Livros: "MEG data"

1

Rongen, Heinz. Echtzeitsystem für Phasenrücksetzanalysen und Neuro-Rückkopplungen am MEG. Jülich: Forschungszentrum Jülich, Zentralbibliothek, 2006.

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2

Gerhardi, Silvia. Real men don't collect soft data. Trento: Dipartimento di Politica Sociale, Universita di Trento, 1988.

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3

Balghabaev, Sultanălī. Dala men darii͡a︡: Povester men ăn͡g︡gīmeler. Almaty: "Zhazushy", 1988.

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4

Spindel, Janis. How to Date Men. New York: Penguin Group USA, Inc., 2008.

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5

Su̇ndetov, Maghzom. Dala men qūm ăuenderī. Almaty: "Zhazushy", 1987.

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6

Statskontoret, Sweden, ed. Försöksverksamhet med PIR: Utvärdering. Stockholm: Statskontoret, 1987.

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7

Torvund, Helge. Multippel og dama med himmelhatten. Oslo: Gyldendal, 1986.

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8

Esenbaĭ, Du̇ĭsenbaev, ed. Dala dausy: Ȯlen͡g︡der men poėmalar. Almaty: Zhazushy, 1987.

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9

Zarra, Marcus S. Core Data. Raleigh, N.C: Pragmatic Bookshelf, 2009.

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10

Beijing Jing Hong En Dian Nao You Xian Gong Si. Dian nao ru men pian. [Beijing]: Qing Hua da xue chu ban she, 2001.

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11

Sage, Lazuardi Adi. Kalah menang SBY-Mega: Data, prediksi, dan analisa populer. Pasar Minggu, Jakarta: Jakarta Citra, 2004.

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12

Lars-Göran, Sund, ed. Familjeägda aktiebolag och generationsskiften: En översikt med empiriska data. 3^a ed. Uppsala: Iustus Förlag, 2010.

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13

Sweden. Datalagen: Med kommentarer. Stockholm: Norstedt, 1989.

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14

Productions, Arnold Shapiro. Date rape/abusive relationships. Chatsworth, CA: Distributed by AIMS Multimedia, 2002.

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15

Kofoed, Lise B. Ting ta'r tid: Planlægning af 16 danske forsøg med informationsteknologi. Aalborg: Aalborg universitetsforlag, 1988.

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16

Brasseur, Christophe. Data management: Qualite des donne es et compe tivite. Paris: Lavoisier, 2005.

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17

Indahyani, Jusuf Ester, Simanjorang Raymond R, Solidaritas Nusa Bangsa (Organization) e Asosiasi Penasehat Hukum dan Hak Asasi Manusia Indonesia., eds. Kerusuhan Mei 1998: Fakta, data, dan analisa : mengungkap kerusuhan Mei 1998 sebagai kejahatan terhadap kemanusiaan. [Jakarta]: Solidaritas Nusa Bangsa (SNB) dan Asosiasi Penasehat Hukum dan Hak Asasi Manusia Indonesia (APHI), 2007.

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18

Nabokov, Vladimir Vladimirovich. Korolʹ, dama, valet. Petrozavodsk: "Karelii︠a︡", 1992.

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19

Nabokov, Vladimir Vladimirovich. Krol, dama, walet. Gdansk: Marabut, 1994.

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20

Jantti, Markus. Matning av Ekonomisk Fattigdom: En oversikt med tillampning pa finskt data. Abo: Abo Akademis, 1990.

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21

Hamalainen, Matti, Risto Ilmoniemi e Lauri Parkkonen. Fundamentals of MEG and EEG: Biophysics, Instrumentation, and Data Analysis. Elsevier Science & Technology Books, 2020.

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22

Hari, MD, PhD, Riitta, e 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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23

Shaikh, Mohd Faraz. Machine Learning in Detecting Auditory Sequences in Magnetoencephalography Data : Research Project in Computational Modelling and Simulation. Technische Universität Dresden, 2021. http://dx.doi.org/10.25368/2022.411.

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Does your brain replay your recent life experiences while you are resting? An open question in neuroscience is which events does our brain replay and is there any correlation between the replay and duration of the event? In this study I tried to investigate this question by using Magnetoencephalography data from an active listening experiment. Magnetoencephalography (MEG) is a non-invasive neuroimaging technique used to study the brain activity and understand brain dynamics in perception and cognitive tasks particularly in the fields of speech and hearing. It records the magnetic field generated in our brains to detect the brain activity. I build a machine learning pipeline which uses part of the experiment data to learn the sound patterns and then predicts the presence of sound in the later part of the recordings in which the participants were made to sit idle and no sound was fed. The aim of the study of test replay of learned sound sequences in the post listening period. I have used classification scheme to identify patterns if MEG responses to different sound sequences in the post task period. The study concluded that the sound sequences can be identified and distinguished above theoretical chance level and hence proved the validity of our classifier. Further, the classifier could predict the sound sequences in the post-listening period with very high probability but in order to validate the model results on post listening period, more evidence is needed.

