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{{short description|Electrophysiological monitoring method to record electrical activity of the brain}} |
{{short description|Electrophysiological monitoring method to record electrical activity of the brain}} |
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{{distinguish|text=[[electrography (disambiguation)|other types of electrography]]}} |
{{distinguish|text=[[electrography (disambiguation)|other types of electrography]]}} |
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'''Electroencephalography''' ('''EEG''') is a method to record an [[electrogram]] of the spontaneous electrical activity of the [[brain]]. The [[biosignal]]s detected by EEG have been shown to represent the [[postsynaptic potential]]s of pyramidal neurons in the [[neocortex]] and [[allocortex]].<ref>{{Cite book |last1=Amzica |first1=Florin |url=https://academic.oup.com/book/35515/chapter/305245903 |title=Cellular Substrates of Brain Rhythms |last2=Lopes da Silva |first2=Fernando H. |date=November 2017 |publisher=Oxford University Press |editor-last=Schomer |editor-first=Donald L. |volume=1 |language=en |doi=10.1093/med/9780190228484.003.0002 |editor-last2=Lopes da Silva |editor-first2=Fernando H.}}</ref> It is typically non-invasive, with the EEG [[electrode]]s placed along the [[scalp]] (commonly called "scalp EEG") using the [[10–20 system (EEG)|International |
'''Electroencephalography''' ('''EEG''') is a method to record an [[electrogram]] of the spontaneous electrical activity of the [[brain]]. The [[biosignal]]s detected by EEG have been shown to represent the [[postsynaptic potential]]s of pyramidal neurons in the [[neocortex]] and [[allocortex]].<ref>{{Cite book |last1=Amzica |first1=Florin |url=https://academic.oup.com/book/35515/chapter/305245903 |title=Cellular Substrates of Brain Rhythms |last2=Lopes da Silva |first2=Fernando H. |date=November 2017 |publisher=Oxford University Press |editor-last=Schomer |editor-first=Donald L. |volume=1 |language=en |doi=10.1093/med/9780190228484.003.0002 |editor-last2=Lopes da Silva |editor-first2=Fernando H.}}</ref> It is typically non-invasive, with the EEG [[electrode]]s placed along the [[scalp]] (commonly called "scalp EEG") using the [[10–20 system (EEG)|International 10–20 system]], or variations of it. [[Electrocorticography]], involving surgical placement of electrodes, is sometimes called "[[Electrocorticography|intracranial EEG]]". Clinical interpretation of EEG recordings is most often performed by visual inspection of the tracing or [[quantitative EEG|quantitative EEG analysis]]. |
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Voltage fluctuations measured by the EEG [[bioamplifier]] and [[electrode]]s allow the evaluation of normal brain activity. As the electrical activity monitored by EEG originates in neurons in the underlying brain tissue, the recordings made by the electrodes on the surface of the scalp vary in accordance with their orientation and distance to the source of the activity. Furthermore, the value recorded is distorted by intermediary tissues and |
Voltage fluctuations measured by the EEG [[bioamplifier]] and [[electrode]]s allow the evaluation of normal [[Brain activity and meditation|brain activity]]. As the electrical activity monitored by EEG originates in [[Neuron|neurons]] in the underlying [[Human brain|brain tissue]], the recordings made by the [[Electrode|electrodes]] on the surface of the [[scalp]] vary in accordance with their orientation and distance to the source of the activity. Furthermore, the value recorded is distorted by intermediary tissues and bones, which act in a manner akin to resistors and capacitors in an [[Electrical Circuit|electrical circuit]]. This means not all neurons will contribute equally to an EEG signal, with an EEG predominately reflecting the activity of [[cortical neurons]] near the [[Electrode|electrodes]] on the scalp. Deep structures within the [[brain]] further away from the [[Electrode|electrodes]] will not contribute directly to an EEG; these include the base of the [[cerebellar cortex|cortical]] [[gyrus]], mesial walls of the major [[Lobes of the brain|lobes]], [[hippocampus]], [[thalamus]], and [[Brainstem|brain stem]].<ref name=":6">{{Cite book |url=https://www.worldcat.org/oclc/1199587061 |title=Principles of neural science |date=2021 |others=Eric R. Kandel, John Koester, Sarah Mack, Steven Siegelbaum |isbn=978-1-259-64223-4 |edition=6th |location=New York |pages=1450 |oclc=1199587061}}</ref> |
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A healthy human EEG will show certain patterns of activity |
A healthy human EEG will show certain patterns of activity that correlate with how awake a person is. The range of frequencies one observes are between 1 and 30 Hz, and amplitudes will vary between 20 and 100 μV. The observed frequencies are subdivided into various groups: [[alpha]] (8–13 Hz), [[beta]] (13–30 Hz), delta (0.5–4 Hz), and [[theta]] (4–7 Hz). [[Alpha wave|Alpha waves]] are observed when a person is in a state of relaxed wakefulness and are mostly prominent over the parietal and occipital sites. During intense [[mental activity]], beta waves are more prominent in frontal areas as well as other regions. If a relaxed person is told to open their eyes, one observes alpha activity decreasing and an increase in beta activity. [[Theta]] and [[Delta wave|delta waves]] are not seen in [[wakefulness]], and if they are, it is a sign of brain dysfunction.<ref name=":6" /> |
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EEG can detect abnormal electrical discharges such as sharp waves, spikes or [[spike-and-wave]] complexes that are seen in people with [[epilepsy]] |
EEG can detect abnormal [[Electric discharge|electrical discharges]] such as [[sharp waves]], spikes, or [[spike-and-wave]] complexes that are seen in people with [[epilepsy]]; thus, it is often used to inform the [[medical diagnosis]]. EEG can detect the onset and spatio-temporal (location and time) evolution of [[seizures]] and the presence of [[status epilepticus]]. It is also used to help diagnose [[sleep disorder]]s, depth of [[anesthesia]], [[coma]], [[encephalopathies]], [[cerebral hypoxia]] after [[cardiac arrest]], and [[brain death]]. EEG used to be a first-line method of diagnosis for [[tumor]]s, [[stroke]], and other focal brain disorders,<ref>{{Cite web|url=https://medlineplus.gov/ency/article/003931.htm|title=EEG: MedlinePlus Medical Encyclopedia|website=medlineplus.gov|access-date=2022-07-24|archive-date=2016-07-05|archive-url=https://web.archive.org/web/20160705035021/https://www.nlm.nih.gov/medlineplus/ency/article/003931.htm|url-status=live}}</ref><ref>{{cite book| vauthors = Chernecky CC, Berger BJ |title=Laboratory tests and diagnostic procedures|date=2013|publisher=Elsevier|location=St. Louis, Mo.|isbn=9781455706945|edition=6th}}</ref> but this use has decreased with the advent of high-resolution anatomical imaging techniques such as [[magnetic resonance imaging]] (MRI) and [[computed tomography]] (CT). Despite its limited spatial resolution, EEG continues to be a valuable tool for research and diagnosis. It is one of the few mobile techniques available and offers millisecond-range temporal resolution, which is not possible with CT, PET, or MRI. |
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Derivatives of the EEG technique include [[evoked potential]]s (EP), which involves averaging the EEG activity time-locked to the presentation of a stimulus of some sort (visual, [[somatosensory]], or auditory). Event-related potentials ([[Event-related potential|ERP]]s) refer to averaged EEG responses that are time-locked to more complex processing of stimuli; this technique is used in [[cognitive science]], [[cognitive psychology]], and [[psychophysiology|psychophysiological]] research. |
Derivatives of the EEG technique include [[evoked potential]]s (EP), which involves averaging the EEG activity time-locked to the presentation of a stimulus of some sort (visual, [[somatosensory]], or auditory). Event-related potentials ([[Event-related potential|ERP]]s) refer to averaged EEG responses that are time-locked to more complex processing of stimuli; this technique is used in [[cognitive science]], [[cognitive psychology]], and [[psychophysiology|psychophysiological]] research. |
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==Uses== |
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⚫ | In 1875, [[Richard Caton]] (1842–1926), a physician practicing in [[Liverpool]], presented his findings about electrical phenomena of the exposed cerebral hemispheres of rabbits and monkeys in the ''[[British Medical Journal]]''. In 1890, Polish physiologist [[Adolf Beck (physiologist)|Adolf Beck]] published an investigation of spontaneous electrical activity of the brain of rabbits and dogs that included rhythmic oscillations altered by light. Beck started experiments on the electrical brain activity of animals. Beck placed electrodes directly on the surface of the brain to test for sensory stimulation. His observation of fluctuating brain activity led to the conclusion of brain waves.<ref name="Adolf Beck pioneer">{{cite journal | |
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⚫ | In 1912, Ukrainian physiologist [[Vladimir Pravdich-Neminsky|Vladimir Vladimirovich Pravdich-Neminsky]] published the first animal EEG and the [[evoked potential]] of the [[mammal]]ian (dog).<ref>{{cite journal | |
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⚫ | EEG is the [[Gold standard (test)|gold standard]] diagnostic procedure to confirm [[epilepsy]]. The [[Sensitivity and specificity|sensitivity]] of a routine EEG to detect interictal epileptiform discharges at epilepsy centers has been reported to be in the range of 29–55%.<ref name=":3">{{Cite journal |last1=Pillai |first1=Jyoti |last2=Sperling |first2=Michael R. |date=2006 |title=Interictal EEG and the diagnosis of epilepsy |url=https://pubmed.ncbi.nlm.nih.gov/17044820 |journal=Epilepsia |volume=47 Suppl 1 |pages=14–22 |doi=10.1111/j.1528-1167.2006.00654.x |issn=0013-9580 |pmid=17044820 |s2cid=8668713 |access-date=2022-10-23 |archive-date=2022-10-23 |archive-url=https://web.archive.org/web/20221023033520/https://pubmed.ncbi.nlm.nih.gov/17044820/ |url-status=live }}</ref> Given the low to moderate sensitivity, a routine EEG (typically with a duration of 20–30 minutes) can be normal in people that have epilepsy. When an EEG shows interictal epileptiform discharges (e.g. sharp waves, spikes, [[spike-and-wave]], etc.) it is confirmatory of epilepsy in nearly all cases (high [[Sensitivity and specificity|specificity]]), however up to 3.5% of the general population may have epileptiform abnormalities in an EEG without ever having had a seizure (low [[false positive rate]])<ref name=":3" /> or with a very low risk of developing epilepsy in the future.<ref>{{Cite journal |last=So |first=Elson L. |date=August 2010 |title=Interictal epileptiform discharges in persons without a history of seizures: what do they mean? |url=https://pubmed.ncbi.nlm.nih.gov/20634716 |journal=Journal of Clinical Neurophysiology |volume=27 |issue=4 |pages=229–238 |doi=10.1097/WNP.0b013e3181ea42a4 |issn=1537-1603 |pmid=20634716 |access-date=2022-10-23 |archive-date=2022-10-23 |archive-url=https://web.archive.org/web/20221023151049/https://pubmed.ncbi.nlm.nih.gov/20634716/ |url-status=live }}</ref> |
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⚫ | When a routine EEG is normal and there is a high suspicion or need to confirm epilepsy, it may be repeated or performed with a longer duration in the epilepsy monitoring unit (EMU) or at home with an ambulatory EEG. In addition, there are activating maneuvers such as photic stimulation, hyperventilation and sleep deprivation that can increase the diagnostic yield of the EEG.<ref name=":3" /> |
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⚫ | German physiologist and psychiatrist [[Hans Berger]] (1873–1941) recorded the first human EEG in 1924.<ref>{{cite journal | |
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⚫ | In 1934, Fisher and Lowenbach first demonstrated epileptiform spikes. In 1935, Gibbs, Davis and Lennox described [[interictal]] spike waves and the three cycles/s pattern of clinical [[absence seizure]]s, which began the field of clinical electroencephalography.<ref>{{cite journal | |
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⚫ | At times, a routine EEG is not sufficient to establish the diagnosis or determine the best course of action in terms of treatment. In this case, attempts may be made to record an EEG while a [[seizure]] is occurring. This is known as an [[ictal]] recording, as opposed to an interictal recording, which refers to the EEG recording between seizures. To obtain an ictal recording, a prolonged EEG is typically performed accompanied by a time-synchronized video and audio recording. This can be done either as an outpatient (at home) or during a hospital admission, preferably to an Epilepsy Monitoring Unit (EMU) with nurses and other personnel trained in the care of patients with seizures. Outpatient ambulatory video EEGs typically last one to three days. An admission to an Epilepsy Monitoring Unit typically lasts several days but may last for a week or longer. While in the hospital, seizure medications are usually withdrawn to increase the odds that a seizure will occur during admission. For reasons of safety, medications are not withdrawn during an EEG outside of the hospital. Ambulatory video EEGs, therefore, have the advantage of convenience and are less expensive than a hospital admission, but they also have the disadvantage of a decreased probability of recording a clinical event.{{citation needed|date=June 2022}} |
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⚫ | Epilepsy monitoring is often considered when patients continue having events despite being on anti-seizure medications or if there is concern that the patient's events have an alternate diagnosis, e.g., [[psychogenic non-epileptic seizures]], [[syncope (medicine)|syncope (fainting)]], sub-cortical [[movement disorder]]s, [[migraine]] variants, stroke, etc. In cases of epileptic seizures, continuous EEG monitoring helps to [[seizure types|characterize seizures]] and localize/lateralize the region of the brain from which a seizure originates. This can help identify appropriate non-medication treatment options.<ref>{{cite journal |vauthors=van Rooij LG, Hellström-Westas L, de Vries LS |date=August 2013 |title=Treatment of neonatal seizures |journal=Seminars in Fetal & Neonatal Medicine |volume=18 |issue=4 |pages=209–215 |doi=10.1016/j.siny.2013.01.001 |pmid=23402893}}</ref> In clinical use, EEG traces are visually analyzed by neurologists to look at various features. Increasingly, quantitative analysis of EEG is being used in conjunction with visual analysis. Quantitative analysis displays like power spectrum analysis, alpha-delta ratio, amplitude integrated EEG, and spike detection can help quickly identify segments of EEG that need close visual analysis or, in some cases, be used as surrogates for quick identification of seizures in long-term recordings. |
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⚫ | EEG may be used to monitor the depth of [[anesthesia]], as an indirect indicator of cerebral perfusion in [[carotid endarterectomy]], or to monitor [[amobarbital]] effects during the [[Wada test]]. EEG can also be used to predict the depth of anesthesia <ref>{{Cite journal |last1=Sun |first1=Christophe |last2=Holcman |first2=David |date=2022-08-01 |title=Combining transient statistical markers from the EEG signal to predict brain sensitivity to general anesthesia |url=https://www.sciencedirect.com/science/article/pii/S174680942200235X |journal=Biomedical Signal Processing and Control |language=en |volume=77 |pages=103713 |doi=10.1016/j.bspc.2022.103713 |issn=1746-8094 |s2cid=248488365}}</ref> based on the dynamics of the alpha band. |
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⚫ | In the 1950s, [[William Grey Walter]] developed an adjunct to EEG called [[EEG topography]], which allowed for the mapping of electrical activity across the surface of the brain. This enjoyed a brief period of popularity in the 1980s and seemed especially promising for psychiatry. It was never accepted by neurologists and remains primarily a research tool. |
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⚫ | An electroencephalograph system manufactured by Beckman Instruments was used on at least one of the [[Project Gemini]] manned spaceflights ( |
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⚫ | The first instance of the use of EEG to control a physical object, a robot, was in 1988. The robot would follow a line or stop depending on the alpha activity of the subject. If the subject relaxed and closed their eyes therefore increasing alpha activity, the bot would move. Opening their eyes thus decreasing alpha activity would cause the robot to stop on the trajectory.<ref>{{Cite journal |last=Bozinovski |first=Stevo |date=2013 |editor-last=Markovski |editor-first=Smile |editor2-last=Gusev |editor2-first=Marjan |title=Controlling Robots Using EEG Signals, Since 1988 |url=https://link.springer.com/chapter/10.1007/978-3-642-37169-1_1 |journal=ICT Innovations 2012 |series=Advances in Intelligent Systems and Computing |
