the neurophysiology of myoclonus was added. Chapters that deal with automated EEG interpretation techniques and with standardizing EEG reporting using ILAE/IFCN approved terminology, were also added. Many chapters in the on-line version of the book will have the ability to link to a database of over 150 complete EEGs that cover the scope seen in a general EEG Lab. This link will allow the reader to manipulate the EEG display parameters as if they were in their own lab, generate a report and compare it to one generated by a panel of senior EEGers.
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Book chapters on the topic "EEJ electric field"

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Jäntti, Ville, Narayan Puthanmadam Subramaniyam, Kotoe Kamata, Tuomo Ylinen, Arvi Yli-Hankala, Pasi Kauppinen, and Outi Väisänen. "Electric field of EEG during anesthesia." In EMBEC & NBC 2017, 354–57. Singapore: Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-5122-7_89.

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Bhattacharya, Sitangshu, and Kamakhya Prasad Ghatak. "The EEM in the Presence of Intense Electric Field." In Effective Electron Mass in Low-Dimensional Semiconductors, 319–63. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-31248-9_7.

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Hari, Riitta, and Aina Puce. "Introduction." In MEG - EEG Primer, edited by Riitta Hari and Aina Puce, 3—C1P47. 2nd ed. Oxford University PressNew York, 2023. http://dx.doi.org/10.1093/med/9780197542187.003.0001.

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Abstract This beginning chapter first briefly introduces MEG and EEG and compares them with each other. Neuronal communication in the brain is associated with minute electrical currents that give rise to both electrical potentials on the scalp (measured by EEG) and magnetic fields outside the head (measurable by MEG). The chapter then describes the common features of MEG and EEG laboratory setups, followed by introduction of a current dipole, a volume conductor, electric potential, and magnetic field. The chapter ends by explaining the main structure of the primer so that the reader can navigate the material more easily.
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Hari, Riitta, and Aina Puce. "Introduction." In MEG-EEG Primer, edited by Riitta Hari and Aina Puce, 3–12. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780190497774.003.0001.

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Neuronal communication in the brain is associated with minute electrical currents that give rise to both electrical potentials on the scalp (measurable by means of electroencephalography [EEG]) and magnetic fields outside the head (measurable by magnetoencephalography [MEG]). Both MEG and EEG are noninvasive neurophysiological methods used to study brain dynamics, that is temporal changes in the activation patterns, and sequences in signal progression. Differences between MEG and EEG mainly reflect differences in the spread of electric and magnetic fields generated by the same electric currents in the human brain. This chapter provides an overall description of the main principles of MEG and EEG and provides background for the following chapters in this and subsequent sections.
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Hari, Riitta, and Aina Puce. "Basic Physics and Physiology of MEG and EEG." In MEG-EEG Primer, edited by Riitta Hari and Aina Puce, 25–37. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780190497774.003.0003.

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This chapter outlines the general principles of physics and physiology underlying MEG and EEG signals. It introduces charges and electric currents, and the associated electric potentials and magnetic fields. Basic laws of electricity and the relationships between magnetic and electrical fields are examined. The phenomenon of superconductivity is introduced and related to measurements of MEG signals with SQUID sensors. The effects of various tissues and current configurations on MEG and EEG signals are described. Basic principles of source localization, that is modeling the neuronal sources of the observed MEG and EEG signals are described. The spatial accuracy versus precision of source localization is illustrated. Volume conduction, current spread in the different compartments of the body is explained.
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Nunez, Paul L., and Ramesh Srinivasan. "Fallacies in EEG." In Electric Fields of the Brain, 56–98. Oxford University Press, 2006. http://dx.doi.org/10.1093/acprof:oso/9780195050387.003.0002.

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Nunez, Paul L., and Ramesh Srinivasan. "High-Resolution EEG." In Electric Fields of the Brain, 313–52. Oxford University Press, 2006. http://dx.doi.org/10.1093/acprof:oso/9780195050387.003.0008.

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Nunez, Paul L., and Ramesh Srinivasan. "The Physics–EEG Interface." In Electric Fields of the Brain, 3–55. Oxford University Press, 2006. http://dx.doi.org/10.1093/acprof:oso/9780195050387.003.0001.

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Nunez, Paul L., and Ramesh Srinivasan. "Measures of EEG Dynamic Properties." In Electric Fields of the Brain, 353–431. Oxford University Press, 2006. http://dx.doi.org/10.1093/acprof:oso/9780195050387.003.0009.

