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Books on the topic 'Electroencephalographic (EEG)'

Author: Grafiati
Published: 11 March 2023

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1

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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2

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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3

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

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4

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

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5

Fisch, Bruce J. Fisch and Spehlmann's EEG primer: Basic principles of digital and analog EEG. 3rd ed. Amsterdam: Elsevier, 1999.

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6

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

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7

1933-, Zschocke S., and Speckmann Erwin-Josef, eds. Basic mechanisms of the EEG. Boston: Birkhäuser, 1993.

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8

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

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9

Rajna, P. The EEG atlas of adulthood epilepsy. [Budapest]: Innomark, 1990.

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10

Eugene, Tolunsky, ed. A primer of EEG: With a mini-atlas. Philadelphia, PA: Butterworth-Heinemann, 2003.

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11

International Workshop on Statistics and Topographic Problems in Quantitative EEG. (1988 Rouen, France). Statistics and topography in quantitative EEG: Proceedings of the International Workshop on Statistics and Topographic Problems in Quantitative EEG, Rouen, France, March 6-9, 1988. Edited by Samson-Dollfus D. Amsterdam: Elsevier, 1988.

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12

Laoprasert, Pramote. Atlas of pediatric EEG. New York: McGraw-Hill Companies, Inc., 2011.

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13

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

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14

M, Rother, and Zwiener Ulrich, eds. Quantitative EEG analysis: Clinical utility and new methods. Jena: Universitätsverlag Jena, 1993.

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15

Abou-Khalil, Bassel. Atlas of EEG & seizure semiology. Philadelphia: Butterworth-Heinemann/Elsevier, 2006.

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16

Mecklinger, Axel. Gedächtnissuchprozesse: Eine Analyse ereigniskorrelierter Potentiale und der EEG-Spontanaktivität. Weinheim: Psychologie Verlags Union, 1992.

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17

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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18

Kanner, Andres M., and Adriana Bermeo-Ovalle. EEG in Psychiatric Disorders. Edited by Donald L. Schomer and Fernando H. Lopes da Silva. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780190228484.003.0025.

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Psychiatric symptoms are not restricted to primary psychiatric disorders and are relatively frequent in medical and neurological disorders. They may represent the clinical manifestations of these disorders, of a comorbid psychiatric disorder, or of iatrogenic complications of pharmacological and/or surgical therapies. Clearly, proper diagnosis is of the essence to provide the correct treatment. Electroencephalographic (EEG) studies are used on a regular basis to identify a potential organic cause of psychiatric symptomatology. This chapter reviews the diagnostic yield of EEG recordings in psychiatric symptomatology associated with primary psychiatric disorders, with neurological and medical conditions, and in particular with epilepsy, and provides suggestions on the optimal use of the different types of EEG recordings in clinical practice.
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19

Beniczky, Sándor, Harald Aurlien, Jan Brøgger, Ronit Pressler, and Lawrence J. Hirsch. Standardizing EEG Interpretation and Reporting. Edited by Donald L. Schomer and Fernando H. Lopes da Silva. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780190228484.003.0026.

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This chapter describes how to standardize electroencephalographic (EEG) interpretation and reporting in clinical practice. The Standardized Computer-Based Organized Reporting of EEG (SCORE) software program was developed by an international taskforce under the auspices of the International League Against Epilepsy and the International Federation of Clinical Neurophysiology. Clinically relevant features are scored by choosing predefined terms in the software. This process automatically generates a report and at the same time builds up a database for education, quality assurance, and research. SCORE is the template used for the interactive online educational EEG platform of this textbook.
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20

Quiroga, Rodrigo Quian, and Walter Freeman. Imaging Brain Function With EEG: Advanced Temporal and Spatial Analysis of Electroencephalographic Signals. Springer, 2014.

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21

Babiloni, Claudio, Claudio Del Percio, and Ana Buján. EEG in Dementing Disorders. Edited by Donald L. Schomer and Fernando H. Lopes da Silva. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780190228484.003.0016.