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⚫ | In October 2018, scientists connected the brains of three people to experiment with the process of thoughts sharing. Five groups of three people participated in the experiment using EEG. The success rate of the experiment was 81%.<ref>{{cite journal | |
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==Clinical utility== |
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⚫ | EEG is the [[Gold standard (test)|gold standard]] diagnostic procedure to confirm [[epilepsy]]. The [[Sensitivity and specificity|sensitivity]] of a routine EEG to detect interictal epileptiform discharges at epilepsy centers has been reported to be in the range of |
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⚫ | When a routine EEG is normal and there is a high suspicion or need to confirm epilepsy, it may be repeated or performed with a longer duration in the epilepsy monitoring unit (EMU) or at home with an ambulatory EEG. In addition, there are activating maneuvers such as photic stimulation, hyperventilation and sleep deprivation that can increase the diagnostic yield of the EEG.<ref name=":3" /> |
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=== Other brain disorders === |
=== Other brain disorders === |
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An EEG might also be helpful for diagnosing or treating the following disorders:<ref>{{Cite web|url=https://www.mayoclinic.org/tests-procedures/eeg/about/pac-20393875|title=EEG (Electroencephalogram) |
An EEG might also be helpful for diagnosing or treating the following disorders:<ref>{{Cite web|url=https://www.mayoclinic.org/tests-procedures/eeg/about/pac-20393875|title=EEG (Electroencephalogram) – Mayo Clinic|website=[[Mayo Clinic]]|access-date=2019-08-30|archive-date=2019-08-30|archive-url=https://web.archive.org/web/20190830221430/https://www.mayoclinic.org/tests-procedures/eeg/about/pac-20393875|url-status=live}}</ref> |
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* Brain tumor |
* Brain tumor |
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* Brain damage from head injury |
* Brain damage from head injury |
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* Brain dysfunction that can have a variety of causes (encephalopathy) |
* Brain dysfunction that can have a variety of causes (encephalopathy) |
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* [[GLUT1 deficiency syndrome]]<ref name="pmid12181017">{{cite journal| author=von Moers A, Brockmann K, Wang D, Korenke CG, Huppke P, De Vivo DC | display-authors=etal| title=EEG features of glut-1 deficiency syndrome. | journal=Epilepsia | year= 2002 | volume= 43 | issue= 8 | pages= 941-5 | pmid=12181017 | doi=10.1046/j.1528-1157.2002.50401.x | pmc= | url=https://www.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&tool=sumsearch.org/cite&retmode=ref&cmd=prlinks&id=12181017 }} </ref> |
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* Inflammation of the brain (encephalitis) |
* Inflammation of the brain (encephalitis) |
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* Stroke |
* Stroke |
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* prognosticate in comatose patients (in certain instances) or in newborns with brain injury from various causes around the time of birth |
* prognosticate in comatose patients (in certain instances) or in newborns with brain injury from various causes around the time of birth |
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* determine whether to wean anti-epileptic medications. |
* determine whether to wean anti-epileptic medications. |
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== Clinical setting == |
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⚫ | At times, a routine EEG is not sufficient to establish the diagnosis or |
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⚫ | Epilepsy monitoring is often considered when patients continue having events despite being on |
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⚫ | EEG may be used to monitor the depth of [[anesthesia]], as an indirect indicator of cerebral perfusion in [[carotid endarterectomy]], or to monitor [[amobarbital]] |
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=== Intensive Care Unit (ICU) === |
=== Intensive Care Unit (ICU) === |
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EEG can also be used in [[intensive care unit]]s for brain function monitoring to monitor for non-convulsive seizures/non-convulsive status epilepticus, to monitor the effect of sedative/anesthesia in patients in medically induced coma (for treatment of refractory seizures or increased [[intracranial pressure]]), and to monitor for secondary brain damage in conditions such as [[subarachnoid hemorrhage]] (currently a research method). |
EEG can also be used in [[intensive care unit]]s for brain function monitoring to monitor for non-convulsive seizures/non-convulsive status epilepticus, to monitor the effect of sedative/anesthesia in patients in medically induced coma (for treatment of refractory seizures or increased [[intracranial pressure]]), and to monitor for secondary brain damage in conditions such as [[subarachnoid hemorrhage]] (currently a research method). |
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In cases where significant brain injury is suspected, e.g. after cardiac arrest, EEG can provide some prognostic information. |
In cases where significant brain injury is suspected, e.g., after cardiac arrest, EEG can provide some prognostic information. |
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If a patient with epilepsy is being considered for [[epilepsy surgery|resective surgery]], it is often necessary to localize the focus (source) of the epileptic brain activity with a resolution greater than what is provided by scalp EEG. In these cases, neurosurgeons typically implant strips and grids of electrodes or penetrating depth electrodes under the [[dura mater]], through either a [[craniotomy]] or a [[burr hole]]. The recording of these signals is referred to as [[electrocorticography]] (ECoG), subdural EEG (sdEEG), intracranial EEG (icEEG), or stereotactic EEG (sEEG). The signal recorded from ECoG is on a different scale of activity than the brain activity recorded from scalp EEG. Low |
If a patient with epilepsy is being considered for [[epilepsy surgery|resective surgery]], it is often necessary to localize the focus (source) of the epileptic brain activity with a resolution greater than what is provided by scalp EEG. In these cases, neurosurgeons typically implant strips and grids of electrodes or penetrating depth electrodes under the [[dura mater]], through either a [[craniotomy]] or a [[burr hole]]. The recording of these signals is referred to as [[electrocorticography]] (ECoG), subdural EEG (sdEEG), intracranial EEG (icEEG), or stereotactic EEG (sEEG). The signal recorded from ECoG is on a different scale of activity than the brain activity recorded from scalp EEG. Low-voltage, high-frequency components that cannot be seen easily (or at all) in scalp EEG can be seen clearly in ECoG. Further, smaller electrodes (which cover a smaller parcel of brain surface) allow for better spatial resolution to narrow down the areas critical for seizure onset and propagation. Some clinical sites record data from penetrating microelectrodes.<ref name="Niedermeyer">{{cite book| vauthors = Niedermeyer E, da Silva FL | title = Electroencephalography: Basic Principles, Clinical Applications, and Related Fields | publisher = Lippincott Williams & Wilkins | year = 2004| isbn = 978-0-7817-5126-1}}{{page needed|date=August 2013}}</ref> |
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EEG is not indicated for diagnosing |
EEG is not indicated for diagnosing headaches.<ref name="AANfive">{{Cite journal |author1 = American Academy of Neurology |author1-link = American Academy of Neurology |title = Five Things Physicians and Patients Should Question |journal = Choosing Wisely: An Initiative of the ABIM Foundation |url = http://www.choosingwisely.org/doctor-patient-lists/american-academy-of-neurology/ |access-date = August 1, 2013 |archive-date = September 1, 2013 |archive-url = https://web.archive.org/web/20130901115555/http://www.choosingwisely.org/doctor-patient-lists/american-academy-of-neurology/ |url-status = live }}, which cites |
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* |
*{{cite journal | vauthors = Gronseth GS, Greenberg MK | title = The utility of the electroencephalogram in the evaluation of patients presenting with headache: a review of the literature | journal = Neurology | volume = 45 | issue = 7 | pages = 1263–1267 | date = July 1995 | pmid = 7617180 | doi = 10.1212/WNL.45.7.1263 | s2cid = 26022438 }}</ref> Recurring headaches are a common pain problem, and this procedure is sometimes used in a search for a diagnosis, but it has no advantage over routine clinical evaluation.<ref name="AANfive" /> |
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=== Home ambulatory EEG === |
=== Home ambulatory EEG === |
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==Research use== |
==Research use== |
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{{Primary sources|section|date=December 2022}} |
{{Primary sources|section|date=December 2022}} |
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EEG |
EEG and the related study of [[event-related potential|ERPs]] are used extensively in [[neuroscience]], [[cognitive science]], [[cognitive psychology]], [[neurolinguistics]], and [[psychophysiology|psychophysiological]] research, as well as to study human functions such as swallowing.<ref>{{cite book | vauthors = Yang H, Ang KK, Wang C, Phua KS, Guan C | title = Neural and cortical analysis of swallowing and detection of motor imagery of swallow for dysphagia rehabilitation-A review | volume = 228 | pages = 185–219 | date = 2016 | pmid = 27590970 | doi = 10.1016/bs.pbr.2016.03.014 | isbn = 9780128042168 | series = Progress in Brain Research }}</ref><ref>{{cite journal | vauthors = Jestrović I, Coyle JL, Sejdić E | title = Decoding human swallowing via electroencephalography: a state-of-the-art review | journal = Journal of Neural Engineering | volume = 12 | issue = 5 | pages = 051001 | date = October 2015 | pmid = 26372528 | pmc = 4596245 | doi = 10.1088/1741-2560/12/5/051001 | bibcode = 2015JNEng..12e1001J }}</ref><ref>{{cite journal | vauthors = Cuellar M, Harkrider AW, Jenson D, Thornton D, Bowers A, Saltuklaroglu T | title = Time-frequency analysis of the EEG mu rhythm as a measure of sensorimotor integration in the later stages of swallowing | journal = Clinical Neurophysiology | volume = 127 | issue = 7 | pages = 2625–2635 | date = July 2016 | pmid = 27291882 | doi = 10.1016/j.clinph.2016.04.027 | s2cid = 3746307 }}</ref> Any EEG techniques used in research are not sufficiently standardised for clinical use, and many ERP studies fail to report all of the necessary processing steps for data collection and reduction,<ref>{{cite journal | vauthors = Clayson PE, Carbine KA, Baldwin SA, Larson MJ | title = Methodological reporting behavior, sample sizes, and statistical power in studies of event-related potentials: Barriers to reproducibility and replicability | journal = Psychophysiology | volume = 56 | issue = 11 | pages = e13437 | date = November 2019 | pmid = 31322285 | doi = 10.1111/psyp.13437 | s2cid = 197665482 | url = https://psyarxiv.com/kgv9z/ | access-date = 2022-10-07 | archive-date = 2022-10-07 | archive-url = https://web.archive.org/web/20221007045735/https://psyarxiv.com/kgv9z/ | url-status = live }}</ref> limiting the reproducibility and replicability of many studies. But research on mental disabilities, such as [[auditory processing disorder]] (APD), [[ADD]], or [[ADHD]], is becoming more widely known, and EEGs are used for research and treatment.{{citation needed|date=June 2022}} |
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===Advantages=== |
===Advantages=== |
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* Hardware costs are significantly lower than those of most other techniques <ref>{{cite journal | vauthors = Vespa PM, Nenov V, Nuwer MR | title = Continuous EEG monitoring in the intensive care unit: early findings and clinical efficacy | journal = Journal of Clinical Neurophysiology | volume = 16 | issue = 1 | pages = 1–13 | date = January 1999 | pmid = 10082088 | doi = 10.1097/00004691-199901000-00001 }}</ref> |
* Hardware costs are significantly lower than those of most other techniques <ref>{{cite journal | vauthors = Vespa PM, Nenov V, Nuwer MR | title = Continuous EEG monitoring in the intensive care unit: early findings and clinical efficacy | journal = Journal of Clinical Neurophysiology | volume = 16 | issue = 1 | pages = 1–13 | date = January 1999 | pmid = 10082088 | doi = 10.1097/00004691-199901000-00001 }}</ref> |
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* EEG prevents limited availability of technologists to provide immediate care in high traffic hospitals.<ref name="Techinical tips">{{cite journal | vauthors = Schultz TL | title = Technical tips: MRI compatible EEG electrodes: advantages, disadvantages, and financial feasibility in a clinical setting | journal = The Neurodiagnostic Journal | volume = 52 | issue = 1 | pages = 69–81 | date = March 2012 | pmid = 22558648 }}</ref> |
* EEG prevents limited availability of technologists to provide immediate care in high traffic hospitals.<ref name="Techinical tips">{{cite journal | vauthors = Schultz TL | title = Technical tips: MRI compatible EEG electrodes: advantages, disadvantages, and financial feasibility in a clinical setting | journal = The Neurodiagnostic Journal | volume = 52 | issue = 1 | pages = 69–81 | date = March 2012 | pmid = 22558648 }}</ref> |
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* EEG only requires a quiet room and briefcase-size equipment, whereas fMRI, SPECT, PET, MRS, or MEG require bulky and immobile equipment. For example, MEG requires equipment consisting of [[liquid helium]]-cooled detectors that can be used only in magnetically shielded rooms, altogether costing upwards of several million dollars;<ref name="Hämäläinen_1993">{{cite journal | |
* EEG only requires a quiet room and briefcase-size equipment, whereas fMRI, SPECT, PET, MRS, or MEG require bulky and immobile equipment. For example, MEG requires equipment consisting of [[liquid helium]]-cooled detectors that can be used only in magnetically shielded rooms, altogether costing upwards of several million dollars;<ref name="Hämäläinen_1993">{{cite journal |vauthors=Hämäläinen M, Hari R, Ilmoniemi RJ, Knuutila J, Lounasmaa OV |title=Magnetoencephalography-theory, instrumentation, and applications to noninvasive studies of the working human brain |volume=65 |year=1993 |pages=413–97 |journal=Reviews of Modern Physics |doi=10.1103/RevModPhys.65.413 |issue=2 |url=https://aaltodoc.aalto.fi/handle/123456789/18757 |bibcode=1993RvMP...65..413H |access-date=2018-09-10 |archive-date=2019-01-26 |archive-url=https://web.archive.org/web/20190126001114/https://aaltodoc.aalto.fi/handle/123456789/18757 |url-status=live }}</ref> and fMRI requires the use of a 1-ton magnet in, again, a shielded room. |
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* EEG can readily have a high temporal resolution, (although sub-millisecond resolution generates less meaningful data), because the two to 32 data streams generated by that number of electrodes is easily stored and processed, whereas 3D spatial technologies provide thousands or millions times as many input data streams, and are thus limited by hardware and software.<ref>{{cite journal | vauthors = Montoya-Martínez J, Vanthornhout J, Bertrand A, Francart T | title = Effect of number and placement of EEG electrodes on measurement of neural tracking of speech | journal = PLOS ONE | volume = 16 | issue = 2 | pages = e0246769 | date = 2021 | pmid = 33571299 | pmc = 7877609 | doi = 10.1101/800979 | s2cid = 208592165 }}</ref> EEG is commonly recorded at sampling rates between 250 and 2000 Hz in clinical and research settings. |
* EEG can readily have a high temporal resolution, (although sub-millisecond resolution generates less meaningful data), because the two to 32 data streams generated by that number of electrodes is easily stored and processed, whereas 3D spatial technologies provide thousands or millions times as many input data streams, and are thus limited by hardware and software.<ref>{{cite journal | vauthors = Montoya-Martínez J, Vanthornhout J, Bertrand A, Francart T | title = Effect of number and placement of EEG electrodes on measurement of neural tracking of speech | journal = PLOS ONE | volume = 16 | issue = 2 | pages = e0246769 | date = 2021 | pmid = 33571299 | pmc = 7877609 | doi = 10.1101/800979 | s2cid = 208592165 }}</ref> EEG is commonly recorded at sampling rates between 250 and 2000 Hz in clinical and research settings. |