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Nunez, Paul L., and Ramesh Srinivasan. "Spatial-Temporal Properties of EEG." In Electric Fields of the Brain, 432–85. Oxford University Press, 2006. http://dx.doi.org/10.1093/acprof:oso/9780195050387.003.0010.

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Conference papers on the topic "EEJ electric field"

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Sirakov, Kiril. "Modelling and Analysis of the Electric Field in a Chamber for Pre-Sowing Electrical Treatment of Seeds of Field Crops." In 2022 8th International Conference on Energy Efficiency and Agricultural Engineering (EE&AE). IEEE, 2022. http://dx.doi.org/10.1109/eeae53789.2022.9831312.

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Beecher, Scott F., and Bret G. Lynch. "Loading Software to Engine Controls in the Field." In ASME 1997 International Gas Turbine and Aeroengine Congress and Exhibition. American Society of Mechanical Engineers, 1997. http://dx.doi.org/10.1115/97-gt-016.

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With the advent of electronically alterable memories in electronic gas turbine engine control systems, there is now the opportunity for updating software in the field. Field loading provides a means to economically correct problems or introduce enhancements to system operation through the electronic control. In this paper we describe the characteristics of high integrity reprogramming systems used to update engine controls in-the-field. Pratt & Whitney Aircraft supports two methods for in-service reprogramming of Electronic Engine Controls (EECs), These two methods are PC Laptop based loaders and ARINC loaders. This discussion will focus on the capabilities provided to support in-the-field reprogramming of engine controls. The flexibility, integrity, and the benefits of field reprogramming provided by these software loading systems will be explained. These reprogramming systems provide a PC based application and ARINC based systems for either on-wing reprogramming or on-board reprogramming directly from a flight deck device to the EEC. The PC Loader reprogramming utilities allow field personnel to reprogram engine control application software and/or constants and configuration information using a suitably equipped IBM PC or compatible computer. These utilities are intended to be operated per Service Bulletin authorization only. They require a PC compatible computer (presumably a laptop model) with 2 UART interface cards, an interfacing cable, and the new software to be loaded. The rigor and manner of the integrity checks to ensure proper loading of the control is essential to an acceptable loading system. There are two types of ARINC-based loaders: on-wing loaders and on-board loaders. Both types enable the operator to upload application, trim, and/or configuration software to the engine control. Additionally the ARINC 615 device allows operators to download fault and configuration data from the control. Each type of loader uses a specially formatted file to control the sequence of operations involved in a data loading session. The on-wing loader utilizes a specially designed portable data loader which connects directly to the EEC via dedicated cabling through the control’s ARINC connectors. This type of data loader contains software which communicates via an ARINC 615 protocol to a peer software entity running on the EEC. The on-board loader uses the aircraft’s central maintenance computer system to communicate with the EEC over the aircraft’s ARINC 629 data bus. It also operates using a peer-to-peer communication protocol with the EEC. The ARINC 629 loader requires no extra equipment or cabling, nor does it require the EEC to be accessible for attachment of cables.
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Wu, Jin-bo, Quan Hong, Hui Li, Si-yuan Guo, Hao Xu, Wei-jun Zhu, and Fan Ouyang. "Study on Method for Field Secondary Signal Simulation of Optical Current Transformer." In Proceedings of the 2nd International Conference on Electrical and Electronic Engineering (EEE 2019). Paris, France: Atlantis Press, 2019. http://dx.doi.org/10.2991/eee-19.2019.3.

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Chen, Xiyou, Jianhui Chen, Guanlin Li, Xianmin Mu, and Chen Qi. "Electric-field-coupled single-wire power transmission — analytical model and experimental demonstration." In 2017 International Symposium on Power Electronics (Ee). IEEE, 2017. http://dx.doi.org/10.1109/pee.2017.8171661.

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Iapascurta, Victor, and Ion Fiodorov. "Kolmogorov-Chaitin Algorithmic Complexity for EEG Analysis." In 12th International Conference on Electronics, Communications and Computing. Technical University of Moldova, 2022. http://dx.doi.org/10.52326/ic-ecco.2022/cs.14.

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Electroencephalography as a generally accepted method of monitoring the electrical activity of brain neurons is widely used both in diseases and in healthy conditions. The recorded electrical signal is usually obtained from several electrodes located on the scalp. While EEG recording techniques are largely standardized, the interpretation of some aspects is still an open question. There is hardly questionable progress in detecting abnormal EEG signals known as seizures. A less explored field is the detection and classification of non-pathological conditions such as emotional and other functional states of the brain. This requires special approaches and techniques that have been widely developed over the past decade. The current paper describes an attempt to use algorithmic complexity concepts and tools for EEG transformation making it possible to combine this approach and machine learning for classification purposes.
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Raicevic, N. B., D. S. Tasic, S. S. Ilic, and S. R. Aleksic. "New EEM/BEM hybrid method for electric field calculation in cable terminations." In IEEE EUROCON 2011 - International Conference on Computer as a Tool. IEEE, 2011. http://dx.doi.org/10.1109/eurocon.2011.5929214.