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This chapter reviews the most relevant literature on qualitative and quantitative abnormalities in resting-state eyes-closed electroencephalographic (rsEEG) rhythms recorded in patients with dementing disorders due to Alzheimer’s disease, frontotemporal lobar degeneration, vascular disease, Parkinson’s disease, Lewy body disease, human immunodeficiency virus infection, and prion disease, mainly Creutzfeldt–Jakob disease. This condition of quiet wakefulness is the most used in clinical practice, as it involves a simple, innocuous, quick, noninvasive, and cost-effective procedure that can be repeated many times without effects of stress, learning, or habituation. While rsEEG has a limited diagnostic value (not reflecting peculiar pathophysiological processes directly), delta, theta, and alpha rhythms might be promising candidates as “topographical markers” for the prognosis and monitoring of disease evolution and therapy response, at least for the most diffuse dementing disorders. More research is needed before those topographical biomarkers can be proposed for routine clinical applications.
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22

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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23

Pfurtscheller, Gert, Clemens Brunner, and Christa Neuper. EEG-Based Brain–Computer Interfaces. Edited by Donald L. Schomer and Fernando H. Lopes da Silva. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780190228484.003.0047.

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A brain–computer interface (BCI) offers an alternative to natural communication and control by recording brain activity, processing it online, and producing control signals that reflect the user’s intent or the current user state. Therefore, a BCI provides a non-muscular communication channel that can be used to convey messages and commands without any muscle activity. This chapter presents information on the use of different electroencephalographic (EEG) features such as steady-state visual evoked potentials, P300 components, event-related desynchronization, or a combination of different EEG features and other physiological signals for EEG-based BCIs. This chapter also reviews motor imagery as a control strategy, discusses various training paradigms, and highlights the importance of feedback. It also discusses important clinical applications such as spelling systems, neuroprostheses, and rehabilitation after stroke. The chapter concludes with a discussion on different perspectives for the future of BCIs.
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24

Galovic, Marian, Bettina Schmitz, and Barbara Tettenborn. EEG in Inflammatory Disorders, Cerebrovascular Diseases, Trauma, and Migraine. Edited by Donald L. Schomer and Fernando H. Lopes da Silva. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780190228484.003.0015.

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This chapter describes electroencephalographic (EEG) abnormalities in inflammatory and cerebrovascular diseases, traumatic brain injury, and headache. It focuses on a practical and clinical approach and covers the most important diseases from this extensive field. Particular attention has been paid to viral and autoimmune encephalitis, prion disease, ischemic stroke, posttraumatic coma, and migraine. Several signature patterns are discussed that facilitate early and accurate diagnosis. The use of EEG in guiding treatment decisions and predicting prognosis is reviewed.
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25

Osman, Gamaleldin M., James J. Riviello, and Lawrence J. Hirsch. EEG in the Intensive Care Unit. Edited by Donald L. Schomer and Fernando H. Lopes da Silva. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780190228484.003.0022.

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The field of continuous electroencephalographic monitoring (cEEG) in the intensive care unit has dramatically expanded over the past two decades. Expansion of cEEG programs led to recognition of the frequent occurrence of electrographic seizures, and complex rhythmic and periodic patterns in various critically ill populations. The majority of electrographic seizures are of nonconvulsive nature, hence the need for cEEG for their identification. Guidelines on when and how to perform cEEG and standardized nomenclature for description of rhythmic and periodic patterns are now available. Quantitative EEG analysis methods depict EEG data in a compressed (hours on one screen) colorful graphical representation, facilitating early identification of key events, recognition of slow, long-term trends, and timely therapeutic intervention. Integration of EEG with other invasive and noninvasive modalities of monitoring brain function provides critical information about the development of secondary neuronal injury, providing a valuable window of opportunity for intervention before irreversible damage ensues.
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26

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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27

Kalitzin, Stiliyan, and Fernando Lopes da Silva. EEG-Based Anticipation and Control of Seizures. Edited by Donald L. Schomer and Fernando H. Lopes da Silva. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780190228484.003.0023.

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Early seizure-prediction paradigms were based on detecting electroencephalographic (EEG) features, but recent approaches are based on dynamic systems theory. Methods that attempted to detect predictive features during the preictal period proved difficult to validate in practice. Brain systems can display bistability (both normal and epileptic states can coexist), and the transitions between states may be initiated by external or internal dynamic factors. In the former case prediction is impossible, but in the latter case prediction is conceivable, leading to the hypothesis that as seizure onset approaches, the excitability of the underlying neuronal networks tends to increase. This assumption is being explored using not only the ongoing EEG but also active probes, applying appropriate stimuli to brain areas to estimate the excitability of the neuronal populations. Experimental results support this assumption, suggesting that it may be possible to develop paradigms to estimate the risk of an impending transition to an epileptic state.
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28

Nuwer, Marc R., Ronald G. Emerson, and Cecil D. Hahn. Principles and Techniques for Long-Term EEG Recording (Epilepsy Monitoring Unit, Intensive Care Unit, Ambulatory). Edited by Donald L. Schomer and Fernando H. Lopes da Silva. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780190228484.003.0031.