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* EEG is relatively tolerant of subject movement, unlike most other neuroimaging techniques. There even exist methods for minimizing, and even eliminating movement artifacts in EEG data <ref>{{cite conference |doi=10.1109/IEMBS.2010.5627282 |title=Automatic detection of EEG artifacts arising from head movements |book-title=2010 Annual International Conference of the IEEE Engineering in Medicine and Biology |year=2010 | vauthors = O'Regan S, Faul S, Marnane W |isbn=978-1-4244-4123-5 |pages=6353–6}}</ref> |
* EEG is relatively tolerant of subject movement, unlike most other neuroimaging techniques. There even exist methods for minimizing, and even eliminating movement artifacts in EEG data <ref>{{cite conference |doi=10.1109/IEMBS.2010.5627282 |title=Automatic detection of EEG artifacts arising from head movements |book-title=2010 Annual International Conference of the IEEE Engineering in Medicine and Biology |year=2010 | vauthors = O'Regan S, Faul S, Marnane W |isbn=978-1-4244-4123-5 |pages=6353–6}}</ref> |
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* the simplicity of EEG readily provides for tracking of brain changes during different phases of life. EEG sleep analysis can indicate significant aspects of the timing of brain development, including evaluating adolescent brain maturation. |
* the simplicity of EEG readily provides for tracking of brain changes during different phases of life. EEG sleep analysis can indicate significant aspects of the timing of brain development, including evaluating adolescent brain maturation. |
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<ref>{{cite journal | vauthors = Feinberg I, Campbell IG | title = Longitudinal sleep EEG trajectories indicate complex patterns of adolescent brain maturation | journal = American Journal of Physiology. Regulatory, Integrative and Comparative Physiology | volume = 304 | issue = 4 | pages = R296–R303 | date = February 2013 | pmid = 23193115 | pmc = 3567357 | doi = 10.1152/ajpregu.00422.2012 }} |
<ref>{{cite journal | vauthors = Feinberg I, Campbell IG | title = Longitudinal sleep EEG trajectories indicate complex patterns of adolescent brain maturation | journal = American Journal of Physiology. Regulatory, Integrative and Comparative Physiology | volume = 304 | issue = 4 | pages = R296–R303 | date = February 2013 | pmid = 23193115 | pmc = 3567357 | doi = 10.1152/ajpregu.00422.2012 }} |
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* |
*{{lay source |template = cite press release|url= https://www.sciencedaily.com/releases/2013/03/130319102757.htm|title = Sleep study reveals how the adolescent brain makes the transition to mature thinking|date = March 19, 2013|website = ScienceDaily }}</ref> |
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* In EEG there is a better understanding of what signal is measured as compared to other research techniques, e.g. the BOLD response in MRI. |
* In EEG there is a better understanding of what signal is measured as compared to other research techniques, e.g. the BOLD response in MRI. |
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===Disadvantages=== |
===Disadvantages=== |
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* Low spatial resolution on the scalp. [[Functional magnetic resonance imaging|fMRI]], for example, can directly display areas of the brain that are active, while EEG requires intense interpretation just to hypothesize what areas are activated by a particular response.<ref>{{cite journal | vauthors = Srinivasan R |year=1999 |title=Methods to Improve the Spatial Resolution of EEG |journal=International Journal |volume=1 |issue=1 |pages=102–11}}</ref> |
* Low spatial resolution on the scalp. [[Functional magnetic resonance imaging|fMRI]], for example, can directly display areas of the brain that are active, while EEG requires intense interpretation just to hypothesize what areas are activated by a particular response.<ref>{{cite journal | vauthors = Srinivasan R |year=1999 |title=Methods to Improve the Spatial Resolution of EEG |journal=International Journal |volume=1 |issue=1 |pages=102–11}}</ref> |
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* Depending on the orientation and location of the dipole causing an EEG change, there may be a false localization due to the inverse problem.<ref>{{cite journal | vauthors = Grech R, Cassar T, Muscat J, Camilleri KP, Fabri SG, Zervakis M, Xanthopoulos P, Sakkalis V, Vanrumste B | display-authors = 6 | title = Review on solving the inverse problem in EEG source analysis | journal = Journal of Neuroengineering and Rehabilitation | volume = 5 | issue = 1 | pages = 25 | date = November 2008 | pmid = 18990257 | pmc = 2605581 | doi = 10.1186/1743-0003-5-25 }}</ref> |
* Depending on the orientation and location of the dipole causing an EEG change, there may be a false localization due to the inverse problem.<ref>{{cite journal | vauthors = Grech R, Cassar T, Muscat J, Camilleri KP, Fabri SG, Zervakis M, Xanthopoulos P, Sakkalis V, Vanrumste B | display-authors = 6 | title = Review on solving the inverse problem in EEG source analysis | journal = Journal of Neuroengineering and Rehabilitation | volume = 5 | issue = 1 | pages = 25 | date = November 2008 | pmid = 18990257 | pmc = 2605581 | doi = 10.1186/1743-0003-5-25 }}</ref> |
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* EEG poorly measures neural activity that occurs below the upper layers of the brain (the cortex). |
* EEG poorly measures neural activity that occurs below the upper layers of the brain (the cortex). |
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* Unlike [[Positron emission tomography|PET]] and MRS, cannot identify specific locations in the brain at which various neurotransmitters, drugs, etc. can be found.<ref name="radioligandPETExample" /> |
* Unlike [[Positron emission tomography|PET]] and MRS, cannot identify specific locations in the brain at which various neurotransmitters, drugs, etc. can be found.<ref name="radioligandPETExample" /> |
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* Often takes a long time to connect a subject to EEG, as it requires precise placement of dozens of electrodes around the head and the use of various gels, saline solutions, and/or pastes to maintain good conductivity, and a cap is used to keep them in place. While the length of time differs dependent on the specific EEG device used, as a general rule it takes considerably less time to prepare a subject for MEG, fMRI, MRS, and SPECT. |
* Often takes a long time to connect a subject to EEG, as it requires precise placement of dozens of electrodes around the head and the use of various gels, saline solutions, and/or pastes to maintain good conductivity, and a cap is used to keep them in place. While the length of time differs dependent on the specific EEG device used, as a general rule it takes considerably less time to prepare a subject for MEG, fMRI, MRS, and SPECT. |
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* Signal-to-noise ratio is poor, so sophisticated data analysis and relatively large numbers of subjects are needed to extract useful information from EEG.<ref>{{cite web | |
* Signal-to-noise ratio is poor, so sophisticated data analysis and relatively large numbers of subjects are needed to extract useful information from EEG.<ref>{{cite web |vauthors=Schlögl A, Slater M, Pfurtscheller G |year=2002 |title=Presence research and EEG |url=http://www-dept.cs.ucl.ac.uk/research/equator/papers/Documents2002/Mel_presence_2002.pdf |access-date=2013-08-24 |archive-date=2017-08-11 |archive-url=https://web.archive.org/web/20170811142535/http://www-dept.cs.ucl.ac.uk/research/equator/papers/Documents2002/Mel_presence_2002.pdf |url-status=live }}</ref> |
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===With other neuroimaging techniques=== |
===With other neuroimaging techniques=== |
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EEG has also been combined with [[positron emission tomography]]. This provides the advantage of allowing researchers to see what EEG signals are associated with different drug actions in the brain.<ref>{{cite journal | vauthors = Schreckenberger M, Lange-Asschenfeldt C, Lange-Asschenfeld C, Lochmann M, Mann K, Siessmeier T, Buchholz HG, Bartenstein P, Gründer G | display-authors = 6 | title = The thalamus as the generator and modulator of EEG alpha rhythm: a combined PET/EEG study with lorazepam challenge in humans | journal = NeuroImage | volume = 22 | issue = 2 | pages = 637–644 | date = June 2004 | pmid = 15193592 | doi = 10.1016/j.neuroimage.2004.01.047 | s2cid = 31790623 }}</ref> |
EEG has also been combined with [[positron emission tomography]]. This provides the advantage of allowing researchers to see what EEG signals are associated with different drug actions in the brain.<ref>{{cite journal | vauthors = Schreckenberger M, Lange-Asschenfeldt C, Lange-Asschenfeld C, Lochmann M, Mann K, Siessmeier T, Buchholz HG, Bartenstein P, Gründer G | display-authors = 6 | title = The thalamus as the generator and modulator of EEG alpha rhythm: a combined PET/EEG study with lorazepam challenge in humans | journal = NeuroImage | volume = 22 | issue = 2 | pages = 637–644 | date = June 2004 | pmid = 15193592 | doi = 10.1016/j.neuroimage.2004.01.047 | s2cid = 31790623 }}</ref> |
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Recent studies using [[machine learning]] techniques such as [[neural networks]] with statistical temporal features extracted from [[frontal lobe]] EEG brainwave data has shown high levels of success in classifying mental states (Relaxed, Neutral, Concentrating),<ref>{{cite book| vauthors = Bird J, Manso LJ, Ekart A, Faria DR |url=https://www.researchgate.net/publication/328615252|title=A Study on Mental State Classification using EEG-based Brain-Machine Interface|date=September 2018|publisher=9th international Conference on Intelligent Systems 2018|location=Madeira Island, Portugal|ref=birdeegmentalstates|access-date=3 December 2018}}</ref> mental emotional states (Negative, Neutral, Positive)<ref>{{cite book| |
Recent studies using [[machine learning]] techniques such as [[neural networks]] with statistical temporal features extracted from [[frontal lobe]] EEG brainwave data has shown high levels of success in classifying mental states (Relaxed, Neutral, Concentrating),<ref>{{cite book| vauthors = Bird J, Manso LJ, Ekart A, Faria DR |url=https://www.researchgate.net/publication/328615252|title=A Study on Mental State Classification using EEG-based Brain-Machine Interface|date=September 2018|publisher=9th international Conference on Intelligent Systems 2018|location=Madeira Island, Portugal|ref=birdeegmentalstates|access-date=3 December 2018}}</ref> mental emotional states (Negative, Neutral, Positive)<ref>{{cite book|vauthors=Bird JJ, Ekart A, Buckingham CD, Faria DR|url=https://www.disp-conference.org/|title=Mental Emotional Sentiment Classification with an EEG-based Brain-Machine Interface|date=2019|publisher=The International Conference on Digital Image and Signal Processing (DISP'19)|location=St Hugh's College, University of Oxford, United Kingdom|ref=birdeegemotions|access-date=3 December 2018|archive-date=3 December 2018|archive-url=https://web.archive.org/web/20181203202733/https://www.disp-conference.org/|url-status=live}}</ref> and [[thalamocortical dysrhythmia]].<ref>{{cite journal | vauthors = Vanneste S, Song JJ, De Ridder D | title = Thalamocortical dysrhythmia detected by machine learning | language = En | journal = Nature Communications | volume = 9 | issue = 1 | pages = 1103 | date = March 2018 | pmid = 29549239 | pmc = 5856824 | doi = 10.1038/s41467-018-02820-0 | bibcode = 2018NatCo...9.1103V }}</ref> |
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==Mechanisms== |
==Mechanisms== |
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During the recording, a series of activation procedures may be used. These procedures may induce normal or abnormal EEG activity that might not otherwise be seen. These procedures include hyperventilation, photic stimulation (with a strobe light), eye closure, mental activity, sleep and sleep deprivation. During (inpatient) epilepsy monitoring, a patient's typical seizure medications may be withdrawn. |
During the recording, a series of activation procedures may be used. These procedures may induce normal or abnormal EEG activity that might not otherwise be seen. These procedures include hyperventilation, photic stimulation (with a strobe light), eye closure, mental activity, sleep and sleep deprivation. During (inpatient) epilepsy monitoring, a patient's typical seizure medications may be withdrawn. |
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The digital EEG signal is stored electronically and can be filtered for display. Typical settings for the [[high-pass filter]] and a [[low-pass filter]] are 0.5–1 [[hertz|Hz]] and 35–70 Hz respectively. The high-pass filter typically filters out slow artifact, such as [[Galvanic skin response|electrogalvanic]] signals and movement artifact, whereas the low-pass filter filters out high-frequency artifacts, such as [[electromyography|electromyographic]] signals. An additional [[band-stop filter|notch filter]] is typically used to remove artifact caused by electrical power lines (60 Hz in the United States and 50 Hz in many other countries).<ref name |
The digital EEG signal is stored electronically and can be filtered for display. Typical settings for the [[high-pass filter]] and a [[low-pass filter]] are 0.5–1 [[hertz|Hz]] and 35–70 Hz respectively. The high-pass filter typically filters out slow artifact, such as [[Galvanic skin response|electrogalvanic]] signals and movement artifact, whereas the low-pass filter filters out high-frequency artifacts, such as [[electromyography|electromyographic]] signals. An additional [[band-stop filter|notch filter]] is typically used to remove artifact caused by electrical power lines (60 Hz in the United States and 50 Hz in many other countries).<ref name="Niedermeyer" /> |
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The EEG signals can be captured with opensource hardware such as [[OpenBCI]] and the signal can be processed by freely available EEG software such as [[EEGLAB]] or the [[Neurophysiological Biomarker Toolbox]]. |
The EEG signals can be captured with opensource hardware such as [[OpenBCI]] and the signal can be processed by freely available EEG software such as [[EEGLAB]] or the [[Neurophysiological Biomarker Toolbox]]. |
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=== Dry EEG electrodes === |
=== Dry EEG electrodes === |
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In the early 1990s Babak Taheri, at [[University of California, Davis]] demonstrated the first single and also multichannel dry active electrode arrays using micro-machining. The single channel dry EEG electrode construction and results were published in 1994.<ref>{{cite journal | vauthors = Taheri BA, Knight RT, Smith RL | title = A dry electrode for EEG recording | journal = Electroencephalography and Clinical Neurophysiology | volume = 90 | issue = 5 | pages = 376–383 | date = May 1994 | pmid = 7514984 | doi = 10.1016/0013-4694(94)90053-1 | url = https://zenodo.org/record/1253862 }}</ref> The arrayed electrode was also demonstrated to perform well compared to [[silver]]/[[silver chloride]] electrodes. The device consisted of four sites of sensors with integrated electronics to reduce noise by [[impedance matching]]. The advantages of such electrodes are: (1) no electrolyte used, (2) no skin preparation, (3) significantly reduced sensor size, and (4) compatibility with EEG monitoring systems. The active electrode array is an integrated system made of an array of capacitive sensors with local integrated circuitry housed in a package with batteries to power the circuitry. This level of integration was required to achieve the functional performance obtained by the electrode. The electrode was tested on an electrical test bench and on human subjects in four modalities of EEG activity, namely: (1) spontaneous EEG, (2) sensory event-related potentials, (3) brain stem potentials, and (4) cognitive event-related potentials. The performance of the dry electrode compared favorably with that of the standard wet electrodes in terms of skin preparation, no gel requirements (dry), and higher signal-to-noise ratio.<ref>{{cite thesis| vauthors = Alizadeh-Taheri B |year=1994|title=Active Micromachined Scalp Electrode Array for Eeg Signal Recording|type=PhD thesis|publisher=University of California, Davis|page=82|bibcode=1994PhDT........82A}}</ref> |
