Academic literature on the topic 'MEG data'
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Journal articles on the topic "MEG data"
Lukka, Tuomas, Bernd Schoner, and Alec Marantz. "Phoneme discrimination from MEG data." Neurocomputing 31, no. 1-4 (March 2000): 153–65. http://dx.doi.org/10.1016/s0925-2312(99)00178-2.
Full textChen, Zhihau, Alex Tretyakov, Hideki Takayasu, and Nobukazu Nakasato. "Spectral Analysis of Multichannel Meg Data." Fractals 06, no. 04 (December 1998): 395–400. http://dx.doi.org/10.1142/s0218348x98000432.
Full textCheung, Michael J., Natasa Kovačević, Zainab Fatima, Bratislav Mišić, and Anthony R. McIntosh. "[MEG]PLS: A pipeline for MEG data analysis and partial least squares statistics." NeuroImage 124 (January 2016): 181–93. http://dx.doi.org/10.1016/j.neuroimage.2015.08.045.
Full textIkeda, S., and K. Toyama. "Independent component analysis for noisy data — MEG data analysis." Neural Networks 13, no. 10 (December 2000): 1063–74. http://dx.doi.org/10.1016/s0893-6080(00)00071-x.
Full textWu, Huanqi, Ruonan Wang, Yuyu Ma, Xiaoyu Liang, Changzeng Liu, Dexin Yu, Nan An, and Xiaolin Ning. "Decoding N400m Evoked Component: A Tutorial on Multivariate Pattern Analysis for OP-MEG Data." Bioengineering 11, no. 6 (June 13, 2024): 609. http://dx.doi.org/10.3390/bioengineering11060609.
Full textAskari, Pegah, Natascha Cardoso da Fonseca, Tyrell Pruitt, Joseph A. Maldjian, Sasha Alick-Lindstrom, and Elizabeth M. Davenport. "Magnetoencephalography (MEG) Data Processing in Epilepsy Patients with Implanted Responsive Neurostimulation (RNS) Devices." Brain Sciences 14, no. 2 (February 9, 2024): 173. http://dx.doi.org/10.3390/brainsci14020173.
Full textLitvak, 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.
Full textGross, J., and A. A. Ioannides. "Linear transformations of data space in MEG." Physics in Medicine and Biology 44, no. 8 (July 22, 1999): 2081–97. http://dx.doi.org/10.1088/0031-9155/44/8/317.
Full textLuckhoo, Henry T., Matthew J. Brookes, and Mark W. Woolrich. "Multi-session statistics on beamformed MEG data." NeuroImage 95 (July 2014): 330–35. http://dx.doi.org/10.1016/j.neuroimage.2013.12.026.
Full textVentrucci, Massimo, Claire Miller (née Ferguson), Joachim Gross, Jan-Mathijs Schoffelen, and Adrian W. Bowman. "Spatiotemporal smoothing of single trial MEG data." Journal of Neuroscience Methods 200, no. 2 (September 2011): 219–28. http://dx.doi.org/10.1016/j.jneumeth.2011.06.004.
Full textDissertations / Theses on the topic "MEG data"
Schönherr, Margit. "Development and Evaluation of Data Processing Techniques in Magnetoencephalography." Doctoral thesis, Universitätsbibliothek Leipzig, 2012. http://nbn-resolving.de/urn:nbn:de:bsz:15-qucosa-96832.
Full textZumer, Johanna Margarete. "Probabilistic methods for neural source reconstruction from MEG data." Diss., Search in ProQuest Dissertations & Theses. UC Only, 2007. http://gateway.proquest.com/openurl?url_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&res_dat=xri:pqdiss&rft_dat=xri:pqdiss:3289309.
Full textSource: Dissertation Abstracts International, Volume: 68-11, Section: B, page: 7485. Adviser: Srikantan Nagarajan.
YU, LIJUN. "Sequential Monte Carlo for Estimating Brain Activity from MEG Data." Case Western Reserve University School of Graduate Studies / OhioLINK, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=case1459528441.
Full textZaremba, 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.
Full textChowdhury, Rasheda. "Localization of the generators of epileptic activity using Magneto-EncephaloGraphy (MEG) data." Thesis, McGill University, 2011. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=103740.
