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Libros sobre el tema "ELECTROENCEPHALOGRAPHY SIGNAL"

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Autor: Grafiati
Publicado: 11 de septiembre de 2023

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1

C, Handy Todd, ed. Brain signal analysis: Advances in neuroelectric and neuromagnetic methods. Cambridge, MA: The MIT Press, 2009.

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2

Genquan, Feng. EKG and EEG multiphase information analysis. [New York]: American Medical Publishers, 1992.

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3

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

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4

Isak, Gath y Inbar Gideon F, eds. Advances in processing and pattern analysis of biological signals. New York: Plenum Press, 1996.

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5

1962-, Laguna Pablo, ed. Bioelectrical signal processing in cardiac and neurological applications. Amsterdam: Elsevier Academic Press, 2005.

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6

M, Dasheiff R. y Vincent D. J, eds. Continuous wave-form analysis. Amsterdam: Elsevier, 1996.

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7

Statistical signal processing for neuroscience and neurotechnology. Burlington, MA: Academic Press, 2011.

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8

Brette, Romain y Alain Destexhe. Handbook of neural activity measurement. Cambridge: Cambridge University Press, 2012.

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9

Freeman, Walter J. Imaging Brain Function With EEG: Advanced Temporal and Spatial Analysis of Electroencephalographic Signals. New York, NY: Springer New York, 2013.

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10

O, Quadens y European Space Agency, eds. Analysis of EEG signals recorded in microgravity during parabolic flight using the method of strange attractor dimensions. Noordwijk, The Netherlands: ESA, 1999.

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11

S, Gevins A. y Rémond Antoine, eds. Methods of analysis of brain electrical and magnetic signals. Amsterdam: Elsevier, 1987.

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12

Koutroumanidis, Michalis, Dimitrios Sakellariou y Vasiliki Tsirka. Electroencephalography. Oxford University Press, 2016. http://dx.doi.org/10.1093/med/9780199688395.003.0011.

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This chapter concentrates on essential technical aspects of the electroencephalogram (EEG) and its role in the clinical and aetiological diagnosis of people with epilepsy. The technical subsection explores important stages of the largely ‘mystifying’ process from the generation of the abnormal signals in the brain to their final visualization on the screen, including digitalization of the signal and sampling rate, montages, and derivations, focusing on their clinical relevance. The second part reviews the behavioural attributes of the interictal and ictal discharges in the different epilepsy types and syndromes, discusses the optimal use of activation methods, including sleep deprivation and sleep, hyperventilation, photic, and other specific stimulation, and describes specific diagnostic tools like polygraphy and cognitive assessment during apparently subclinical discharges. It also discusses aspects of the clinical EEG interpretation and reporting and delineates indications and limitations of the EEG.
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13

Sanei, Saeid y Jonathon A. Chambers. EEG Signal Processing. Wiley & Sons, Incorporated, John, 2013.

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14

Chambers, Jonathon y Saeid Sanei. Eeg Signal Processing. John Wiley & Sons Inc, 2007.

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15

Zhang, Yanchun, Yan Li y Siuly Siuly. EEG Signal Analysis and Classification: Techniques and Applications. Springer International Publishing AG, 2017.

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16

Zhang, Yanchun, Yan Li y Siuly Siuly. EEG Signal Analysis and Classification: Techniques and Applications. Springer, 2018.

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17

Zhang, Yanchun, Yan Li y Siuly Siuly. EEG Signal Analysis and Classification: Techniques and Applications. Springer, 2017.

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18

Leong, Wai Yie. EEG Signal Processing: Feature extraction, selection and classification methods. The Institution of Engineering and Technology, 2019.

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19

Leong, Wai Yie. EEG Signal Processing: Feature Extraction, Selection and Classification Methods. Institution of Engineering & Technology, 2019.

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20

Brain Source Localization Using EEG Signal Analysis. Taylor & Francis Group, 2017.

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21

Kamel, Nidal y Munsif Ali Jatoi. Brain Source Localization Using EEG Signal Analysis. Taylor & Francis Group, 2017.

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22

Dehuri, Satchidananda, Alok Kumar Jagadev, Shruti Mishra y Sandeep Kumar Satapathy. EEG Brain Signal Classification for Epileptic Seizure Disorder Detection. Elsevier Science & Technology Books, 2019.

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23

Dehuri, Satchidananda, Alok Kumar Jagadev, Shruti Mishra y Sandeep Kumar Satapathy. EEG Brain Signal Classification for Epileptic Seizure Disorder Detection. Elsevier Science & Technology, 2019.

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24

Durka, Piotr. Matching Pursuit and Unification in EEG Analysis (Engineering in Medicine & Biology). Artech House, 2007.

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25

Handy, Todd C., Douglas Cheyne, Sam M. Doesburg y John J. Foxe. Brain Signal Analysis: Advances in Neuroelectric and Neuromagnetic Methods. MIT Press, 2009.

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26

Handy, Todd C., Douglas Cheyne, Sam M. Doesburg y John J. Foxe. Brain Signal Analysis: Advances in Neuroelectric and Neuromagnetic Methods. MIT Press, 2009.

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27

Hsu, Hui-Huang. Neural networks for signal and information processing. 1994.

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28

(Editor), Rajendra Acharya U., Jasjit S. Suri (Editor), Jos A.E. Spaan (Editor) y S. M. Krishnan (Editor), eds. Advances in Cardiac Signal Processing. Springer, 2007.