In the early 1990s Babak Taheri, at [[University of California, Davis]] demonstrated the first single and also multichannel dry active electrode arrays using micro-machining. The single channel dry EEG electrode construction and results were published in 1994.<ref>{{cite journal | vauthors = Taheri BA, Knight RT, Smith RL | title = A dry electrode for EEG recording | journal = Electroencephalography and Clinical Neurophysiology | volume = 90 | issue = 5 | pages = 376–383 | date = May 1994 | pmid = 7514984 | doi = 10.1016/0013-4694(94)90053-1 | url = https://zenodo.org/record/1253862 | access-date = 2019-12-10 | archive-date = 2019-12-22 | archive-url = https://web.archive.org/web/20191222051700/https://zenodo.org/record/1253862 | url-status = live }}</ref> The arrayed electrode was also demonstrated to perform well compared to [[silver]]/[[silver chloride]] electrodes. The device consisted of four sites of sensors with integrated electronics to reduce noise by [[impedance matching]]. The advantages of such electrodes are: (1) no electrolyte used, (2) no skin preparation, (3) significantly reduced sensor size, and (4) compatibility with EEG monitoring systems. The active electrode array is an integrated system made of an array of capacitive sensors with local integrated circuitry housed in a package with batteries to power the circuitry. This level of integration was required to achieve the functional performance obtained by the electrode. The electrode was tested on an electrical test bench and on human subjects in four modalities of EEG activity, namely: (1) spontaneous EEG, (2) sensory event-related potentials, (3) brain stem potentials, and (4) cognitive event-related potentials. The performance of the dry electrode compared favorably with that of the standard wet electrodes in terms of skin preparation, no gel requirements (dry), and higher signal-to-noise ratio.<ref>{{cite thesis| vauthors = Alizadeh-Taheri B |year=1994|title=Active Micromachined Scalp Electrode Array for Eeg Signal Recording|type=PhD thesis|publisher=University of California, Davis|page=82|bibcode=1994PhDT........82A}}</ref> |
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In 1999 researchers at [[Case Western Reserve University]], in [[Cleveland]], [[Ohio]], led by Hunter Peckham, used 64-electrode EEG skullcap to return limited hand movements to [[quadriplegic]] Jim Jatich. As Jatich concentrated on simple but opposite concepts like up and down, his beta-rhythm EEG output was analysed using software to identify patterns in the noise. A basic pattern was identified and used to control a switch: Above average activity was set to on, below average off. As well as enabling Jatich to control a computer cursor the signals were also used to drive the nerve controllers embedded in his hands, restoring some movement.<ref>{{Cite magazine | vauthors = Hockenberry J |date=August 2001 |title=The Next Brainiacs |url=https://www.wired.com/wired/archive/9.08/assist_pr.html |magazine=Wired Magazine}}</ref> |
In 1999 researchers at [[Case Western Reserve University]], in [[Cleveland]], [[Ohio]], led by Hunter Peckham, used 64-electrode EEG skullcap to return limited hand movements to [[quadriplegic]] Jim Jatich. As Jatich concentrated on simple but opposite concepts like up and down, his beta-rhythm EEG output was analysed using software to identify patterns in the noise. A basic pattern was identified and used to control a switch: Above average activity was set to on, below average off. As well as enabling Jatich to control a computer cursor the signals were also used to drive the nerve controllers embedded in his hands, restoring some movement.<ref>{{Cite magazine | vauthors = Hockenberry J |date=August 2001 |title=The Next Brainiacs |url=https://www.wired.com/wired/archive/9.08/assist_pr.html |magazine=Wired Magazine}}</ref> |
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ARL also developed a visualization tool, Customizable Lighting Interface for the Visualization of EEGs or CLIVE, which showed how well two brains are synchronized.<ref>{{Cite web|url= https://www.arl.army.mil/www/default.cfm?article=3214 |title=Army neuroscientists foresee intelligent agents on the battlefield | work = U.S. Army Research Laboratory |language=en|access-date=2018-08-29|archive-date=2018-08-29|archive-url=https://web.archive.org/web/20180829175612/https://www.arl.army.mil/www/default.cfm?article=3214|url-status=dead}}</ref> |
ARL also developed a visualization tool, Customizable Lighting Interface for the Visualization of EEGs or CLIVE, which showed how well two brains are synchronized.<ref>{{Cite web|url= https://www.arl.army.mil/www/default.cfm?article=3214 |title=Army neuroscientists foresee intelligent agents on the battlefield | work = U.S. Army Research Laboratory |language=en|access-date=2018-08-29|archive-date=2018-08-29|archive-url=https://web.archive.org/web/20180829175612/https://www.arl.army.mil/www/default.cfm?article=3214|url-status=dead}}</ref> |
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Currently, headsets are available incorporating dry electrodes with up to 30 channels.<ref>{{Cite web|url=https://www.cgxsystems.com/products|title=Dry EEG Headsets | Products | CGX|website=CGX 2021}}</ref> Such designs are able to compensate for some of the signal quality degradation related to high impedances by optimizing pre-amplification, shielding and supporting mechanics.<ref>{{cite web |title=Dry EEG Technology |url=https://www.cgxsystems.com/technology |publisher=CGX LLC}}</ref> |
Currently, headsets are available incorporating dry electrodes with up to 30 channels.<ref>{{Cite web|url=https://www.cgxsystems.com/products|title=Dry EEG Headsets | Products | CGX|website=CGX 2021|access-date=2020-02-13|archive-date=2020-02-13|archive-url=https://web.archive.org/web/20200213022928/https://www.cgxsystems.com/products|url-status=live}}</ref> Such designs are able to compensate for some of the signal quality degradation related to high impedances by optimizing pre-amplification, shielding and supporting mechanics.<ref>{{cite web |title=Dry EEG Technology |url=https://www.cgxsystems.com/technology |publisher=CGX LLC |access-date=2020-02-13 |archive-date=2020-02-13 |archive-url=https://web.archive.org/web/20200213022929/https://www.cgxsystems.com/technology |url-status=live }}</ref> |
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===Limitations=== |
===Limitations=== |
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Therefore, EEG provides information with a large bias to select neuron types, and generally should not be used to make claims about global brain activity. The [[meninges]], [[cerebrospinal fluid]] and skull "smear" the EEG signal, obscuring its intracranial source. |
Therefore, EEG provides information with a large bias to select neuron types, and generally should not be used to make claims about global brain activity. The [[meninges]], [[cerebrospinal fluid]] and skull "smear" the EEG signal, obscuring its intracranial source. |
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It is mathematically impossible to reconstruct a unique intracranial current source for a given EEG signal,<ref name |
It is mathematically impossible to reconstruct a unique intracranial current source for a given EEG signal,<ref name="Niedermeyer" /> as some currents produce potentials that cancel each other out. This is referred to as the [[inverse problem]]. However, much work has been done to produce remarkably good estimates of, at least, a localized [[electric dipole]] that represents the recorded currents.{{Citation needed|date=April 2010}} |
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===EEG |
===EEG vis-à-vis fMRI, fNIRS, fUS and PET=== |
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EEG has several strong points as a tool for exploring brain activity. EEGs can detect changes over milliseconds, which is excellent considering an [[action potential]] takes approximately 0.5–130 milliseconds to propagate across a single neuron, depending on the type of neuron.<ref>{{cite book | vauthors = Anderson J |title= Cognitive Psychology and Its Implications |edition= 6th |publisher= Worth |location=New York, NY |page= 17 |isbn=978-0-7167-0110-1 | date=22 October 2004 |type=Hardcover}}</ref> Other methods of looking at brain activity, such as [[Positron emission tomography|PET]], [[Functional magnetic resonance imaging|fMRI]] or [[Functional ultrasound imaging|fUS]] have time resolution between seconds and minutes. EEG measures the brain's electrical activity directly, while other methods record changes in blood flow (e.g., [[Single photon emission computed tomography|SPECT]], fMRI, fUS) or metabolic activity (e.g., PET, [[functional near-infrared spectroscopy|NIRS]]), which are indirect markers of brain electrical activity. |
EEG has several strong points as a tool for exploring brain activity. EEGs can detect changes over milliseconds, which is excellent considering an [[action potential]] takes approximately 0.5–130 milliseconds to propagate across a single neuron, depending on the type of neuron.<ref>{{cite book | vauthors = Anderson J |title= Cognitive Psychology and Its Implications |edition= 6th |publisher= Worth |location=New York, NY |page= 17 |isbn=978-0-7167-0110-1 | date=22 October 2004 |type=Hardcover}}</ref> Other methods of looking at brain activity, such as [[Positron emission tomography|PET]], [[Functional magnetic resonance imaging|fMRI]] or [[Functional ultrasound imaging|fUS]] have time resolution between seconds and minutes. EEG measures the brain's electrical activity directly, while other methods record changes in blood flow (e.g., [[Single photon emission computed tomography|SPECT]], fMRI, fUS) or metabolic activity (e.g., PET, [[functional near-infrared spectroscopy|NIRS]]), which are indirect markers of brain electrical activity. |
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EEG can be used simultaneously with [[functional near-infrared spectroscopy|NIRS]] or fUS without major technical difficulties. There is no influence of these modalities on each other and a combined measurement can give useful information about electrical activity as well as hemodynamics at medium spatial resolution. |
EEG can be used simultaneously with [[functional near-infrared spectroscopy|NIRS]] or fUS without major technical difficulties. There is no influence of these modalities on each other and a combined measurement can give useful information about electrical activity as well as hemodynamics at medium spatial resolution. |
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===EEG |
===EEG vis-à-vis MEG=== |
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EEG reflects correlated synaptic activity caused by [[post-synaptic potentials]] of cortical [[neurons]]. The ionic currents involved in the generation of fast [[action potentials]] may not contribute greatly to the averaged [[local field potential|field potentials]] representing the EEG.<ref name="Nunez PL, Srinivasan R 1981"/><ref>{{cite journal | vauthors = Creutzfeldt OD, Watanabe S, Lux HD | title = Relations between EEG phenomena and potentials of single cortical cells. I. Evoked responses after thalamic and erpicortical stimulation | journal = Electroencephalography and Clinical Neurophysiology | volume = 20 | issue = 1 | pages = 1–18 | date = January 1966 | pmid = 4161317 | doi = 10.1016/0013-4694(66)90136-2 }}</ref> More specifically, the scalp electrical potentials that produce EEG are generally thought to be caused by the extracellular ionic currents caused by [[dendrite|dendritic]] electrical activity, whereas the fields producing [[magnetoencephalography|magnetoencephalographic]] signals<ref name="Hämäläinen_1993"/> are associated with intracellular ionic currents.<ref>{{cite book| vauthors = Buzsaki G | title = Rhythms of the brain |publisher = Oxford University Press | year = 2006| isbn = 978-0-19-530106-9}}{{page needed|date=August 2013}}</ref> |
EEG reflects correlated synaptic activity caused by [[post-synaptic potentials]] of cortical [[neurons]]. The ionic currents involved in the generation of fast [[action potentials]] may not contribute greatly to the averaged [[local field potential|field potentials]] representing the EEG.<ref name="Nunez PL, Srinivasan R 1981" /><ref>{{cite journal | vauthors = Creutzfeldt OD, Watanabe S, Lux HD | title = Relations between EEG phenomena and potentials of single cortical cells. I. Evoked responses after thalamic and erpicortical stimulation | journal = Electroencephalography and Clinical Neurophysiology | volume = 20 | issue = 1 | pages = 1–18 | date = January 1966 | pmid = 4161317 | doi = 10.1016/0013-4694(66)90136-2 }}</ref> More specifically, the scalp electrical potentials that produce EEG are generally thought to be caused by the extracellular ionic currents caused by [[dendrite|dendritic]] electrical activity, whereas the fields producing [[magnetoencephalography|magnetoencephalographic]] signals<ref name="Hämäläinen_1993" /> are associated with intracellular ionic currents.<ref>{{cite book| vauthors = Buzsaki G | title = Rhythms of the brain |publisher = Oxford University Press | year = 2006| isbn = 978-0-19-530106-9}}{{page needed|date=August 2013}}</ref> |
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==Normal activity== |
==Normal activity== |
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<gallery |
<gallery mode="packed" widths="260px" heights="140"> |
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File:Human EEG with prominent alpha-rhythm.png|Human EEG with prominent resting state activity – alpha-rhythm. Left: EEG traces (horizontal – time in seconds; vertical – amplitudes, scale 100 μV). Right: power spectra of shown signals (vertical lines – 10 and 20 Hz, scale is linear). Alpha-rhythm consists of sinusoidal-like waves with frequencies in 8–12 Hz range (11 Hz in this case) more prominent in posterior sites. Alpha range is red at power spectrum graph. |
File:Human EEG with prominent alpha-rhythm.png|Human EEG with prominent resting state activity – alpha-rhythm. Left: EEG traces (horizontal – time in seconds; vertical – amplitudes, scale 100 μV). Right: power spectra of shown signals (vertical lines – 10 and 20 Hz, scale is linear). Alpha-rhythm consists of sinusoidal-like waves with frequencies in 8–12 Hz range (11 Hz in this case) more prominent in posterior sites. Alpha range is red at power spectrum graph. |
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File:Human EEG without alpha-rhythm.png|Human EEG with in resting state. Left: EEG traces (horizontal – time in seconds; vertical – amplitudes, scale 100 μV). Right: power spectra of shown signals (vertical lines – 10 and 20 Hz, scale is linear). 80–90% of people have prominent sinusoidal-like waves with frequencies in 8–12 Hz range – alpha rhythm. Others (like this) lack this type of activity. |
File:Human EEG without alpha-rhythm.png|Human EEG with in resting state. Left: EEG traces (horizontal – time in seconds; vertical – amplitudes, scale 100 μV). Right: power spectra of shown signals (vertical lines – 10 and 20 Hz, scale is linear). 80–90% of people have prominent sinusoidal-like waves with frequencies in 8–12 Hz range – alpha rhythm. Others (like this) lack this type of activity. |
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File:Human EEG artefacts.png |Common artifacts in human EEG. 1: Electrooculographic artifact caused by the excitation of eyeball's muscles (related to blinking, for example). Big-amplitude, slow, positive wave prominent in frontal electrodes. 2: Electrode's artifact caused by bad contact (and thus bigger impedance) between P3 electrode and skin. 3: Swallowing artifact. 4: Common reference electrode's artifact caused by bad contact between reference electrode and skin. Huge wave similar in all channels. |
File:Human EEG artefacts.png |Common artifacts in human EEG. 1: Electrooculographic artifact caused by the excitation of eyeball's muscles (related to blinking, for example). Big-amplitude, slow, positive wave prominent in frontal electrodes. 2: Electrode's artifact caused by bad contact (and thus bigger impedance) between P3 electrode and skin. 3: Swallowing artifact. 4: Common reference electrode's artifact caused by bad contact between reference electrode and skin. Huge wave similar in all channels. |
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</gallery> |
</gallery> |
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The EEG is typically described in terms of (1) [[neural oscillation|rhythmic activity]] and (2) transients. The rhythmic activity is divided into bands by frequency. To some degree, these frequency bands are a matter of nomenclature (i.e., any rhythmic activity between 8–12 Hz can be described as "alpha"), but these designations arose because rhythmic activity within a certain frequency range was noted to have a certain distribution over the scalp or a certain biological significance. Frequency bands are usually extracted using spectral methods (for instance Welch) as implemented for instance in freely available EEG software such as [[EEGLAB]] or the [[Neurophysiological Biomarker Toolbox]]. |
The EEG is typically described in terms of (1) [[neural oscillation|rhythmic activity]] and (2) transients. The rhythmic activity is divided into bands by frequency. To some degree, these frequency bands are a matter of nomenclature (i.e., any rhythmic activity between 8–12 Hz can be described as "alpha"), but these designations arose because rhythmic activity within a certain frequency range was noted to have a certain distribution over the scalp or a certain biological significance. Frequency bands are usually extracted using spectral methods (for instance Welch) as implemented for instance in freely available EEG software such as [[EEGLAB]] or the [[Neurophysiological Biomarker Toolbox]]. |
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Computational processing of the EEG is often named [[quantitative electroencephalography]] (qEEG). |
Computational processing of the EEG is often named [[quantitative electroencephalography]] (qEEG). |