Full textComprendre les mécanismes sous-jacents associés à la génération d'une activité épileptique ainsi que la localisation des régions cérébrales impliquées lors d'une telle décharge sont d'un intérêt majeur lors du planning pré-chirurgical des patients souffrant d'épilepsie pharmaco-résistante. Les pointes épileptiques sont des décharges neuronales anormales générées de manière spontanée. Elles ne sont associées à aucune manifestation clinique et sont caractéristiques de l'épilepsie de chaque patient. Elles sont détectables à l'aide d'enregistrements de scalp tels que l'Electro-EncéphaloGraphie (EEG) ou la Magnéto-EncéphaloGraphie (MEG), mesurant respectivement les potentiels électriques et champs magnétiques générés par des populations de neurones activées de manière synchrone. Les pointes épileptiques peuvent être détectées en EEG ou en MEG à condition qu'elles se distinguent de l'activité de fond. Pour cela, elles doivent être associées à des générateurs suffisamment étendus spatialement. Alors que les méthodes dites de localisation de sources s'intéressent principalement à localiser l'origine des générateurs de ces décharges épileptiques, l'objectif de ce travail de Maîtrise est d'associer la localisation de ces générateurs à l'estimation de leur extension spatiale. Dans le cadre de ce projet de Maîtrise, nous avons développé et validé des méthodes de localisation des sources capables de localiser les générateurs d'activité épileptique ainsi que leur extension spatiale le long de la surface corticale. Le Maximum d'Entropie sur la Moyenne (MEM) est une technique de localisation de la source qui a démontré de telles performances lors de l'utilisation de données EEG. L'objectif de ce projet était d'adapter et valider le comportement du MEM lors de l'utilisation de données MEG. Le MEM introduit des connaissances a priori réalistes afin de modéliser les générateurs de pointes épileptiques. A partir de tels modèles a priori, deux nouvelles variantes du MEM ont été proposées et comparées avec de nouvelles méthodes implémentées dans le cadre du modèle hiérarchique Bayésien (inférence obtenue par maximum de vraisemblance restreint ReML). Notre objectif était de comparer la pertinence des modèles a priori considérés dans deux cadres statistiques de régularisation (MEM et ReML). A l'aide de simulations réaliste de l'activité épileptique, ces nouvelles méthodes ont été étudiées et leurs performances en termes de localisation spatiale des sources et de leur extension spatiale ont été évaluées. Les résultats ont montré que les variantes du MEM ont fourni les meilleures performances pour localiser les sources avec leur extension spatiale. Finalement, nous présentons quelques résultats préliminaires illustrant les performances de méthodes proposées sur des données cliniques. Ces nouvelles méthodes ont été appliquées sur quelques données cliniques afin d'évaluer leur pertinence dans le contexte du planning pré-chirurgical. Finalement, nous nous sommes intéressés à la possibilité d'utiliser les techniques de régularisation de type MEM et ReML pour proposer des métriques de comparaison de modèles, lors de l'analyse de données cliniques. Nous avons appliqué ces métriques afin d'évaluer l'impact du type de modèle direct sur la précision des méthodes. Nos résultats préliminaires suggèrent que le modèle réaliste des éléments frontières serait plus pertinent que le modèle sphérique lors de la localisation de données MEG.
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.
Full textIncludes 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.
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.
Full textZavala, 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.
Full textDubarry, Anne-Sophie. "Linking neurophysiological data to cognitive functions : methodological developments and applications." Thesis, Aix-Marseille, 2016. http://www.theses.fr/2016AIXM5017.
Full textA 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
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.
Full textBooks on the topic "MEG data"
Rongen, Heinz. Echtzeitsystem für Phasenrücksetzanalysen und Neuro-Rückkopplungen am MEG. Jülich: Forschungszentrum Jülich, Zentralbibliothek, 2006.
Find full textGerhardi, Silvia. Real men don't collect soft data. Trento: Dipartimento di Politica Sociale, Universita di Trento, 1988.
Find full textBalghabaev, Sultanălī. Dala men darii͡a︡: Povester men ăn͡g︡gīmeler. Almaty: "Zhazushy", 1988.
Find full textSpindel, Janis. How to Date Men. New York: Penguin Group USA, Inc., 2008.
Find full textSu̇ndetov, Maghzom. Dala men qūm ăuenderī. Almaty: "Zhazushy", 1987.