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29

Astolfi, Laura y Fabio Babiloni. Estimation of Cortical Connectivity in Humans: Advanced Signal Processing Techniques. Springer International Publishing AG, 2007.

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30

Astolfi, Laura y Fabio Babiloni. Estimation of Cortical Connectivity in Humans: Advanced Signal Processing Techniques. Morgan & Claypool Publishers, 2007.

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31

Lee, Cheoung Nam. Expert system design and implementation for multichannel sleep EEG signal processing. 1985.

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32

EEG Signal Processing. Wiley-Interscience, 2007.

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33

Sanei, Saeid y Jonathon A. Chambers. EEG Signal Processing. Wiley & Sons, Incorporated, John, 2013.

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34

Sanei, Saeid y Jonathon A. Chambers. EEG Signal Processing. Wiley & Sons, Incorporated, John, 2008.

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35

Kamel, Nidal y Munsif Ali Jatoi. Brain Source Localization Using EEG Signal Analysis. Taylor & Francis Group, 2017.

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36

Kamel, Nidal y Munsif Ali Jatoi. Brain Source Localization Using EEG Signal Analysis. Taylor & Francis Group, 2017.

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37

Kamel, Nidal y Munsif Ali Jatoi. Brain Source Localization Using EEG Signal Analysis. Taylor & Francis Group, 2017.

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38

Kamel, Nidal y Munsif Ali Jatoi. Brain Source Localization Using EEG Signal Analysis. Taylor & Francis Group, 2017.

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39

Astofli, Laura y Fabio Bablioni. Advanced Signal Processing Techniques for the Estimation of Cortical Connectivity in Humans (Synthesis Lectures on Biomedical Engineering). Not Avail, 2008.

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40

Oweiss, Karim G. Statistical Signal Processing for Neuroscience and Neurotechnology. Elsevier Science & Technology Books, 2010.

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41

Sanei, Saeid. Adaptive Processing of Brain Signals. Wiley & Sons, Limited, John, 2013.

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42

Sanei, Saeid. Adaptive Processing of Brain Signals. Wiley & Sons, Incorporated, John, 2013.

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43

Adaptive Processing Of Brain Signals. John Wiley and Sons Ltd, 2013.

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44

Signal Processing and Machine Learning for Brain-Machine Interfaces. The Institution of Engineering and Technology, 2018.

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45

Legatt, Alan D., Marc R. Nuwer y Ronald G. Emerson. Intraoperative Monitoring of Central Neurophysiology. Editado por Donald L. Schomer y Fernando H. Lopes da Silva. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780190228484.003.0034.

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This chapter covers neurophysiological intraoperative monitoring (NIOM). It describes the relevant neurophysiological signals, their anatomical sources, the techniques used to record them, the manner in which they are assessed, and possible causes of intraoperative signal changes. Techniques used include electroencephalography (EEG), electromyography, and auditory, somatosensory, and motor evoked potentials. Some of these techniques can be used to localize and identify areas of cerebral cortex or the corticospinal tract. Recording of the electromyogram generated by reflex activity can be used to evaluate central nervous system function in some circumstances. EEG can be used to assess depth of anesthesia. Signals can be affected by anesthesia, and the chapter discusses various anesthetic agents, their effects on signals, and considerations for anesthetic management during NIOM. Personnel performing NIOM must be knowledgeable about the anatomy and physiology underlying the signals, the technology used to record them, and the factors (including anesthesia) that can affect them.
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46

Seeck, Margitta, L. Spinelli, Jean Gotman y Fernando H. Lopes da Silva. Combination of Brain Functional Imaging Techniques. Editado por Donald L. Schomer y Fernando H. Lopes da Silva. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780190228484.003.0046.

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Several tools are available to map brain electrical activity. Clinical applications focus on epileptic activity, although electric source imaging (ESI) and electroencephalography-coupled functional magnetic resonance imaging (EEG–fMRI) are also used to investigate non-epileptic processes in healthy subjects. While positron-emission tomography (PET) reflects glucose metabolism, strongly linked with synaptic activity, and single-photon-emission computed tomography (SPECT) reflects blood flow, fMRI (BOLD) signals have a hemodynamic component that is a surrogate signal of neuronal (synaptic) activity. The exact interpretation of BOLD signals is not completely understood; even in unifocal epilepsy, more than one region of positive or negative BOLD is often observed. Co-registration of medical images is essential to answer clinical questions, particularly for presurgical epilepsy evaluations. Multimodal imaging can yield information about epileptic foci and underlying networks. Co-registering MRI, PET, SPECT, fMRI, and ESI (or magnetic source imaging) provides information to estimate the epileptogenic zone and can help optimize surgical results.
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47

Advanced Signal Processing on Event-Related Potentials: Filtering Erps in Time, Frequency and Space Domains Sequentially and Simultaneously. World Scientific Publishing Co Pte Ltd, 2015.

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48

Sanei, Saeid. Adaptive Processing of Brain Signals. Wiley & Sons, Incorporated, John, 2013.

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49

Sanei, Saeid. Adaptive Processing of Brain Signals. Wiley & Sons, Incorporated, John, 2013.

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50

Sanei, Saeid. Adaptive Processing of Brain Signals. Wiley & Sons, Incorporated, John, 2013.

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