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Most of the cerebral signal observed in the scalp EEG falls in the range of 1–20 Hz (activity below or above this range is likely to be artifactual, under standard clinical recording techniques). Waveforms are subdivided into bandwidths known as alpha, beta, theta, and delta to signify the majority of the EEG used in clinical practice.<ref name=EEG>{{cite journal | vauthors = Tatum WO | title = Ellen R. Grass Lecture: extraordinary EEG | journal = The Neurodiagnostic Journal | volume = 54 | issue = 1 | pages = 3–21 | date = March 2014 | pmid = 24783746 }}</ref> |
Most of the cerebral signal observed in the scalp EEG falls in the range of 1–20 Hz (activity below or above this range is likely to be artifactual, under standard clinical recording techniques). Waveforms are subdivided into bandwidths known as alpha, beta, theta, and delta to signify the majority of the EEG used in clinical practice.<ref name="EEG">{{cite journal | vauthors = Tatum WO | title = Ellen R. Grass Lecture: extraordinary EEG | journal = The Neurodiagnostic Journal | volume = 54 | issue = 1 | pages = 3–21 | date = March 2014 | pmid = 24783746 }}</ref> |
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===Comparison of EEG bands=== |
===Comparison of EEG bands=== |
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|+ Comparison of EEG bands |
|+ Comparison of EEG bands |
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* drowsiness in adults and teens |
* drowsiness in adults and teens |
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* idling |
* idling |
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* Associated with inhibition of elicited responses (has been found to spike in situations where a person is actively trying to repress a response or action).<ref name="Kirmizi-Alsan2006"/> |
* Associated with inhibition of elicited responses (has been found to spike in situations where a person is actively trying to repress a response or action).<ref name="Kirmizi-Alsan2006" /> |
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* focal subcortical lesions |
* focal subcortical lesions |
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! [[Gamma wave|Gamma]] |
! [[Gamma wave|Gamma]] |
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| > 32 || Somatosensory cortex || |
| > 32 || Somatosensory cortex || |
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* Displays during cross-modal sensory processing (perception that combines two different senses, such as sound and sight)<ref name=KisleyCornwell2006>{{cite journal | vauthors = Kisley MA, Cornwell ZM | title = Gamma and beta neural activity evoked during a sensory gating paradigm: effects of auditory, somatosensory and cross-modal stimulation | journal = Clinical Neurophysiology | volume = 117 | issue = 11 | pages = 2549–2563 | date = November 2006 | pmid = 17008125 | pmc = 1773003 | doi = 10.1016/j.clinph.2006.08.003 }}</ref><ref name=KanayamaSatoOhira2007>{{cite journal | vauthors = Kanayama N, Sato A, Ohira H | title = Crossmodal effect with rubber hand illusion and gamma-band activity | journal = Psychophysiology | volume = 44 | issue = 3 | pages = 392–402 | date = May 2007 | pmid = 17371495 | doi = 10.1111/j.1469-8986.2007.00511.x }}</ref> |
* Displays during cross-modal sensory processing (perception that combines two different senses, such as sound and sight)<ref name="KisleyCornwell2006">{{cite journal | vauthors = Kisley MA, Cornwell ZM | title = Gamma and beta neural activity evoked during a sensory gating paradigm: effects of auditory, somatosensory and cross-modal stimulation | journal = Clinical Neurophysiology | volume = 117 | issue = 11 | pages = 2549–2563 | date = November 2006 | pmid = 17008125 | pmc = 1773003 | doi = 10.1016/j.clinph.2006.08.003 }}</ref><ref name="KanayamaSatoOhira2007">{{cite journal | vauthors = Kanayama N, Sato A, Ohira H | title = Crossmodal effect with rubber hand illusion and gamma-band activity | journal = Psychophysiology | volume = 44 | issue = 3 | pages = 392–402 | date = May 2007 | pmid = 17371495 | doi = 10.1111/j.1469-8986.2007.00511.x }}</ref> |
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* Also is shown during short-term memory matching of recognized objects, sounds, or tactile sensations |
* Also is shown during short-term memory matching of recognized objects, sounds, or tactile sensations |
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The practice of using only whole numbers in the definitions comes from practical considerations in the days when only whole cycles could be counted on paper records. This leads to gaps in the definitions, as seen elsewhere on this page. The theoretical definitions have always been more carefully defined to include all frequencies. Unfortunately there is no agreement in standard reference works on what these ranges should be''' '''– values for the upper end of alpha and lower end of beta include 12, 13, 14 and 15. If the threshold is taken as 14 Hz, then the slowest beta wave has about the same duration as the longest spike (70 ms), which makes this the most useful value. |
The practice of using only whole numbers in the definitions comes from practical considerations in the days when only whole cycles could be counted on paper records. This leads to gaps in the definitions, as seen elsewhere on this page. The theoretical definitions have always been more carefully defined to include all frequencies. Unfortunately there is no agreement in standard reference works on what these ranges should be''' '''– values for the upper end of alpha and lower end of beta include 12, 13, 14 and 15. If the threshold is taken as 14 Hz, then the slowest beta wave has about the same duration as the longest spike (70 ms), which makes this the most useful value. |
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[[File:eeg gamma.svg|thumb|[[Gamma wave]]s|400px|right]] |
[[File:eeg gamma.svg|thumb|[[Gamma wave]]s|400px|right]] |
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* [[Gamma wave|Gamma]] is the frequency range approximately 30–100 Hz. Gamma rhythms are thought to represent binding of different populations of neurons together into a network for the purpose of carrying out a certain cognitive or motor function.<ref name="Niedermeyer"/> |
* [[Gamma wave|Gamma]] is the frequency range approximately 30–100 Hz. Gamma rhythms are thought to represent binding of different populations of neurons together into a network for the purpose of carrying out a certain cognitive or motor function.<ref name="Niedermeyer" /> |
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* [[Mu wave|Mu]] range is 8–13 Hz and partly overlaps with other frequencies. It reflects the synchronous firing of motor neurons in rest state. Mu suppression is thought to reflect motor mirror neuron systems, because when an action is observed, the pattern extinguishes, possibly because the normal and mirror neuronal systems "go out of sync" and interfere with one other.<ref name="Oberman LM 2005"/> |
* [[Mu wave|Mu]] range is 8–13 Hz and partly overlaps with other frequencies. It reflects the synchronous firing of motor neurons in rest state. Mu suppression is thought to reflect motor mirror neuron systems, because when an action is observed, the pattern extinguishes, possibly because the normal and mirror neuronal systems "go out of sync" and interfere with one other.<ref name="Oberman LM 2005" /> |
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"Ultra-slow" or "near-[[Direct current|DC]]" activity is recorded using DC amplifiers in some research contexts. It is not typically recorded in a clinical context because the signal at these frequencies is susceptible to a number of artifacts. |
"Ultra-slow" or "near-[[Direct current|DC]]" activity is recorded using DC amplifiers in some research contexts. It is not typically recorded in a clinical context because the signal at these frequencies is susceptible to a number of artifacts. |
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The normal EEG also varies depending on state. The EEG is used along with other measurements ([[electrooculography|EOG]], [[electromyography|EMG]]) to define [[Sleep cycle|sleep stages]] in [[polysomnography]]. Stage I sleep (equivalent to drowsiness in some systems) appears on the EEG as drop-out of the posterior basic rhythm. There can be an increase in theta frequencies. Santamaria and Chiappa cataloged a number of the variety of patterns associated with drowsiness. Stage II sleep is characterized by sleep spindles – transient runs of rhythmic activity in the 12–14 Hz range (sometimes referred to as the "sigma" band) that have a frontal-central maximum. Most of the activity in Stage II is in the 3–6 Hz range. Stage III and IV sleep are defined by the presence of delta frequencies and are often referred to collectively as "slow-wave sleep". Stages I–IV comprise non-REM (or "NREM") sleep. The EEG in REM (rapid eye movement) sleep appears somewhat similar to the awake EEG. |
The normal EEG also varies depending on state. The EEG is used along with other measurements ([[electrooculography|EOG]], [[electromyography|EMG]]) to define [[Sleep cycle|sleep stages]] in [[polysomnography]]. Stage I sleep (equivalent to drowsiness in some systems) appears on the EEG as drop-out of the posterior basic rhythm. There can be an increase in theta frequencies. Santamaria and Chiappa cataloged a number of the variety of patterns associated with drowsiness. Stage II sleep is characterized by sleep spindles – transient runs of rhythmic activity in the 12–14 Hz range (sometimes referred to as the "sigma" band) that have a frontal-central maximum. Most of the activity in Stage II is in the 3–6 Hz range. Stage III and IV sleep are defined by the presence of delta frequencies and are often referred to collectively as "slow-wave sleep". Stages I–IV comprise non-REM (or "NREM") sleep. The EEG in REM (rapid eye movement) sleep appears somewhat similar to the awake EEG. |
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EEG under general anesthesia depends on the type of anesthetic employed. With halogenated anesthetics, such as [[halothane]] or intravenous agents, such as [[propofol]], a rapid (alpha or low beta), nonreactive EEG pattern is seen over most of the scalp, especially anteriorly; in some older terminology this was known as a WAR (widespread anterior rapid) pattern, contrasted with a WAIS (widespread slow) pattern associated with high doses of [[opiate]]s. Anesthetic effects on EEG signals are beginning to be understood at the level of drug actions on different kinds of synapses and the circuits that allow synchronized neuronal activity.<ref>{{cite web | title = The MacIver Lab | url = http://www.stanford.edu/group/maciverlab/ | publisher = Stanford University }}</ref> |
EEG under general anesthesia depends on the type of anesthetic employed. With halogenated anesthetics, such as [[halothane]] or intravenous agents, such as [[propofol]], a rapid (alpha or low beta), nonreactive EEG pattern is seen over most of the scalp, especially anteriorly; in some older terminology this was known as a WAR (widespread anterior rapid) pattern, contrasted with a WAIS (widespread slow) pattern associated with high doses of [[opiate]]s. Anesthetic effects on EEG signals are beginning to be understood at the level of drug actions on different kinds of synapses and the circuits that allow synchronized neuronal activity.<ref>{{cite web | title = The MacIver Lab | url = http://www.stanford.edu/group/maciverlab/ | publisher = Stanford University | access-date = 2006-12-16 | archive-date = 2008-11-23 | archive-url = https://web.archive.org/web/20081123211653/http://www.stanford.edu/group/maciverlab/ | url-status = live }}</ref> |
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==Artifacts== |
==Artifacts== |
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It is important to be able to distinguish artifacts from genuine brain activity in order to prevent incorrect interpretations of EEG data. General approaches for the removal of artifacts from the data are, prevention, rejection and cancellation. The goal of any approach is to develop methodology capable of identifying and removing artifacts without affecting the quality of the EEG signal. As artifact sources are quite different the majority of researchers focus on developing algorithms that will identify and remove a single type of noise in the signal. Simple filtering using a [[Band-stop filter|notch filter]] is commonly employed to reject components with a 50/60 Hz frequency. However such simple filters are not an appropriate choice for dealing with all artifacts, as for some, their frequencies will overlap with the EEG frequencies. |
It is important to be able to distinguish artifacts from genuine brain activity in order to prevent incorrect interpretations of EEG data. General approaches for the removal of artifacts from the data are, prevention, rejection and cancellation. The goal of any approach is to develop methodology capable of identifying and removing artifacts without affecting the quality of the EEG signal. As artifact sources are quite different the majority of researchers focus on developing algorithms that will identify and remove a single type of noise in the signal. Simple filtering using a [[Band-stop filter|notch filter]] is commonly employed to reject components with a 50/60 Hz frequency. However such simple filters are not an appropriate choice for dealing with all artifacts, as for some, their frequencies will overlap with the EEG frequencies. |
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Regression algorithms have a moderate computation cost and are simple. They represented the most popular correction method up until the mid-1990s when they were replaced by "blind source separation" type methods. Regression algorithms work on the premise that all artifacts are comprised by one or more reference channels. Subtracting these reference channels from the other contaminated channels, in either the time or frequency domain, by estimating the impact of the reference channels on the other channels, would correct the channels for the artifact. Although the requirement of reference channels ultimately lead to this class of algorithm being replaced, they still represent the benchmark to which modern algorithms are evaluated against.<ref name=":7">{{Cite journal |last1=Alsuradi |first1=Haneen |last2=Park |first2=Wanjoo |last3=Eid |first3=Mohamad |date=2020 |title=EEG-Based Neurohaptics Research: A Literature Review |url=https://ieeexplore.ieee.org/document/9031313 |journal=IEEE Access |volume=8 |pages=49313–49328 |doi=10.1109/ACCESS.2020.2979855 |s2cid=214596892 |issn=2169-3536}}</ref> |
Regression algorithms have a moderate computation cost and are simple. They represented the most popular correction method up until the mid-1990s when they were replaced by "blind source separation" type methods. Regression algorithms work on the premise that all artifacts are comprised by one or more reference channels. Subtracting these reference channels from the other contaminated channels, in either the time or frequency domain, by estimating the impact of the reference channels on the other channels, would correct the channels for the artifact. Although the requirement of reference channels ultimately lead to this class of algorithm being replaced, they still represent the benchmark to which modern algorithms are evaluated against.<ref name=":7">{{Cite journal |last1=Alsuradi |first1=Haneen |last2=Park |first2=Wanjoo |last3=Eid |first3=Mohamad |date=2020 |title=EEG-Based Neurohaptics Research: A Literature Review |url=https://ieeexplore.ieee.org/document/9031313 |journal=IEEE Access |volume=8 |pages=49313–49328 |doi=10.1109/ACCESS.2020.2979855 |s2cid=214596892 |issn=2169-3536 |access-date=2022-12-19 |archive-date=2022-10-24 |archive-url=https://web.archive.org/web/20221024055648/https://ieeexplore.ieee.org/document/9031313/ |url-status=live }}</ref> |
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Blind source separation (BSS) algorithms employed to remove artifacts include [[principal component analysis]] (PCA) and [[independent component analysis]] (ICA) and several algorithms in the this class have been successful at tackling most physiological artifacts.<ref name=":7" /> |
Blind source separation (BSS) algorithms employed to remove artifacts include [[principal component analysis]] (PCA) and [[independent component analysis]] (ICA) and several algorithms in the this class have been successful at tackling most physiological artifacts.<ref name=":7" /> |