Find full textStatskontoret, Sweden, ed. Försöksverksamhet med PIR: Utvärdering. Stockholm: Statskontoret, 1987.
Find full textTorvund, Helge. Multippel og dama med himmelhatten. Oslo: Gyldendal, 1986.
Find full textEsenbaĭ, Du̇ĭsenbaev, ed. Dala dausy: Ȯlen͡g︡der men poėmalar. Almaty: Zhazushy, 1987.
Find full textZarra, Marcus S. Core Data. Raleigh, N.C: Pragmatic Bookshelf, 2009.
Find full textBeijing Jing Hong En Dian Nao You Xian Gong Si. Dian nao ru men pian. [Beijing]: Qing Hua da xue chu ban she, 2001.
Find full textBook chapters on the topic "MEG data"
Perry, Gavin. "Analysing data time series." In Working with MEG, 59–92. London: Routledge, 2022. http://dx.doi.org/10.4324/9781315205175-5.
Full textPerry, Gavin. "How to collect MEG data." In Working with MEG, 31–55. London: Routledge, 2022. http://dx.doi.org/10.4324/9781315205175-3.
Full textSellers, 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.
Full textIversen, 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.
Full textIversen, 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.
Full textIversen, 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.
Full textSorrentino, 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.
Full textRoche, David, Alexandra Calteau, and David Vallenet. "Analyzing Prokaryotic Transcriptomics in the Light of Genome Data with the MicroScope Platform." In Microbial Environmental Genomics (MEG), 241–70. New York, NY: Springer US, 2022. http://dx.doi.org/10.1007/978-1-0716-2871-3_13.
Full textAhrendt, Steven R., Stephen J. Mondo, Sajeet Haridas, and Igor V. Grigoriev. "MycoCosm, the JGI’s Fungal Genome Portal for Comparative Genomic and Multiomics Data Analyses." In Microbial Environmental Genomics (MEG), 271–91. New York, NY: Springer US, 2022. http://dx.doi.org/10.1007/978-1-0716-2871-3_14.
Full textGreenblatt, R. E., A. Ossadtchi, L. Kurelowech, D. Lawson, and J. Criado. "Time-Frequency Source Estimation from MEG Data." In IFMBE Proceedings, 136–39. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-12197-5_28.
Full textConference papers on the topic "MEG data"
Mouches, Pauline, Thibaut Dejean, Julien Jung, Romain Bouet, Carole Lartizien, and Romain Quentin. "Time CNN and Graph Convolution Network for Epileptic Spike Detection in Meg Data." In 2024 IEEE International Symposium on Biomedical Imaging (ISBI), 1–5. IEEE, 2024. http://dx.doi.org/10.1109/isbi56570.2024.10635822.
Full textIvanova, Marina, Grigoriy Kopytin, Viktoria Moiseeva, and Anna Shestakova. "Neurophysiological Correlates of Probabilistic Reward-Based Learning: Using Decoding Approach on MEG Data." In 2024 Sixth International Conference Neurotechnologies and Neurointerfaces (CNN), 47–50. IEEE, 2024. http://dx.doi.org/10.1109/cnn63506.2024.10705812.
Full textVlasenko, 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.
Full textZhang, Yishuo, Nayyar A. Zaidi, Gang Li, and Wray Buntine. "MEG: Masked Ensemble Tabular Data Generator." In 2023 IEEE International Conference on Data Mining (ICDM). IEEE, 2023. http://dx.doi.org/10.1109/icdm58522.2023.00093.
Full textSreenathan, G., and G. K. Sadanandan. "Denoising MEG sensor data using wavelets." In 2013 Annual International Conference on Emerging Research Areas (AICERA) - 2013 International Conference on Microelectronics, Communications and Renewable Energy (ICMiCR). IEEE, 2013. http://dx.doi.org/10.1109/aicera-icmicr.2013.6575998.
Full textDATE, SUSUMU, SHIMOJO SHINJI, MIZUNO-MATSUMOTO YUKO, SONG JIE, BU SUNG LEE, WENTONG CAI, and LIZHE WANG. "Distributed processing and visualization of MEG data." In Proceedings of the International Conference on Scientific and Engineering Computation (IC-SEC) 2002. PUBLISHED BY IMPERIAL COLLEGE PRESS AND DISTRIBUTED BY WORLD SCIENTIFIC PUBLISHING CO., 2002. http://dx.doi.org/10.1142/9781860949524_0196.