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Long-term monitoring is a set of methods for recording electroencephalographic (EEG) signals over a period of 24 hours or longer. Patient video recording is often synchronized to the EEG. Interpretation aids help physicians to identify events, which include automated spike and seizure detection and various trending displays of frequency EEG content. These techniques are used in epilepsy monitoring units for presurgical evaluations and differential diagnosis of seizures versus nonepileptic events. They are used in intensive care units to identify nonconvulsive seizures, to measure the effectiveness of therapy, to assess depth and prognosis in coma, and other applications. The patient can be monitored at home with ambulatory monitoring equipment. Specialized training is needed for competent interpretation of long-term monitoring EEGs. Problems include false-positive events flagged by automated spike and seizure detection software, and muscle and movement artifact contamination during seizures.
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29

Tatum, William O., Claus Reinsberger, and Barbara A. Dworetzky. Artifacts of Recording and Common Errors in Interpretation. Edited by Donald L. Schomer and Fernando H. Lopes da Silva. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780190228484.003.0011.

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This chapter examines the fundamental neurophysiological principles involved in determining electroencephalographic (EEG) artifact and provides general instructions for minimizing the risk of error during clinical interpretation. Examples from EEG recordings are given to illustrate common artifacts that may be challenging to the reader because they mimic epileptiform pattern associated to people with epilepsy. Emerging techniques used to detect and reduce artifact without altering the electrocerebral signal are being developed to limit the contamination. While many artifacts are easy to recognize, more complex waveforms may confuse even the most experienced reader. Constant vigilance and a team effort to minimize artifact will help to ensure a proper interpretation of the EEG for optimal patient care.
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30

Thompson, Phillip D., Hiroshi Shibasaki, and Mark Hallett. The Neurophysiological Basis of Myoclonus. Edited by Donald L. Schomer and Fernando H. Lopes da Silva. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780190228484.003.0037.

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There are several types of myoclonus, with a variety of classification schemes, and the clinician must determine what type of myoclonus a patient has and what type of neurophysiological assessment can facilitate diagnosis. The electromyographic (EMG) correlate of the myoclonus should be examined, including the response to sensory stimuli (C-reflex). The electroencephalographic (EEG) correlate of the myoclonus should then be examined, possibly including back-averaging from the myoclonus or looking at corticomuscular (EEG–EMG) coherence. The somatosensory evoked response (SEP) should be obtained. Such studies will help determine the myoclonus origin, most commonly cortical or brainstem. One form of cortical myoclonus has the clinical appearance of a tremor (cortical tremor). Brainstem myoclonus includes exaggerated startle (hyperekplexia). Other forms of myoclonus include spinal myoclonus and functional myoclonus, which have their own distinct physiological signature. Several causes of myoclonus are reviewed, including rare types such as Creutzfeldt-Jakob disease and subacute sclerosing panencephalitis.
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31

Wendling, Fabrice, and Fernando H. Lopes da Silva. Dynamics of EEGs as Signals of Neuronal Populations. Edited by Donald L. Schomer and Fernando H. Lopes da Silva. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780190228484.003.0003.

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This chapter gives an overview of approaches used to understand the generation of electroencephalographic (EEG) signals using computational models. The basic concept is that appropriate modeling of neuronal networks, based on relevant anatomical and physiological data, allows researchers to test hypotheses about the nature of EEG signals. Here these models are considered at different levels of complexity. The first level is based on single cell biophysical properties anchored in classic Hodgkin-Huxley theory. The second level emphasizes on detailed neuronal networks and their role in generating different kinds of EEG oscillations. At the third level are models derived from the Wilson-Cowan approach, which constitutes the backbone of neural mass models. Another part of the chapter is dedicated to models of epileptiform activities. Finally, the themes of nonlinear dynamic systems and topological models in EEG generation are discussed.
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32

Staedtke, Verena, and Eric H. Kossoff. Epilepsy Syndromes in Childhood. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780199937837.003.0074.