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Artifact removal on a single electrode uses a reference segment obtained just before the artifact starts. The method consists in transporting the wavelet cumulative coefficient of the artifact onto the cumulative one of the reference signal. This normalization transport smooths the signal, so that the replaced signal instead of the artifact has similar statistical properties as the reference. The method is called wavelet quantil normalization WQN<ref>{{Cite journal |last1=Dora |first1=Matteo |last2=Holcman |first2=David |date=2022 |title=Adaptive Single-Channel EEG Artifact Removal With Applications to Clinical Monitoring |url=https://ieeexplore.ieee.org/document/9694664 |journal=IEEE Transactions on Neural Systems and Rehabilitation Engineering |volume=30 |pages=286–295 |doi=10.1109/TNSRE.2022.3147072 |pmid=35085086 |issn=1558-0210}}</ref> and can be used in real-time to remove artifacts on EEG sedline monitor. This approach performs better than classical soft and hard thresholding.<ref>{{Cite journal |last1=Dora |first1=Matteo |last2=Jaffard |first2=Stéphane |last3=Holcman |first3=David |date=2022-11-01 |title=The WQN algorithm to adaptively correct artifacts in the EEG signal |url=https://www.sciencedirect.com/science/article/pii/S1063520322000616 |journal=Applied and Computational Harmonic Analysis |language=en |volume=61 |pages=347–356 |doi=10.1016/j.acha.2022.07.007 |arxiv=2207.11696 |s2cid=251040553 |issn=1063-5203}}</ref> |
Artifact removal on a single electrode uses a reference segment obtained just before the artifact starts. The method consists in transporting the wavelet cumulative coefficient of the artifact onto the cumulative one of the reference signal. This normalization transport smooths the signal, so that the replaced signal instead of the artifact has similar statistical properties as the reference. The method is called wavelet quantil normalization WQN<ref>{{Cite journal |last1=Dora |first1=Matteo |last2=Holcman |first2=David |date=2022 |title=Adaptive Single-Channel EEG Artifact Removal With Applications to Clinical Monitoring |url=https://ieeexplore.ieee.org/document/9694664 |journal=IEEE Transactions on Neural Systems and Rehabilitation Engineering |volume=30 |pages=286–295 |doi=10.1109/TNSRE.2022.3147072 |pmid=35085086 |issn=1558-0210 |access-date=2023-01-28 |archive-date=2023-03-08 |archive-url=https://web.archive.org/web/20230308035936/https://ieeexplore.ieee.org/document/9694664/ |url-status=live }}</ref> and can be used in real-time to remove artifacts on EEG sedline monitor. This approach performs better than classical soft and hard thresholding.<ref>{{Cite journal |last1=Dora |first1=Matteo |last2=Jaffard |first2=Stéphane |last3=Holcman |first3=David |date=2022-11-01 |title=The WQN algorithm to adaptively correct artifacts in the EEG signal |url=https://www.sciencedirect.com/science/article/pii/S1063520322000616 |journal=Applied and Computational Harmonic Analysis |language=en |volume=61 |pages=347–356 |doi=10.1016/j.acha.2022.07.007 |arxiv=2207.11696 |s2cid=251040553 |issn=1063-5203}}</ref> |
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=== Physiological artifacts === |
=== Physiological artifacts === |
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Ocular artifacts affect the EEG signal significantly. This is due to eye movements involving a change in electric fields surrounding the eyes, distorting the electric field over the scalp, and as EEG is recorded on the scalp, it therefore distorts the recorded signal. A difference of opinion exists among researchers, with some arguing ocular artifacts are, or may be reasonably described as a single generator, whilst others argue it is important to understand the potentially complicated mechanisms. Three potential mechanisms have been proposed to explain the ocular artifact. |
Ocular artifacts affect the EEG signal significantly. This is due to eye movements involving a change in electric fields surrounding the eyes, distorting the electric field over the scalp, and as EEG is recorded on the scalp, it therefore distorts the recorded signal. A difference of opinion exists among researchers, with some arguing ocular artifacts are, or may be reasonably described as a single generator, whilst others argue it is important to understand the potentially complicated mechanisms. Three potential mechanisms have been proposed to explain the ocular artifact. |
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The first is corneal retinal dipole movement which argues that an electric [[dipole]] is formed between the cornea and retina, as the former is positively and the latter negatively charged. When the eye moves, so does this dipole which impacts the electrical field over the scalp, this is the most standard view. The second mechanism is retinal dipole movement, which is similar to the first but differing in that it argues there is a potential difference, hence dipole across the retina with the cornea having little effect. The third mechanism is eyelid movement. It is known that there is a change in voltage around the eyes when the eyelid moves, even if the eyeball does not. It is thought that the eyelid can be described as a sliding potential source and that the impacting of blinking is different to eye movement on the recorded EEG.<ref name=":8">{{Cite journal |last1=Croft |first1=R. J. |last2=Barry |first2=R. J. |date=2000-02-01 |title=Removal of ocular artifact from the EEG: a review |url=https://www.sciencedirect.com/science/article/pii/S0987705300000551 |journal=Neurophysiologie Clinique/Clinical Neurophysiology |language=en |volume=30 |issue=1 |pages=5–19 |doi=10.1016/S0987-7053(00)00055-1 |pmid=10740792 |s2cid=13738373 |issn=0987-7053}}</ref> |
The first is corneal retinal dipole movement which argues that an electric [[dipole]] is formed between the cornea and retina, as the former is positively and the latter negatively charged. When the eye moves, so does this dipole which impacts the electrical field over the scalp, this is the most standard view. The second mechanism is retinal dipole movement, which is similar to the first but differing in that it argues there is a potential difference, hence dipole across the retina with the cornea having little effect. The third mechanism is eyelid movement. It is known that there is a change in voltage around the eyes when the eyelid moves, even if the eyeball does not. It is thought that the eyelid can be described as a sliding potential source and that the impacting of blinking is different to eye movement on the recorded EEG.<ref name=":8">{{Cite journal |last1=Croft |first1=R. J. |last2=Barry |first2=R. J. |date=2000-02-01 |title=Removal of ocular artifact from the EEG: a review |url=https://www.sciencedirect.com/science/article/pii/S0987705300000551 |journal=Neurophysiologie Clinique/Clinical Neurophysiology |language=en |volume=30 |issue=1 |pages=5–19 |doi=10.1016/S0987-7053(00)00055-1 |pmid=10740792 |s2cid=13738373 |issn=0987-7053 |access-date=2022-12-19 |archive-date=2020-07-31 |archive-url=https://web.archive.org/web/20200731105128/https://www.sciencedirect.com/science/article/pii/S0987705300000551 |url-status=live }}</ref> |
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Eyelid fluttering artifacts of a characteristic type were previously called Kappa rhythm (or Kappa waves). It is usually seen in the prefrontal leads, that is, just over the eyes. Sometimes they are seen with mental activity. They are usually in the Theta (4–7 Hz) or Alpha (7–14 Hz) range. They were named because they were believed to originate from the brain. Later study revealed they were generated by rapid fluttering of the eyelids, sometimes so minute that it was difficult to see. They are in fact noise in the EEG reading, and should not technically be called a rhythm or wave. Therefore, current usage in electroencephalography refers to the phenomenon as an eyelid fluttering artifact, rather than a Kappa rhythm (or wave).<ref name="recom_clin_neurophys_1983">{{cite book |title=Introduction to EEG and evoked potentials |vauthors=Epstein CM |publisher=J. B. Lippincott Co. |year=1983 |isbn=978-0-397-50598-2}}{{page needed|date=August 2013}}</ref> |
Eyelid fluttering artifacts of a characteristic type were previously called Kappa rhythm (or Kappa waves). It is usually seen in the prefrontal leads, that is, just over the eyes. Sometimes they are seen with mental activity. They are usually in the Theta (4–7 Hz) or Alpha (7–14 Hz) range. They were named because they were believed to originate from the brain. Later study revealed they were generated by rapid fluttering of the eyelids, sometimes so minute that it was difficult to see. They are in fact noise in the EEG reading, and should not technically be called a rhythm or wave. Therefore, current usage in electroencephalography refers to the phenomenon as an eyelid fluttering artifact, rather than a Kappa rhythm (or wave).<ref name="recom_clin_neurophys_1983">{{cite book |title=Introduction to EEG and evoked potentials |vauthors=Epstein CM |publisher=J. B. Lippincott Co. |year=1983 |isbn=978-0-397-50598-2}}{{page needed|date=August 2013}}</ref> |
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==== Cardiac artifacts ==== |
==== Cardiac artifacts ==== |
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The potential due to cardiac activity introduces [[Electrocardiography|electrocardiograph]] (ECG) errors in the EEG.<ref>{{Citation |last=Kaya |first=İbrahim |title=Brain-Computer Interface |chapter=A Brief Summary of EEG Artifact Handling |date=2022-05-18 |chapter-url=https://www.intechopen.com/chapters/77731 |series=Artificial Intelligence |volume=9 |editor-last=Asadpour |editor-first=Vahid |publisher=IntechOpen |language=en |doi=10.5772/intechopen.99127 |isbn=978-1-83962-522-0 |s2cid=209832569 |access-date=2022-12-20}}</ref> Artifacts arising due to cardiac activity may be removed with the help of an ECG reference signal.<ref name="ReferenceA"/> |
The potential due to cardiac activity introduces [[Electrocardiography|electrocardiograph]] (ECG) errors in the EEG.<ref>{{Citation |last=Kaya |first=İbrahim |title=Brain-Computer Interface |chapter=A Brief Summary of EEG Artifact Handling |date=2022-05-18 |chapter-url=https://www.intechopen.com/chapters/77731 |series=Artificial Intelligence |volume=9 |editor-last=Asadpour |editor-first=Vahid |publisher=IntechOpen |language=en |doi=10.5772/intechopen.99127 |isbn=978-1-83962-522-0 |s2cid=209832569 |access-date=2022-12-20 |archive-date=2022-12-20 |archive-url=https://web.archive.org/web/20221220022603/https://www.intechopen.com/chapters/77731 |url-status=live }}</ref> Artifacts arising due to cardiac activity may be removed with the help of an ECG reference signal.<ref name="ReferenceA" /> |
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==== Other physiological artifacts ==== |
==== Other physiological artifacts ==== |
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The [[United States Department of Defense|Department of Defense]] (DoD) and [[United States Department of Veterans Affairs|Veteran's Affairs]] (VA), and [[United States Army Research Laboratory|U.S Army Research Laboratory]] (ARL), collaborated on EEG diagnostics in order to detect [[MTBI|mild to moderate Traumatic Brain Injury]] (mTBI) in combat soldiers.<ref name=":4">{{cite journal | vauthors = Rapp PE, Keyser DO, Albano A, Hernandez R, Gibson DB, Zambon RA, Hairston WD, Hughes JD, Krystal A, Nichols AS | display-authors = 6 | title = Traumatic brain injury detection using electrophysiological methods | journal = Frontiers in Human Neuroscience | volume = 9 | pages = 11 | date = 2015 | pmid = 25698950 | pmc = 4316720 | doi = 10.3389/fnhum.2015.00011 | doi-access = free }}</ref> Between 2000 and 2012, 75 percent of U.S. military operations brain injuries were classified mTBI. In response, the DoD pursued new technologies capable of rapid, accurate, non-invasive, and field-capable detection of mTBI to address this injury.<ref name=":4" /> |
The [[United States Department of Defense|Department of Defense]] (DoD) and [[United States Department of Veterans Affairs|Veteran's Affairs]] (VA), and [[United States Army Research Laboratory|U.S Army Research Laboratory]] (ARL), collaborated on EEG diagnostics in order to detect [[MTBI|mild to moderate Traumatic Brain Injury]] (mTBI) in combat soldiers.<ref name=":4">{{cite journal | vauthors = Rapp PE, Keyser DO, Albano A, Hernandez R, Gibson DB, Zambon RA, Hairston WD, Hughes JD, Krystal A, Nichols AS | display-authors = 6 | title = Traumatic brain injury detection using electrophysiological methods | journal = Frontiers in Human Neuroscience | volume = 9 | pages = 11 | date = 2015 | pmid = 25698950 | pmc = 4316720 | doi = 10.3389/fnhum.2015.00011 | doi-access = free }}</ref> Between 2000 and 2012, 75 percent of U.S. military operations brain injuries were classified mTBI. In response, the DoD pursued new technologies capable of rapid, accurate, non-invasive, and field-capable detection of mTBI to address this injury.<ref name=":4" /> |
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Combat personnel often develop PTSD and mTBI in correlation. Both conditions present with altered low-frequency brain wave oscillations.<ref>{{cite journal | vauthors = Franke LM, Walker WC, Hoke KW, Wares JR | title = Distinction in EEG slow oscillations between chronic mild traumatic brain injury and PTSD | journal = International Journal of Psychophysiology | volume = 106 | pages = 21–29 | date = August 2016 | pmid = 27238074 | doi = 10.1016/j.ijpsycho.2016.05.010 }}</ref> Altered brain waves from PTSD patients present with decreases in low-frequency oscillations, whereas, mTBI injuries are linked to increased low-frequency wave oscillations. Effective EEG diagnostics can help doctors accurately identify conditions and appropriately treat injuries in order to mitigate long-term effects.<ref name=":5">{{Cite web|url=https://www.research.va.gov/currents/1116-2.cfm|title=Study: EEG can help tell apart PTSD, mild traumatic brain injury|website=www.research.va.gov|access-date=2019-10-09}}</ref> |
Combat personnel often develop PTSD and mTBI in correlation. Both conditions present with altered low-frequency brain wave oscillations.<ref>{{cite journal | vauthors = Franke LM, Walker WC, Hoke KW, Wares JR | title = Distinction in EEG slow oscillations between chronic mild traumatic brain injury and PTSD | journal = International Journal of Psychophysiology | volume = 106 | pages = 21–29 | date = August 2016 | pmid = 27238074 | doi = 10.1016/j.ijpsycho.2016.05.010 }}</ref> Altered brain waves from PTSD patients present with decreases in low-frequency oscillations, whereas, mTBI injuries are linked to increased low-frequency wave oscillations. Effective EEG diagnostics can help doctors accurately identify conditions and appropriately treat injuries in order to mitigate long-term effects.<ref name=":5">{{Cite web|url=https://www.research.va.gov/currents/1116-2.cfm|title=Study: EEG can help tell apart PTSD, mild traumatic brain injury|website=www.research.va.gov|access-date=2019-10-09|archive-date=2019-10-09|archive-url=https://web.archive.org/web/20191009195224/https://www.research.va.gov/currents/1116-2.cfm|url-status=live}}</ref> |
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Traditionally, clinical evaluation of EEGs involved visual inspection. Instead of a visual assessment of brain wave oscillation topography, quantitative electroencephalography (qEEG), computerized algorithmic methodologies, analyzes a specific region of the brain and transforms the data into a meaningful "power spectrum" of the area.<ref name=":4" /> Accurately differentiating between mTBI and PTSD can significantly increase positive recovery outcomes for patients especially since long-term changes in neural communication can persist after an initial mTBI incident.<ref name=":5" /> |
Traditionally, clinical evaluation of EEGs involved visual inspection. Instead of a visual assessment of brain wave oscillation topography, quantitative electroencephalography (qEEG), computerized algorithmic methodologies, analyzes a specific region of the brain and transforms the data into a meaningful "power spectrum" of the area.<ref name=":4" /> Accurately differentiating between mTBI and PTSD can significantly increase positive recovery outcomes for patients especially since long-term changes in neural communication can persist after an initial mTBI incident.<ref name=":5" /> |
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Another common measurement made from EEG data is that of complexity measures such as [[Lempel-Ziv complexity]], [[fractal dimension]], and [[spectral flatness]],<ref name="Burns et al 2015"/> which are associated with particular pathologies or pathology stages. |
Another common measurement made from EEG data is that of complexity measures such as [[Lempel-Ziv complexity]], [[fractal dimension]], and [[spectral flatness]],<ref name="Burns et al 2015" /> which are associated with particular pathologies or pathology stages. |
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== Economics == |
== Economics == |
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Inexpensive EEG devices exist for the low-cost research and consumer markets. Recently, a few companies have miniaturized medical grade EEG technology to create versions accessible to the general public. Some of these companies have built commercial EEG devices retailing for less than US$100. |
Inexpensive EEG devices exist for the low-cost research and consumer markets. Recently, a few companies have miniaturized medical grade EEG technology to create versions accessible to the general public. Some of these companies have built commercial EEG devices retailing for less than US$100. |
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* In 2004 OpenEEG released its ModularEEG as open source hardware. Compatible open source software includes a game for balancing a ball. |