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24

Hari, Riitta. Magnetoencephalography. Editado por Donald L. Schomer e Fernando H. Lopes da Silva. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780190228484.003.0035.

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This chapter introduces magnetoencephalography (MEG), a tool to study brain dynamics in basic and clinical neuroscience. MEG picks up brain signals with millisecond resolution, as does electroencephalography, but without distortion by skull and scalp. The chapter describes current instrumentation based on superconducting quantum interference devices (SQUIDs). It delineates basic characteristics of measured signals: (1) brain rhythms and their reactivity during sensory processing and various tasks and (2) evoked responses elicited by sensory stimuli, and the dependence of these responses on various stimulus characteristics. Signals are described from healthy and diseased brains. The chapter presents studies of the brain basis of cognition and social interaction studied in dual-MEG setups and describes how MEG applications can be broadened by innovative setups, including frequency tagging. Progress in the field is predicted regarding sensor technology, data analysis, and multimodal brain imaging, all of which could strengthen MEG’s role in the study of brain dynamics.

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25

Great Britain: Parliament: House of Commons: Home Affairs Committee e Keith Vaz. ID Cards: Data security issues, oral and written evidence, Tuesday 26 February 2008, Meg Hillier MP, Parliamentary under-Secretary of State, Home Office and Dr Duncan Hine, Executive Director of Integrity and Security, Identity and Passport Service. Stationery Office, The, 2008.

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26

Chernock, Richard S., Regis J. Crinon, Michael A. Dolan, Jr John R. Mick, Regis Crinon e Richard Chernock. Data Broadcasting: Understanding the ATSC Data Broadcast Standard. McGraw-Hill Professional, 2001.

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27

Ray, Sumantra (Shumone), Sue Fitzpatrick, Rajna Golubic, Susan Fisher e Sarah Gibbings, eds. Data management. Oxford University Press, 2016. http://dx.doi.org/10.1093/med/9780199608478.003.0020.

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28

O'Cathain, Alicia. Collecting data. Editado por Alicia O'Cathain. Oxford University Press, 2018. http://dx.doi.org/10.1093/med/9780198802082.003.0009.

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There are many issues to consider when collecting data in qualitative research with RCTs. In this chapter consideration is given to the range of methods that can be used, who to include in the data collection, how to identify people or places to include, how many people or places to include, when to undertake data collection, and who is best placed to undertake it.

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29

Gibson, Charles, e Fred Roberts. Anaesthesia data. Oxford University Press, 2018. http://dx.doi.org/10.1093/med/9780198719410.003.0044.

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This final chapter contains a selection of useful information for the anaesthetist, gathered together for convenience and for the aid of revision in examinations. It contains the American Society of Anesthesiologists classification, the (National) Confidential Enquiry into Patient Outcome and Death classification, the Mapleson classification of breathing systems, a discussion of pulmonary function tests and their normal values, cardiovascular physiology data, the Glasgow Coma Scale, and a series of useful anaesthetic equations and definitions. It concludes with a table of normal values, a list of useful websites, and a checklist for anaesthetic equipment.

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30

Thomas, James, e Tanya Monaghan. Clinical data interpretation. Oxford University Press, 2018. http://dx.doi.org/10.1093/med/9780199593972.003.0019.

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31

O'Cathain, Alicia. Analysing the qualitative data. Editado por Alicia O'Cathain. Oxford University Press, 2018. http://dx.doi.org/10.1093/med/9780198802082.003.0010.

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Numerous books and journal articles have been written about how to analyse qualitative data. In this chapter the assumption has been made that readers know how to do this. The focus of the chapter is on how researchers have analysed qualitative data collected with RCTs. The chapter begins with a brief description of the range of approaches to analysis used in the context of qualitative research with RCTs, giving examples of how researchers describe their analysis. Consideration is then given to who can be involved in the analysis and the challenges researchers may face.

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32

Kaski, Juan Carlos, Carl Hayward, Saagar Mahida, Stuart Baker, Teck Khong e Juan Tamargo, eds. Drugs in Cardiology. Oxford University Press, 2010. http://dx.doi.org/10.1093/med/9780199557462.001.0001.

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This practical resource, organised both by clinical condition and specific drug, contains pharmacological data and up-to-date evidence for the clinical uses of each drug in an easily digestible and accessible format.