Full textMosher, John C., Paul S. Lewis, Richard M. Leahy, and Manbir Singh. "Multiple dipole modeling of spatiotemporal MEG data." In San Diego '90, 8-13 July, edited by Arthur F. Gmitro, Paul S. Idell, and Ivan J. LaHaie. SPIE, 1990. http://dx.doi.org/10.1117/12.23649.
Full textTASS, P., J. GROSS, M. G. ROSENBLUM, A. SCHNITZLER, J. VOLKMANN, J. KURTHS, and H. J. FREUND. "DETECTION OF PHASE SYNCHRONIZATION IN HUMAN MEG DATA." In Proceedings of the Workshop. WORLD SCIENTIFIC, 2000. http://dx.doi.org/10.1142/9789812793782_0032.
Full textMaksimenko, Vladimir A., Nikita S. Frolov, and Alexander N. Pisarchik. "Analysis of bistable perception based on MEG data." In Dynamics and Fluctuations in Biomedical Photonics XV, edited by Valery V. Tuchin, Kirill V. Larin, Martin J. Leahy, and Ruikang K. Wang. SPIE, 2018. http://dx.doi.org/10.1117/12.2291673.
Full textAnton, Selskii, Frolov Nikita, and Pisarchik Alexander. "Mathematical methods of analysis of the MEG brain data:." In 2018 2nd School on Dynamics of Complex Networks and their Application in Intellectual Robotics (DCNAIR). IEEE, 2018. http://dx.doi.org/10.1109/dcnair.2018.8589200.
Full textReports on the topic "MEG data"
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.
Full textNäslund, Joacim, Björn Ardestam, Malin Hällbom, Ola Renman, and Thomas Staveley. Båtelfiske i lugnflytande åar 2021 – metod, resultat och erfarenheter. Department of Aquatic Resources, Swedish University of Agricultural Sciences, 2023. http://dx.doi.org/10.54612/a.76acmsrd73.
Full textLaycak, Danny. MACCS2 Met Data. Office of Scientific and Technical Information (OSTI), April 2020. http://dx.doi.org/10.2172/1616389.
Full textSigfridsson, Tove, and Karin Skill. Digitala delningslösningar som innovation i landsbygder och glesa geografier? Fallet skolskjuts. Linköping University Electronic Press, July 2023. http://dx.doi.org/10.3384/report-194773.
Full textKaharevic, Ahmed, and Elin Wihlborg. Fler sidor av digital medieanvändning bland unga i bostadsområden med socioekonomiska utmaningar: En forskningsöversikt och diskussion om metoder genomförd på uppdrag av Statens medieråd. Linköping University Electronic Press, February 2024. http://dx.doi.org/10.3384/dino-2023.2.
Full textBergström, Lena, Emma Svahn, and Anders Adill. Provfiske efter strömming i södra Bottenhavet – översikt av äldre studier och återbesök 2022. Department of Aquatic Resources, Swedish University of Agricultural Sciences, 2023. http://dx.doi.org/10.54612/a.6rd1p380jp.
Full textLundström, Karl. Rikstäckande inventering av häckande storskarv (Phalacrocorax carbo) i Sverige 2023. Department of Aquatic Resources, Swedish University of Agricultural Sciences, 2024. http://dx.doi.org/10.54612/a.6tcqoqona2.
Full textMalmquist, Louise, and Jennie Barron. Högfrekvent vattenföringsmätning i Braån, Loftaån, Örsundaån och Ösan år 2022 till 2023. Department of Soil and Environment, Swedish University of Agricultural Sciences, 2024. http://dx.doi.org/10.54612/a.103r5g5o99.
Full textSvenson, Anders, and Jonas Hentati Sundberg. Expeditionsrapport SPRAS 2023 : ekosystemundersökning i Östersjön. Department of Aquatic Resources, Swedish University of Agricultural Sciences, 2024. http://dx.doi.org/10.54612/a.2g3ik551e3.
Full textSvenson, Anders, and Jonas Hentati Sundberg. Expeditionsrapport SPRAS 2022 : Ekosystemundersökning i Östersjön. Department of Aquatic Resources, Swedish University of Agricultural Sciences, 2023. http://dx.doi.org/10.54612/a.77of4l0u1v.
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