* In 2004 OpenEEG released its ModularEEG as open source hardware. Compatible open source software includes a game for balancing a ball. |
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* In 2007 [[NeuroSky]] released the first affordable consumer based EEG along with the game NeuroBoy. This was also the first large scale EEG device to use dry sensor technology.<ref>{{cite news|url= http://www.economist.com/science/displaystory.cfm?story_id=8847846 |title=Mind Games |date= 2007-03-23 |publisher=The Economist }}</ref> |
* In 2007 [[NeuroSky]] released the first affordable consumer based EEG along with the game NeuroBoy. This was also the first large scale EEG device to use dry sensor technology.<ref>{{cite news |url= http://www.economist.com/science/displaystory.cfm?story_id=8847846 |title= Mind Games |date= 2007-03-23 |publisher= The Economist |access-date= 2010-08-12 |archive-date= 2009-12-12 |archive-url= https://web.archive.org/web/20091212220903/http://www.economist.com/science/displaystory.cfm?story_id=8847846 |url-status= live }}</ref> |
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* In 2008 [[OCZ Technology]] developed device for use in video games relying primarily on [[electromyography]]. |
* In 2008 [[OCZ Technology]] developed device for use in video games relying primarily on [[electromyography]]. |
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* In 2008 the [[Final Fantasy]] developer [[Square Enix]] announced that it was partnering with NeuroSky to create a game, ''Judecca''.<ref name="Mind reading is on the market">{{cite news|url=https://www.latimes.com/business/la-fi-mind-reader-20100808,0,6235181,full.story|archive-url=https://archive.today/20130104065206/http://www.latimes.com/business/la-fi-mind-reader-20100808,0,6235181,full.story|url-status=dead|archive-date=2013-01-04|title= Mind reading is on the market |date=2010-08-08 |work=[[Los Angeles Times]] | vauthors = Li S }}</ref><ref>{{cite web | |
* In 2008 the [[Final Fantasy]] developer [[Square Enix]] announced that it was partnering with NeuroSky to create a game, ''Judecca''.<ref name="Mind reading is on the market">{{cite news|url=https://www.latimes.com/business/la-fi-mind-reader-20100808,0,6235181,full.story|archive-url=https://archive.today/20130104065206/http://www.latimes.com/business/la-fi-mind-reader-20100808,0,6235181,full.story|url-status=dead|archive-date=2013-01-04|title= Mind reading is on the market |date=2010-08-08 |work=[[Los Angeles Times]] | vauthors = Li S }}</ref><ref>{{cite web |vauthors=Fruhlinger J |date=9 October 2008 |url=https://www.engadget.com/2008/10/09/brains-on-with-neurosky-and-squareenixs-judecca-mind-control-ga/ |title=Brains-on with NeuroSky and Square Enix's Judecca mind-control game |publisher=Engadget |access-date=2010-12-02 |archive-date=2010-10-30 |archive-url=https://web.archive.org/web/20101030014828/http://www.engadget.com/2008/10/09/brains-on-with-neurosky-and-squareenixs-judecca-mind-control-ga/ |url-status=live }}</ref> |
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* In 2009 [[Mattel]] partnered with NeuroSky to release the [[Mindflex]], a game that used an EEG to steer a ball through an obstacle course. By far the best-selling consumer based EEG to date.<ref name="Mind reading is on the market"/><ref>{{cite web|url=http://www.physorg.com/news150781868.html |title=New games powered by brain waves |publisher=Physorg.com |access-date=2010-12-02 |url-status=dead |archive-url=https://web.archive.org/web/20110606080916/http://www.physorg.com/news150781868.html |archive-date=2011-06-06 }}</ref> |
* In 2009 [[Mattel]] partnered with NeuroSky to release the [[Mindflex]], a game that used an EEG to steer a ball through an obstacle course. By far the best-selling consumer based EEG to date.<ref name="Mind reading is on the market" /><ref>{{cite web|url=http://www.physorg.com/news150781868.html |title=New games powered by brain waves |publisher=Physorg.com |access-date=2010-12-02 |url-status=dead |archive-url=https://web.archive.org/web/20110606080916/http://www.physorg.com/news150781868.html |archive-date=2011-06-06 }}</ref> |
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* In 2009 Uncle Milton Industries partnered with NeuroSky to release the ''[[Star Wars]]'' [[Force Trainer]], a game designed to create the illusion of possessing [[the Force]].<ref name="Mind reading is on the market"/><ref>{{cite news| url=https://www.usatoday.com/life/lifestyle/2009-01-06-force-trainer-toy_N.htm | work=USA Today | title=Toy trains 'Star Wars' fans to use The Force | vauthors |
* In 2009 Uncle Milton Industries partnered with NeuroSky to release the ''[[Star Wars]]'' [[Force Trainer]], a game designed to create the illusion of possessing [[the Force]].<ref name="Mind reading is on the market" /><ref>{{cite news | url=https://www.usatoday.com/life/lifestyle/2009-01-06-force-trainer-toy_N.htm | work=USA Today | title=Toy trains 'Star Wars' fans to use The Force | vauthors=Snider M | date=2009-01-07 | access-date=2010-05-01 | archive-date=2012-10-23 | archive-url=https://web.archive.org/web/20121023104506/https://usatoday30.usatoday.com/life/lifestyle/2009-01-06-force-trainer-toy_N.htm | url-status=live }}</ref> |
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⚫ | * In 2010, NeuroSky added a blink and electromyography function to the MindSet.<ref>{{cite web |url=http://www.gamasutra.com/view/news/29190/NeuroSky_Upgrades_SDK_Allows_For_Eye_Blink_BrainwavePowered_Games.php |title=News – NeuroSky Upgrades SDK, Allows For Eye Blink, Brainwave-Powered Games |publisher=Gamasutra |date=2010-06-30 |access-date=2010-12-02 |archive-date=2017-02-22 |archive-url=https://web.archive.org/web/20170222053401/http://www.gamasutra.com/view/news/29190/NeuroSky_Upgrades_SDK_Allows_For_Eye_Blink_BrainwavePowered_Games.php |url-status=live }}</ref> |
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access-date=2010-05-01}}</ref> |
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⚫ | * In 2011, NeuroSky released the MindWave, an EEG device designed for educational purposes and games.<ref name="educationmindwave">{{cite web |url= http://www.ubergizmo.com/2011/03/neurosky-mindwave/ |title= NeuroSky MindWave Brings Brain-Computer Interface to Education |vauthors= Fiolet E |website= www.ubergizmo.com |publisher= Ubergizmo |access-date= 2015-05-18 |archive-date= 2017-12-12 |archive-url= https://web.archive.org/web/20171212142303/http://www.ubergizmo.com/2011/03/neurosky-mindwave/ |url-status= live }}</ref> The MindWave won the Guinness Book of World Records award for "Heaviest machine moved using a brain control interface".<ref name="Guinness">{{cite web|url= http://neurogadget.com/2011/04/12/neurosky-mindwave-sets-guinness-world-record-for-%E2%80%9Clargest-object-moved-using-a-brain-computer-interface%E2%80%9D/1820|title= NeuroSky MindWave Sets Guinness World Record for "Largest Object Moved Using a Brain-Computer Interface"|website= NeuroGadget.com|publisher= NeuroGadget|access-date= 2011-06-02|archive-url= https://web.archive.org/web/20131015064018/http://neurogadget.com/2011/04/12/neurosky-mindwave-sets-guinness-world-record-for-%E2%80%9Clargest-object-moved-using-a-brain-computer-interface%E2%80%9D/1820|archive-date= 2013-10-15|url-status= dead}}</ref> |
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⚫ | * In 2010, NeuroSky added a blink and electromyography function to the MindSet.<ref>{{cite web|url=http://www.gamasutra.com/view/news/29190/NeuroSky_Upgrades_SDK_Allows_For_Eye_Blink_BrainwavePowered_Games.php |title=News |
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⚫ | * In 2011, NeuroSky released the MindWave, an EEG device designed for educational purposes and games.<ref name="educationmindwave">{{cite web|url= http://www.ubergizmo.com/2011/03/neurosky-mindwave/ |title= NeuroSky MindWave Brings Brain-Computer Interface to Education | |
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* In 2012, a Japanese gadget project, [[neurowear]], released Necomimi: a headset with motorized cat ears. The headset is a NeuroSky MindWave unit with two motors on the headband where a cat's ears might be. Slipcovers shaped like cat ears sit over the motors so that as the device registers emotional states the ears move to relate. For example, when relaxed, the ears fall to the sides and perk up when excited again. |
* In 2012, a Japanese gadget project, [[neurowear]], released Necomimi: a headset with motorized cat ears. The headset is a NeuroSky MindWave unit with two motors on the headband where a cat's ears might be. Slipcovers shaped like cat ears sit over the motors so that as the device registers emotional states the ears move to relate. For example, when relaxed, the ears fall to the sides and perk up when excited again. |
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* In 2014, OpenBCI released an eponymous [[open source]] brain-computer interface after a successful kickstarter campaign in 2013. The basic [[OpenBCI]] has 8 channels, expandable to 16, and supports EEG, [[EKG]], and [[Electromyography|EMG]]. The OpenBCI is based on the Texas Instruments ADS1299 [[Integrated circuit|IC]] and the Arduino or PIC microcontroller, and costs $399 for the basic version. It uses standard metal cup electrodes and conductive paste. |
* In 2014, OpenBCI released an eponymous [[open source]] brain-computer interface after a successful kickstarter campaign in 2013. The basic [[OpenBCI]] has 8 channels, expandable to 16, and supports EEG, [[EKG]], and [[Electromyography|EMG]]. The OpenBCI is based on the Texas Instruments ADS1299 [[Integrated circuit|IC]] and the Arduino or PIC microcontroller, and costs $399 for the basic version. It uses standard metal cup electrodes and conductive paste. |
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* In 2015, [[Mind Solutions Inc]] released the smallest consumer BCI to date, the [[NeuroSync]]. This device functions as a dry sensor at a size no larger than a [[Bluetooth]] ear piece.<ref name="prnewswire, 2017">{{cite web|title=Product Launch! Neurosync |
* In 2015, [[Mind Solutions Inc]] released the smallest consumer BCI to date, the [[NeuroSync]]. This device functions as a dry sensor at a size no larger than a [[Bluetooth]] ear piece.<ref name="prnewswire, 2017">{{cite web|title=Product Launch! Neurosync – The World's Smallest Brain-Computer-Interface|url=http://www.prnewswire.com/news-releases/product-launch-neurosync---the-worlds-smallest-brain-computer-interface-300113433.html|website=www.prnewswire.com|access-date=July 21, 2017|language=en|date=July 15, 2015|archive-date=December 9, 2018|archive-url=https://web.archive.org/web/20181209221332/https://www.prnewswire.com/news-releases/product-launch-neurosync---the-worlds-smallest-brain-computer-interface-300113433.html|url-status=live}}</ref> |
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* In 2015, A Chinese-based company [[Macrotellect Ltd|Macrotellect]] released [[BrainLink Pro]] and [[BrainLink Lite]], a [[consumer grade]] EEG wearable product providing 20 brain fitness enhancement Apps on [[Apple Inc.|Apple]] and [[Android App Store]]s.<ref>{{Cite web|url=http://o.macrotellect.com/app.html|title=APP |
* In 2015, A Chinese-based company [[Macrotellect Ltd|Macrotellect]] released [[BrainLink Pro]] and [[BrainLink Lite]], a [[consumer grade]] EEG wearable product providing 20 brain fitness enhancement Apps on [[Apple Inc.|Apple]] and [[Android App Store]]s.<ref>{{Cite web|url=http://o.macrotellect.com/app.html|title=APP – Macrotellect|website=o.macrotellect.com|access-date=2016-12-08|archive-date=2017-01-23|archive-url=https://web.archive.org/web/20170123204243/http://o.macrotellect.com/app.html|url-status=live}}</ref> |
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* In 2021, [[Bioserenity|BioSerenity]] release the Neuronaute and Icecap a single-use disposable EEG headset that allows recording with equivalent quality to traditional cup electrodes.<ref>{{Cite web|title=510(k) Premarket Notification|url=https://www.accessdata.fda.gov/scripts/cdrh/cfdocs/cfpmn/pmn.cfm?ID=K202334|access-date=2021-11-12|website=www.accessdata.fda.gov}}</ref><ref>{{Cite web|last=niamhcurran|date=2021-01-08|title=BioSerenity receives FDA clearance for EEG wearable device system|url=https://neuronewsinternational.com/bioserenity-receives-fda-clearance-for-eeg-wearable-device-system/|access-date=2021-11-12|website=NeuroNews International|language=en-GB}}</ref> |
* In 2021, [[Bioserenity|BioSerenity]] release the Neuronaute and Icecap a single-use disposable EEG headset that allows recording with equivalent quality to traditional cup electrodes.<ref>{{Cite web|title=510(k) Premarket Notification|url=https://www.accessdata.fda.gov/scripts/cdrh/cfdocs/cfpmn/pmn.cfm?ID=K202334|access-date=2021-11-12|website=www.accessdata.fda.gov|archive-date=2021-11-12|archive-url=https://web.archive.org/web/20211112211158/https://www.accessdata.fda.gov/scripts/cdrh/cfdocs/cfpmn/pmn.cfm?ID=K202334|url-status=live}}</ref><ref>{{Cite web|last=niamhcurran|date=2021-01-08|title=BioSerenity receives FDA clearance for EEG wearable device system|url=https://neuronewsinternational.com/bioserenity-receives-fda-clearance-for-eeg-wearable-device-system/|access-date=2021-11-12|website=NeuroNews International|language=en-GB|archive-date=2021-11-12|archive-url=https://web.archive.org/web/20211112211156/https://neuronewsinternational.com/bioserenity-receives-fda-clearance-for-eeg-wearable-device-system/|url-status=live}}</ref> |
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* In 2023, a Swiss-based company [https://iduntechnologies.com/ IDUN Technologies] released the [https://iduntechnologies.com/idun-guardian/ IDUN Guardian], the first commercially available in-ear EEG earbuds with a software solution that allows for the recording, processing, storing, and analysis of long periods of contextualized EEG data. |
* In 2023, a Swiss-based company [https://iduntechnologies.com/ IDUN Technologies] released the [https://iduntechnologies.com/idun-guardian/ IDUN Guardian], the first commercially available in-ear EEG earbuds with a software solution that allows for the recording, processing, storing, and analysis of long periods of contextualized EEG data. |
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Honda is attempting to develop a system to enable an operator to control its [[Asimo]] robot using EEG, a technology it eventually hopes to incorporate into its automobiles.<ref>{{cite web | url = http://search.japantimes.co.jp/cgi-bin/nb20090401a2.html | title = Mind over matter: Brain waves control Asimo | archive-url = https://web.archive.org/web/20090403021412/http://search.japantimes.co.jp/cgi-bin/nb20090401a2.html | archive-date=2009-04-03 | date = 1 April 2009 | work = Japan Times }}</ref> |
Honda is attempting to develop a system to enable an operator to control its [[Asimo]] robot using EEG, a technology it eventually hopes to incorporate into its automobiles.<ref>{{cite web | url = http://search.japantimes.co.jp/cgi-bin/nb20090401a2.html | title = Mind over matter: Brain waves control Asimo | archive-url = https://web.archive.org/web/20090403021412/http://search.japantimes.co.jp/cgi-bin/nb20090401a2.html | archive-date=2009-04-03 | date = 1 April 2009 | work = Japan Times }}</ref> |
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EEGs have been used as evidence in criminal trials in the [[India]]n state of [[Maharashtra]].<ref>{{cite web | url = |
EEGs have been used as evidence in criminal trials in the [[India]]n state of [[Maharashtra]].<ref>{{cite web | url = http://articles.timesofindia.indiatimes.com/2008-07-21/mumbai/27890043_1_sessions-court-murder-rukmani-krishnamurthy | title = This brain test maps the truth | date = 21 July 2008 | vauthors = Natu N | work = The Times of India | access-date = 2021-04-14 | archive-date = 2012-07-18 | archive-url = https://archive.today/20120718100522/http://articles.timesofindia.indiatimes.com/2008-07-21/mumbai/27890043_1_sessions-court-murder-rukmani-krishnamurthy | url-status = dead }}</ref><ref>{{cite conference | vauthors = Puranik DA, Jospeh SK, Daundkar BB, Garad MV | title = Brain Signature Profiling In India: It's Status As An Aid In Investigation And As Corroborative Evidence-As Seen From Judgments. | conference = Proceedings of XX All India Forensic Science Conference | date = November 2009 | pages = 815–822 | url = http://forensic-centre.com/wp-content/uploads/2013/07/BEOS-IN-INDIA-IT_222S-STATUS-AS-AN-AID-IN-INVESTIGATION-AND-AS-CORROBORATIVE-EVIDENCE-AS-SEEN-FROM-JUDGMENTS_.pdf |archive-url=https://web.archive.org/web/20160303223045/http://forensic-centre.com/wp-content/uploads/2013/07/BEOS-IN-INDIA-IT_222S-STATUS-AS-AN-AID-IN-INVESTIGATION-AND-AS-CORROBORATIVE-EVIDENCE-AS-SEEN-FROM-JUDGMENTS_.pdf |archive-date=2016-03-03 |url-status=dead }}</ref> [[Brain Electrical Oscillation Signature Profiling]] (BEOS), an EEG technique, was used in the trial of ''State of Maharashtra v. Sharma'' to show Sharma remembered using arsenic to poison her ex-fiancé, although the reliability and scientific basis of BEOS is disputed.<ref>{{cite journal |vauthors=Gaudet LM |title=Brain Fingerprinting, Scientific Evidence, and "Daubert": A Cautionary Lesson from India |journal=Jurimetrics |date=2011 |volume=51 |issue=3 |pages=293–318 |jstor=41307131 |url=https://www.jstor.org/stable/41307131 |issn=0897-1277 |access-date=2019-03-20 |archive-date=2021-04-16 |archive-url=https://web.archive.org/web/20210416072703/https://www.jstor.org/stable/41307131 |url-status=live }}</ref> |