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Epilepsy syndromes of childhood are a heterogeneous group of disorders that occur at specific neurodevelopmental stages, with a variable prognosis ranging from benign to catastrophic. In clinical practice they are categorized based on seizure type, age of onset, clinical presentation, electroencephalographic (EEG) findings, as well as response to treatment. In addition, recent advancements in neuroimaging and genetic testing have become important diagnostic tools revealing underlying defects for some of these syndromes. This knowledge has consequences for clinical practice, as it opens new perspectives for early diagnosis, prognosis and treatment. Here, we provide an up-to-date overview of the most common pediatric epilepsy syndromes, their clinical findings, associated EEG findings, and clinical management.
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33

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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34

Sutter, Raoul, Peter W. Kaplan, and Donald L. Schomer. Historical Aspects of Electroencephalography. Edited by Donald L. Schomer and Fernando H. Lopes da Silva. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780190228484.003.0001.

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Electroencephalography (EEG), a dynamic real-time recording of electrical neocortical brain activity, began in the 1600s with the discovery of electrical phenomena and the concept of an “action current.” The galvanometer was introduced in the 1800s and the first bioelectrical observations of human brain signals were made in the 1900s. Certain EEG patterns were associated with brain disorders, increasing the clinical and scientific use of EEG. In the 1980s, technical advances allowed EEGs to be digitized and linked with videotape recording. In the 1990s, digital data storage increased and computer networking enabled remote real-time EEG reading, which made possible continuous EEG (cEEG) monitoring. Manual cEEG analysis became increasingly labor-intensive, calling for methods to assist this process. In the 2000s, complex algorithms enabling quantitative EEG analyses were introduced, with a new focus on shared activity between rhythms, including phase and magnitude synchrony. The automation of spectral analysis enabled studies of spectral content.
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35

Koutroumanidis, Michalis, Dimitrios Sakellariou, and 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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36

Gaetz, Michael B., and Kelly J. Jantzen. Electroencephalography. Edited by Ruben Echemendia and Grant L. Iverson. Oxford University Press, 2016. http://dx.doi.org/10.1093/oxfordhb/9780199896585.013.006.

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Axonal injury is currently considered to be the structural substrate behind most concussion-related neurological dysfunction. Because the principal generators of EEG fields are graded excitatory and inhibitory synaptic potentials of pyramidal neurons, the EEG is well suited for characterizing large-scale functional disruptions associated with concussion induced metabolic and neurochemical changes, and for connecting those disruptions to deficits in behavior and cognition. This essay provides an overview of the use of EEG and newly developed analytical procedures for the measurement of functional impairment related to sport concussion. Elevations in delta and theta activity can be expected in a percentage of athletes and change in asymmetry and coherence may also be present. Newer techniques are likely to be of critical importance for understanding the anatomical and physiological basis of cognitive deficits and may provide additional insight into susceptibility to future injury. Computational modeling may advance our understanding of concussion.
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37

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

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38

IV, Tatum William, and William Tatum. Handbook of EEG Interpretation. Springer Publishing Company, Incorporated, 2014.

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39

IV, Tatum William, Selim R. Benbadis, Peter W. Kaplan, and Aatif M. Husain. Handbook of EEG Interpretation. Springer Publishing Company, Incorporated, 2008.

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40

Husain, Aatif, Selim Benbadis, and William Tatum. Handbook of Eeg Interpretation. Springer Publishing Company, Incorporated, 2010.

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41

Nundy, Amitabh. Neuroscience EEG Atlas. Jaypee Brothers Medical Publishers, 2015.

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42

Fisch and Spehlmann's EEG Primer: Basic Principles of Digital and Analog EEG. Elsevier, 1999.

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43

(Editor), Steven C. Schachter, Donald L. Schomer (Editor), and Bernard S. Chang (Editor), eds. Atlas of Ambulatory EEG. Academic Press, 2005.

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44

(Editor), Steven C. Schachter, Donald L. Schomer (Editor), and Bernard S. Chang (Editor), eds. Atlas of Ambulatory EEG. Academic Press, 2005.

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45

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

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46

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

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47

Crespel, Arielle, and Philippe Gelisse. Atlas of Electroencephalography: Awake and Sleep EEG. Editions John Libbey Eurotext, 2016.

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48

Brenner, Richard, and Lawrence Hirsch. Atlas of EEG in Critical Care. Wiley & Sons, Incorporated, John, 2022.

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49

I. V. Do William Tatum. Handbook of Eeg Interpretation, 2nd Ed. Springer Publishing Company, Incorporated, 2014.

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50

Brenner, Richard, and Lawrence Hirsch. Atlas of EEG in Critical Care. Wiley & Sons, Incorporated, John, 2011.

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