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Books on the topic 'Eeg'

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Author: Grafiati

Published: 4 June 2021

Last updated: 14 September 2024

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1

Lutzenberger, Werner, Thomas Elbert, Brigitte Rockstroh, and Niels Birbaumer. Das EEG. Berlin, Heidelberg: Springer Berlin Heidelberg, 1985. http://dx.doi.org/10.1007/978-3-662-06459-7.

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2

Mulert, Christoph, and Louis Lemieux, eds. EEG - fMRI. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-540-87919-0.

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3

Mulert, Christoph, and Louis Lemieux, eds. EEG - fMRI. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-07121-8.

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4

National Institutes of Health (U.S.). Office of Clinical Center Communications, ed. EEG (electroencephalogram). [Bethesda, Md.?]: Clinical Center Communications, National Institutes of Health, 1989.

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5

Axmacher, Nikolai, ed. Intracranial EEG. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-20910-9.

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6

Mark, Quigg, ed. EEG pearls. Philadelphia: Mosby Elsevier, 2006.

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7

Tatum, William O., ed. Ambulatory EEG Monitoring. New York, NY: Springer Publishing Company, 2017. http://dx.doi.org/10.1891/9781617052781.

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8

Sanei, Saeid, and J. A. Chambers. EEG Signal Processing. West Sussex, England: John Wiley & Sons Ltd,, 2007. http://dx.doi.org/10.1002/9780470511923.

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9

Im, Chang-Hwan, ed. Computational EEG Analysis. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-0908-3.

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10

Kursawe, Hubertus. Übungsbuch Klinisches EEG. Berlin, Heidelberg: Springer Berlin Heidelberg, 2018. http://dx.doi.org/10.1007/978-3-662-56756-2.

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11

Husain, Aatif M., and Saurabh R. Sinha, eds. Continuous EEG Monitoring. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-31230-9.

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12

S, Ebersole John, ed. Ambulatory EEG monitoring. New York: Raven Press, 1989.

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13

Sanei, Saeid. EEG signal processing. Chichester: John Wiley & Sons, 2007.

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14

Dyro, Frances M. The EEG handbook. Boston: Little, Brown, 1989.

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15

1931-, Spehlmann Rainer, ed. Spehlmann's EEG primer. 2nd ed. Amsterdam: Elsevier, 1991.

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16

Bredow, Hartwig Freiherr von. Biogasanlagen im EEG. 2nd ed. Berlin: E. Schmidt, 2011.

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17

Stern, John M. Atlas of EEG patterns. Philadelphia: Lippincott Williams & Wilkins, 2005.

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18

Tatum, William O., ed. Handbook of EEG Interpretation. 3rd ed. New York, NY: Springer Publishing Company, 2021. http://dx.doi.org/10.1891/9780826147097.

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19

Knösche, Thomas R., and Jens Haueisen. EEG/MEG Source Reconstruction. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-74918-7.

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20

Pichlmayr, Ina. EEG-Atlas für Anästhesisten. Berlin, Heidelberg: Springer Berlin Heidelberg, 1985. http://dx.doi.org/10.1007/978-3-662-06833-5.

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21

Pichlmayr, Ina, and Peter Lehmkuhl. EEG -Überwachung des Intensivpatienten. Berlin, Heidelberg: Springer Berlin Heidelberg, 1988. http://dx.doi.org/10.1007/978-3-642-83288-8.

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22

Schmitt, Bernhard, and Gabriele Wohlrab. EEG in der Neuropädiatrie. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-39887-2.

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23

Pichlmayr, Ina, Peter Lehmkuhl, and Ulrich Lips. EEG Atlas for Anesthesiologists. Berlin, Heidelberg: Springer Berlin Heidelberg, 1987. http://dx.doi.org/10.1007/978-3-642-83161-4.

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24

Tatum, William O., ed. Handbook of EEG Interpretation. New York, NY: Springer Publishing Company, 2014. http://dx.doi.org/10.1891/9781617051807.

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25

Erlichman, Martin. Electroencephalographic (EEG) video monitoring. Rockville, MD: U.S. Dept. of Health and Human Services, Public Health Service, Agency for Health Care Policy and Research, 1990.

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26

H, Chiappa Keith, ed. The EEG of drowsiness. New York: DEMOS Publications, 1987.

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27

Pichlmayr, I. EEG atlas for anesthesiologists. Berlin: Springer-Verlag, 1987.

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28

Tyner, Fay S. Fundamentals of EEG technology. New York: Raven Press, 1989.

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29

R, Hughes John. EEG in clinical practice. 2nd ed. Boston: Butterworth-Heinemann, 1994.

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30

EEG. 2016.

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31

Eeg. Quercus, 2021.

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32

EEG. Quercus, 2018.

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33

Axmacher, Nikolai. Intracranial EEG. Springer International Publishing AG, 2023.