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A lot of research is currently being carried out in order to make EEG devices smaller, more portable and easier to use. So called "Wearable EEG" is based upon creating low power wireless collection electronics and 'dry' electrodes which do not require a conductive gel to be used.<ref>{{cite journal | vauthors = Casson A, Yates D, Smith S, Duncan J, Rodriguez-Villegas E | title = Wearable electroencephalography. What is it, why is it needed, and what does it entail? | journal = IEEE Engineering in Medicine and Biology Magazine | volume = 29 | issue = 3 | pages = 44–56 | year = 2010 | pmid = 20659857 | doi = 10.1109/MEMB.2010.936545 | hdl-access = free | s2cid = 1891995 | hdl = 10044/1/5910 }}</ref> Wearable EEG aims to provide small EEG devices which are present only on the head and which can record EEG for days, weeks, or months at a time, as [[ear-EEG]]. Such prolonged and easy-to-use monitoring could make a step change in the diagnosis of chronic conditions such as epilepsy, and greatly improve the end-user acceptance of BCI systems.<ref>{{cite journal | vauthors = Looney D, Kidmose P, Park C, Ungstrup M, Rank M, Rosenkranz K, Mandic D | title = The in-the-ear recording concept: user-centered and wearable brain monitoring | journal = IEEE Pulse | volume = 3 | issue = 6 | pages = 32–42 | date = 2012-11-01 | pmid = 23247157 | doi = 10.1109/MPUL.2012.2216717 | s2cid = 14103460 }}</ref> Research is also being carried out on identifying specific solutions to increase the battery lifetime of Wearable EEG devices through the use of the data reduction approach. |
A lot of research is currently being carried out in order to make EEG devices smaller, more portable and easier to use. So called "Wearable EEG" is based upon creating low power wireless collection electronics and 'dry' electrodes which do not require a conductive gel to be used.<ref>{{cite journal | vauthors = Casson A, Yates D, Smith S, Duncan J, Rodriguez-Villegas E | title = Wearable electroencephalography. What is it, why is it needed, and what does it entail? | journal = IEEE Engineering in Medicine and Biology Magazine | volume = 29 | issue = 3 | pages = 44–56 | year = 2010 | pmid = 20659857 | doi = 10.1109/MEMB.2010.936545 | hdl-access = free | s2cid = 1891995 | hdl = 10044/1/5910 }}</ref> Wearable EEG aims to provide small EEG devices which are present only on the head and which can record EEG for days, weeks, or months at a time, as [[ear-EEG]]. Such prolonged and easy-to-use monitoring could make a step change in the diagnosis of chronic conditions such as epilepsy, and greatly improve the end-user acceptance of BCI systems.<ref>{{cite journal | vauthors = Looney D, Kidmose P, Park C, Ungstrup M, Rank M, Rosenkranz K, Mandic D | title = The in-the-ear recording concept: user-centered and wearable brain monitoring | journal = IEEE Pulse | volume = 3 | issue = 6 | pages = 32–42 | date = 2012-11-01 | pmid = 23247157 | doi = 10.1109/MPUL.2012.2216717 | s2cid = 14103460 }}</ref> Research is also being carried out on identifying specific solutions to increase the battery lifetime of Wearable EEG devices through the use of the data reduction approach. |
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In research, currently EEG is often used in combination with [[machine learning]].<ref>{{cite journal | vauthors = Lotte F, Bougrain L, Cichocki A, Clerc M, Congedo M, Rakotomamonjy A, Yger F | title = A review of classification algorithms for EEG-based brain-computer interfaces: a 10 year update | journal = Journal of Neural Engineering | volume = 15 | issue = 3 | pages = 031005 | date = June 2018 | pmid = 29488902 | doi = 10.1088/1741-2552/aab2f2 | doi-access = free | bibcode = 2018JNEng..15c1005L }}</ref> EEG data are pre-processed then passed on to machine learning algorithms. These algorithms are then trained to recognize different diseases like [[schizophrenia]],<ref>{{cite journal | vauthors = Shim M, Hwang HJ, Kim DW, Lee SH, Im CH | title = Machine-learning-based diagnosis of schizophrenia using combined sensor-level and source-level EEG features | journal = Schizophrenia Research | volume = 176 | issue = 2–3 | pages = 314–319 | date = October 2016 | pmid = 27427557 | doi = 10.1016/j.schres.2016.05.007 | s2cid = 44504680 }}</ref> [[epilepsy]]<ref>{{Cite journal| |
In research, currently EEG is often used in combination with [[machine learning]].<ref>{{cite journal | vauthors = Lotte F, Bougrain L, Cichocki A, Clerc M, Congedo M, Rakotomamonjy A, Yger F | title = A review of classification algorithms for EEG-based brain-computer interfaces: a 10 year update | journal = Journal of Neural Engineering | volume = 15 | issue = 3 | pages = 031005 | date = June 2018 | pmid = 29488902 | doi = 10.1088/1741-2552/aab2f2 | doi-access = free | bibcode = 2018JNEng..15c1005L }}</ref> EEG data are pre-processed then passed on to machine learning algorithms. These algorithms are then trained to recognize different diseases like [[schizophrenia]],<ref>{{cite journal | vauthors = Shim M, Hwang HJ, Kim DW, Lee SH, Im CH | title = Machine-learning-based diagnosis of schizophrenia using combined sensor-level and source-level EEG features | journal = Schizophrenia Research | volume = 176 | issue = 2–3 | pages = 314–319 | date = October 2016 | pmid = 27427557 | doi = 10.1016/j.schres.2016.05.007 | s2cid = 44504680 }}</ref> [[epilepsy]]<ref>{{Cite journal|vauthors=Buettner R, Frick J, Rieg T|date=2019-11-12|title=High-performance detection of epilepsy in seizure-free EEG recordings: A novel machine learning approach using very specific epileptic EEG sub-bands|url=https://aisel.aisnet.org/icis2019/is_health/is_health/16|journal=ICIS 2019 Proceedings|access-date=2021-01-13|archive-date=2021-01-21|archive-url=https://web.archive.org/web/20210121014450/https://aisel.aisnet.org/icis2019/is_health/is_health/16/|url-status=live}}</ref> or [[dementia]].<ref>{{cite journal | vauthors = Ieracitano C, Mammone N, Hussain A, Morabito FC | title = A novel multi-modal machine learning based approach for automatic classification of EEG recordings in dementia | journal = Neural Networks | volume = 123 | pages = 176–190 | date = March 2020 | pmid = 31884180 | doi = 10.1016/j.neunet.2019.12.006 | s2cid = 209510497 }}</ref> Furthermore, they are increasingly used to study seizure detection.<ref>{{cite journal | vauthors = Bhattacharyya A, Pachori RB | title = A Multivariate Approach for Patient-Specific EEG Seizure Detection Using Empirical Wavelet Transform | journal = IEEE Transactions on Bio-Medical Engineering | volume = 64 | issue = 9 | pages = 2003–2015 | date = September 2017 | pmid = 28092514 | doi = 10.1109/TBME.2017.2650259 | s2cid = 3522546 }}</ref><ref>{{cite journal | vauthors = Saab K, Dunnmon J, Ré C, Rubin D, Lee-Messer C | title = Weak supervision as an efficient approach for automated seizure detection in electroencephalography | journal = NPJ Digital Medicine | volume = 3 | issue = 1 | pages = 59 | date = 2020-04-20 | pmid = 32352037 | pmc = 7170880 | doi = 10.1038/s41746-020-0264-0 | doi-access = free }}</ref><ref>{{cite journal | vauthors = Bomela W, Wang S, Chou CA, Li JS | title = Real-time Inference and Detection of Disruptive EEG Networks for Epileptic Seizures | journal = Scientific Reports | volume = 10 | issue = 1 | pages = 8653 | date = May 2020 | pmid = 32457378 | pmc = 7251100 | doi = 10.1038/s41598-020-65401-6 | doi-access = free | bibcode = 2020NatSR..10.8653B }}</ref><ref>{{cite journal | vauthors = Paesschen WV | title = The future of seizure detection | journal = The Lancet. Neurology | volume = 17 | issue = 3 | pages = 200–202 | date = March 2018 | pmid = 29452676 | doi = 10.1016/S1474-4422(18)30034-6 | s2cid = 3376296 }}</ref> By using machine learning, the data can be analyzed automatically. In the long run this research is intended to build algorithms that support physicians in their clinical practice <ref>{{cite journal | vauthors = Chen PC, Liu Y, Peng L | title = How to develop machine learning models for healthcare | journal = Nature Materials | volume = 18 | issue = 5 | pages = 410–414 | date = May 2019 | pmid = 31000806 | doi = 10.1038/s41563-019-0345-0 | s2cid = 122563425 | bibcode = 2019NatMa..18..410C }}</ref> and to provide further insights into diseases.<ref>{{cite journal | vauthors = Rudin C | title = Stop Explaining Black Box Machine Learning Models for High Stakes Decisions and Use Interpretable Models Instead | journal = Nature Machine Intelligence | volume = 1 | issue = 5 | pages = 206–215 | date = May 2019 | pmid = 35603010 | pmc = 9122117 | doi = 10.1038/s42256-019-0048-x | arxiv = 1811.10154 | doi-access = free }}</ref> In this vein, complexity measures of EEG data are often calculated, such as [[Lempel-Ziv complexity]], [[fractal dimension]], and [[spectral flatness]].<ref name="Burns et al 2015" /> It has been shown that combining or multiplying such measures can reveal previously hidden information in EEG data.<ref name="Burns et al 2015" /> |
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EEG signals from musical performers were used to create instant compositions and one CD by the Brainwave Music Project, run at the [[Computer Music Center]] at [[Columbia University]] by [[Brad Garton]] and [[Dave Soldier]].{{Citation needed|date=January 2023}} Similarly, an hour-long recording of the brainwaves of [[Ann Druyan]] was included on the [[Voyager Golden Record]], launched on the ''[[Voyager program|Voyager]]'' probes in 1977, in case any extraterrestrial intelligence could decode her thoughts, which included what it was like to fall in love.{{Citation needed|date=January 2023}} |
EEG signals from musical performers were used to create instant compositions and one CD by the Brainwave Music Project, run at the [[Computer Music Center]] at [[Columbia University]] by [[Brad Garton]] and [[Dave Soldier]].{{Citation needed|date=January 2023}} Similarly, an hour-long recording of the brainwaves of [[Ann Druyan]] was included on the [[Voyager Golden Record]], launched on the ''[[Voyager program|Voyager]]'' probes in 1977, in case any extraterrestrial intelligence could decode her thoughts, which included what it was like to fall in love.{{Citation needed|date=January 2023}} |
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== History == |
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⚫ | In 1875, [[Richard Caton]] (1842–1926), a physician practicing in [[Liverpool]], presented his findings about electrical phenomena of the exposed cerebral hemispheres of rabbits and monkeys in the ''[[British Medical Journal]]''. In 1890, Polish physiologist [[Adolf Beck (physiologist)|Adolf Beck]] published an investigation of spontaneous electrical activity of the brain of rabbits and dogs that included rhythmic oscillations altered by light. Beck started experiments on the electrical brain activity of animals. Beck placed electrodes directly on the surface of the brain to test for sensory stimulation. His observation of fluctuating brain activity led to the conclusion of brain waves.<ref name="Adolf Beck pioneer">{{cite journal |vauthors=Coenen A, Fine E, Zayachkivska O |date=2014 |title=Adolf Beck: a forgotten pioneer in electroencephalography |journal=Journal of the History of the Neurosciences |volume=23 |issue=3 |pages=276–286 |doi=10.1080/0964704x.2013.867600 |pmid=24735457 |s2cid=205664545}}</ref> |
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⚫ | In 1912, Ukrainian physiologist [[Vladimir Pravdich-Neminsky|Vladimir Vladimirovich Pravdich-Neminsky]] published the first animal EEG and the [[evoked potential]] of the [[mammal]]ian (dog).<ref>{{cite journal |vauthors=Pravdich-Neminsky VV |year=1913 |title=Ein Versuch der Registrierung der elektrischen Gehirnerscheinungen |journal=Zentralblatt für Physiologie |volume=27 |pages=951–60}}</ref> In 1914, [[Napoleon Cybulski]] and Jelenska-Macieszyna photographed EEG recordings of experimentally induced seizures.{{citation needed|date=June 2022}} |
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⚫ | German physiologist and psychiatrist [[Hans Berger]] (1873–1941) recorded the first human EEG in 1924.<ref>{{cite journal |vauthors=Haas LF |date=January 2003 |title=Hans Berger (1873–1941), Richard Caton (1842–1926), and electroencephalography |journal=Journal of Neurology, Neurosurgery, and Psychiatry |volume=74 |issue=1 |pages=9 |doi=10.1136/jnnp.74.1.9 |pmc=1738204 |pmid=12486257}}</ref> Expanding on work previously conducted on animals by Richard Caton and others, Berger also invented the electroencephalograph (giving the device its name), an invention described "as one of the most surprising, remarkable, and momentous developments in the history of clinical neurology".<ref>{{cite conference |date=June 2002 |title=The origins of EEG. |url=http://www.bri.ucla.edu/nha/ishn/ab24-2002.htm |conference=7th Annual Meeting of the International Society for the History of the Neurosciences (ISHN) |vauthors=Millet D}}{{dead link|date=March 2023}}</ref> His discoveries were first confirmed by British scientists [[Edgar Douglas Adrian]] and B. H. C. Matthews in 1934 and developed by them. |
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⚫ | In 1934, Fisher and Lowenbach first demonstrated epileptiform spikes. In 1935, Gibbs, Davis and Lennox described [[interictal]] spike waves and the three cycles/s pattern of clinical [[absence seizure]]s, which began the field of clinical electroencephalography.<ref>{{cite journal |vauthors=Gibbs FA, Davis H, Lennox WG |date=December 1935 |title=The Electro-Encephalogram in Epilepsy and in Conditions of Impaired Consciousness |journal=Archives of Neurology and Psychiatry |volume=34 |issue=6 |pages=1133 |doi=10.1001/archneurpsyc.1935.02250240002001}}</ref> Subsequently, in 1936 Gibbs and Jasper reported the interictal spike as the focal signature of epilepsy. The same year, the first EEG laboratory opened at Massachusetts General Hospital.{{citation needed|date=June 2022}} |
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⚫ | In the 1950s, [[William Grey Walter]] developed an adjunct to EEG called [[EEG topography]], which allowed for the mapping of electrical activity across the surface of the brain. This enjoyed a brief period of popularity in the 1980s and seemed especially promising for psychiatry. It was never accepted by neurologists and remains primarily a research tool. |
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⚫ | An electroencephalograph system manufactured by Beckman Instruments was used on at least one of the [[Project Gemini]] manned spaceflights (1965–1966) to monitor the brain waves of astronauts on the flight. It was one of many Beckman Instruments specialized for and used by NASA.<ref name="SNR1965">{{cite news |date=March 3, 1965 |title=Beckman Instruments Supplying Medical Flight Monitoring Equipment |pages=4–5 |work=Space News Roundup |url=https://historycollection.jsc.nasa.gov/JSCHistoryPortal/history/roundups/issues/65-03-03.pdf |url-status=live |access-date=7 August 2019 |archive-url=https://web.archive.org/web/20190807120214/https://historycollection.jsc.nasa.gov/JSCHistoryPortal/history/roundups/issues/65-03-03.pdf |archive-date=7 August 2019}}</ref> |
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⚫ | The first instance of the use of EEG to control a physical object, a robot, was in 1988. The robot would follow a line or stop depending on the alpha activity of the subject. If the subject relaxed and closed their eyes therefore increasing alpha activity, the bot would move. Opening their eyes thus decreasing alpha activity would cause the robot to stop on the trajectory.<ref>{{Cite journal |last=Bozinovski |first=Stevo |date=2013 |editor-last=Markovski |editor-first=Smile |editor2-last=Gusev |editor2-first=Marjan |title=Controlling Robots Using EEG Signals, Since 1988 |url=https://link.springer.com/chapter/10.1007/978-3-642-37169-1_1 |url-status=live |journal=ICT Innovations 2012 |series=Advances in Intelligent Systems and Computing |language=en |location=Berlin, Heidelberg |publisher=Springer |volume=207 |pages=1–11 |doi=10.1007/978-3-642-37169-1_1 |isbn=978-3-642-37169-1 |archive-url=https://web.archive.org/web/20230113013556/https://link.springer.com/chapter/10.1007/978-3-642-37169-1_1 |archive-date=2023-01-13 |access-date=2023-01-13}}</ref> |
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⚫ | In October 2018, scientists connected the brains of three people to experiment with the process of thoughts sharing. Five groups of three people participated in the experiment using EEG. The success rate of the experiment was 81%.<ref>{{cite journal |vauthors=Jiang L, Stocco A, Losey DM, Abernethy JA, Prat CS, Rao RP |date=April 2019 |title=BrainNet: A Multi-Person Brain-to-Brain Interface for Direct Collaboration Between Brains |journal=Scientific Reports |volume=9 |issue=1 |pages=6115 |arxiv=1809.08632 |bibcode=2019NatSR...9.6115J |doi=10.1038/s41598-019-41895-7 |pmc=6467884 |pmid=30992474 |doi-access=free}}</ref> |
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== See also == |
== See also == |