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33

Deal, Norma. Golf Data Journal: National Men Health Month. Independently Published, 2020.

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34

(Narrator), Otto Haugland, e Julie Schaller (Narrator), eds. Why Men Don't Date. Listen & Live Audio, 1997.

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35

Haugland, Otto. Why Men Don't Date. Hara Publishing Group, 1996.

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36

Peacock, Janet L., Sally M. Kerry e Raymond R. Balise. Analysing matched or paired data. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780198779100.003.0008.

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Chapter 8 covers analysing matched or paired data, and includes the paired t test, non-Normal data, matched case-control data, and cohort data. It describes the use of data transformations and how results are interpreted when data are paired. It includes how to calculate 95% confidence intervals for estimates. The chapter includes analyses using Stata, SAS, SPSS, and R.

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37

Peacock, Janet L., e Sally M. Kerry. Analysing matched or paired data. Oxford University Press, 2013. http://dx.doi.org/10.1093/med/9780198599661.003.0008.

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38

Brazier, John, Julie Ratcliffe, Joshua A. Salomon e Aki Tsuchiya. Modelling health state valuation data. Oxford University Press, 2016. http://dx.doi.org/10.1093/med/9780198725923.003.0005.

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This chapter examines the technical issues in modelling health state valuation data. Most measures of health define too many states to directly value all of them (e.g. SF-6D defines 18,000 health states). The solution has been to value a subset and by using modelling to predict the values of all states. This chapter reviews two approaches to modelling: one using multiattribute utility theory to determine health values given an assumed functional form; and the other is using statistical modelling of SF-6D preference data that are skewed, bimodal, and clustered by respondents. This chapter examines the selection of health states for valuation, data preparation, model specification, and techniques for modelling the data starting with ordinary least squares (OLS) and moving on to more complex techniques including Bayesian non-parametric and semi-parametric approaches, and a hybrid approach that combines cardinal preference data with the results of paired data from a discrete choice experiment.

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39

Smith, Thomas J., e J. Brian Cassel. The economic challenges of palliative medicine. Oxford University Press, 2015. http://dx.doi.org/10.1093/med/9780199656097.003.0010.

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The compelling reasons for palliative care continue to be better symptom management, better advanced care planning and medically appropriate goal setting, and transitions to hospice care. Other new-found compelling reasons include better survival with hospice care, better survival with concurrent palliative care, and lower cost to hospitals and government and insurance funders. In studies to date, hospice and palliative care have been associated with equal or better survival and equal or lower cost. This chapter defines the various types of cost and clinical studies (including cost minimization, cost-effectiveness, cost utility analysis, cost-benefit, cost avoidance) and discusses the available data about the economic challenges of palliative care, how to apply the available data, how to collect and present some useful and useable data, and new directions for research.

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40

Robinson, Leetta. Aamc Data Book: Statistical Information (Aamc Data Book: Stat Info Rel to Med Educ). Association of American Medical Colleges, 2005.

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41

Journaling, Abducent Mood. Best Data Scientist Ever: Data Scientist Notebook / Journal, Appreciation Gift Idea for Women, Men. Independently Published, 2020.

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42

Smedley, Julia, Finlay Dick e Steven Sadhra. Skin disorders. Oxford University Press, 2013. http://dx.doi.org/10.1093/med/9780199651627.003.0008.

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Dermatitis 1 242Dermatitis 2: management 244Contact urticaria 246Skin cancers 247Skin pigmentation disorders 248Folliculitis and acne 249Photodermatitis 250Scleroderma 251Occupational skin infections 252Prevalence data1 suggest that 20000 people in the UK have skin problems that are caused or made worse by work. Occupational dermatitis (OD) makes up the greatest proportion of these. Data come from two main sources: ...

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43

McDermott, Geree. Men I Date: Dating Journal. Independently Published, 2021.

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44

Daka tikatom mem Bharata darsana. Nai Dilli: Nesanala Buka Trasta, 2010.

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45

Why Christian men don't date. Mukilteo, WA: WinePress Pub., 1996.

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46

Expiration Date (Men at Work). Harlequin Books, 1998.

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47

Wingard, Nanette. Broken Men/Women Shouldn't Date. Trilogy Christian Publishing, Inc., 2021.

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48

Wingard, Nanette. Broken Men/Women Shouldn't Date. Trilogy Christian Publishing, Inc., 2021.

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49

Young, Forrest W., Pedro M. Valero-Mora e Michael Friendly. Visual Statistics: Seeing Data with Dynamic Interactive Graphics. Wiley & Sons, Incorporated, John, 2011.

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50

Roberson, Blythe. How to Date Men When You Hate Men. Flatiron Books, 2019.

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Livros sobre o tema "MEG data"

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