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34

EEG Pearls. Elsevier, 2006. http://dx.doi.org/10.1016/b0-323-04233-3/x1000-x.

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35

Wendling, Fabrice, Marco Congendo, and Fernando H. Lopes da Silva. EEG Analysis. Edited by Donald L. Schomer and Fernando H. Lopes da Silva. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780190228484.003.0044.

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This chapter addresses the analysis and quantification of electroencephalographic (EEG) and magnetoencephalographic (MEG) signals. Topics include characteristics of these signals and practical issues such as sampling, filtering, and artifact rejection. Basic concepts of analysis in time and frequency domains are presented, with attention to non-stationary signals focusing on time-frequency signal decomposition, analytic signal and Hilbert transform, wavelet transform, matching pursuit, blind source separation and independent component analysis, canonical correlation analysis, and empirical model decomposition. The behavior of these methods in denoising EEG signals is illustrated. Concepts of functional and effective connectivity are developed with emphasis on methods to estimate causality and phase and time delays using linear and nonlinear methods. Attention is given to Granger causality and methods inspired by this concept. A concrete example is provided to show how information processing methods can be combined in the detection and classification of transient events in EEG/MEG signals.

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36

Quigg, Mark. EEG Pearls. Mosby, 2006.

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37

EEG: HANDKOMMENTAR. Nomos Verlagsgesellschaft, 2020.

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38

Goldensohn, Eli S. Eeg Interpretation. Wiley & Sons, Incorporated, John, 1997.

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39

Osselton, J. W., R. Cooper, and J. C. Shaw. EEG Technology. Elsevier Science & Technology Books, 2014.

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40

Gabler, Andreas, and Reinald Gunther. Eeg: Handkommentar. Nomos Verlagsgesellschaft, 2023.

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41

Neundörfer, Bernhard, and Klaus Witzel. EEG - Fibel. Das EEG in der ärztlichen Praxis. Urban & Fischer, 2002.

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42

Erneuerbare-Energien-Gesetz 2020: EEG - EEV - GEEV. Independently Published, 2020.

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43

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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44

EEG and Epilepsy. Jaypee Brothers Medical Publishers (P) Ltd., 2015. http://dx.doi.org/10.5005/jp/books/12478.

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45

Detsche, Hellmuth, Susan C. Etlinger, and Hellmuth Petsche. EEG and Thinking. Austrian Academy of Sciences,Austria, 1998.

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46

Shafi, Mouhsin M., and M. Brandon Westover. EEG Activation Methods. Edited by Donald L. Schomer and Fernando H. Lopes da Silva. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780190228484.003.0010.

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Activation procedures are commonly employed to increase the diagnostic yield of electroencephalography (EEG) in patients with suspected epilepsy. This chapter reviews the effects and utility of hyperventilation, intermittent photic stimulation, and color/pattern stimulation on the EEG in patients with epilepsy and other neurological disorders. In theory, the greater the number of different activation methods used in EEG evaluation of an epilepsy patient, the greater the chance of obtaining abnormal findings. However, the specificity of these findings for epilepsy is uncertain. Furthermore, from a practical point of view, desirable activations are those methods that can be carried out easily and systematically, in a short time frame, with affordable equipment, without undesirable side effects for patients, and with reliable and predictive results. At this time, hyperventilation and intermittent photic stimulation are the most widely used activation methods and have an extensive body of literature supporting them.

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47

Seeck, Margitta, and Donald L. Schomer. Intracranial EEG Monitoring. Edited by Donald L. Schomer and Fernando H. Lopes da Silva. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780190228484.003.0029.

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Intracranial electroencephalography (iEEG) is used to localize the focus of seizures and determine vital adjacent cortex before epilepsy surgery. The two most commonly used electrode types are subdural and depth electrodes. Foramen ovale electrodes are less often used. Combinations of electrode types are possible. The choice depends on the presumed focus site. Careful planning is needed before implantation, taking into account the results of noninvasive studies. While subdural recordings allow better mapping of functional cortex, depth electrodes can reach deep structures. There are no guidelines on how to read ictal intracranial EEG recordings, but a focal onset (<5 contacts) and a high-frequency onset herald a good prognosis. High-frequency oscillations have been described as a potential biomarker of the seizure onset zone. Intracranial recordings provide a focal but magnified view of the brain, which is also exemplified by the use of microelectrodes, which allow the recording of single-unit or multi-unit activity.

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48

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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49

Nidal, Kamel, and Aamir Saeed Malik, eds. EEG/ERP Analysis. CRC Press, 2014. http://dx.doi.org/10.1201/b17605.

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50

Nundy, Amitabh. Neuroscience EEG Atlas. Jaypee Brothers Medical Publishers (P) Ltd., 2016. http://dx.doi.org/10.5005/jp/books/12724.

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