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Knox, W. H., J. E. Henry, B. Tell, K. D. Li, D. A. B. Miller, and D. S. Chemla. "Femtosecond Excitonic Electroabsorption Sampling." In Picosecond Electronics and Optoelectronics. Washington, D.C.: Optica Publishing Group, 1989. http://dx.doi.org/10.1364/peo.1989.ds264.

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Optoelectronic sampling based on the Pockels' effect1 has become an important technique for the measurement of electrical signals with the highest time resolution, currently at 300 fs. We present first results obtained using a new technique for femtosecond electrical pulse measurement: excitonic electroabsorption sampling (EES). We have previously shown that excitons exhibit a femtosecond electroabsorption response, however the device which was used did not facilitate propagation studies over macroscopic distances2. In our new embodiment, a coplanar stripline is fabricated on a GaAs multiple quantum well mesa ridge structure (Fig. 1). We thus obtain optical modulation by parallel-field electroabsorption, which is due to lifetime broadening by field ionization of the excitons3. The detection sensitivity is about 1%/volt in a 10 micron structure. We etch the GaAs substrate down to a 1 micron AlGaAs stop-etch layer in a 1×2 mm area and leave the stripline free-standing on the 1 micron thick film, thus obtaining an extremely low dispersion structure to test the EES concept. We use an infrared dye laser which produces femtosecond pulses at a wavelength of 805 nm4 at 82 MHZ repetition rate. The exciton energy is temperature-tuned to the laser with a Peletier device, in this case operating at about 5 degrees above ambient temperature. At 300 fs pulsewidth the laser spectrum is already comparable to the exciton linewidth, and we expect that shorter pulses will provide reduced sensitivity relative to the DC response. We expect that time resolution of 100 fs or less may be possible with this technique. We note that electroabsorption is a purely electronic phenomenon, with no ionic lattice contribution such as that of LiTaO3.
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Szajerman, Dominik, and Piotr Napieralski. "Joint analysis of simultaneous EEG and eye tracking data for video picture." In 2017 18th International Symposium on Electromagnetic Fields in Mechatronics, Electrical and Electronic Engineering (ISEF). IEEE, 2017. http://dx.doi.org/10.1109/isef.2017.8090693.

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Sun, Shengkun, Yuhan Liu, and Yunfeng Jia. "Effect of Equipment Layout on Electromagnetic Field Distribution in Engine Room." In 2019 International Conference on Electronic Engineering and Informatics (EEI). IEEE, 2019. http://dx.doi.org/10.1109/eei48997.2019.00012.

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Alrajeh, N. A., K. W. Divine, T. P. Sullivan, and N. M. Bukhari. "Controlling a Valve Actuator and the Flow of Fluids with Interpreted Brain Signals." In SPE/IADC Middle East Drilling Technology Conference and Exhibition. SPE, 2023. http://dx.doi.org/10.2118/214559-ms.

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Abstract The ability to control machines and equipment with just one's thoughts is a concept that has been explored in the field of neuroscience for decades. The development of brain-computer interfaces (BCIs) has made it possible to translate electrical signals generated by the brain into machine commands. In recent years, the field of BCI has made significant advancements in non-intrusive technology, which has the potential to revolutionize the way we interact with machines and equipment in a variety of fields. The objective of this project is to merge non-intrusive BCI technology with personal protective equipment (PPE) and control the flow of fluids through human sensory and peripheral signals in field environments. A revolutionary system application was developed and tested by paper authors Kyle W. Divine, Thomas P. Sullivan, Nawaf A. Alrajeh and Nabil M. Bukhari from Saudi Aramco. This proposed system involves the use of a BCI with Electroencephalography (EEG) mounted within a traditional hardhat. The electrical signals generated from the thoughts of the wearer are used to train a Convolutional Neural Network (CNN) and modeled to recognize the wearer's imagined words, primarily open, close, push, pull, turn left, or turn right. These interpreted electrical signals are then relayed to the valve through a Micro Controller device resulting in the operation of the valve and the successful control of fluids with simple thought commands.
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