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Electroencephalography ( EEG) Compare EEC: is a method to record an electrogram of the spontaneous electrical activity of the brain. The biosignal detected by EEG have been shown to represent the postsynaptic potentials of pyramidal neurons in the neocortex and allocortex. It is typically non-invasive, with the EEG placed along the scalp (commonly called "scalp EEG") using the International 10–20 system, or variations of it. Electrocorticography, involving surgical placement of electrodes, is sometimes called "intracranial EEG". Clinical interpretation of EEG recordings is most often performed by visual inspection of the tracing or quantitative EEG.
Voltage fluctuations measured by the EEG bioamplifier and allow the evaluation of normal brain activity. As the electrical activity monitored by EEG originates in in the underlying Human brain, the recordings made by the 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. This means that not all neurons will contribute equally to an EEG signal, with an EEG predominately reflecting the activity of cortical neurons near the on the scalp. Deep structures within the brain further away from the will not contribute directly to an EEG; these include the base of the cortical gyrus, medial walls of the major lobes, hippocampus, thalamus, and Brainstem. (2025). 9781259642234, McGraw-Hill Companies. ISBN 9781259642234
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). 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, 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 wave and are not generally seen in wakefulness – if they are, it is a sign of brain dysfunction.
EEG can detect abnormal electrical discharges such as sharp waves, spikes, or spike-and-wave complexes, as observable in people with epilepsy; thus, it is often used to inform 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 , depth of anesthesia, coma, encephalopathies, cerebral hypoxia after cardiac arrest, and brain death. EEG used to be a first-line method of diagnosis for , stroke, and other focal brain disorders, (2025). 9781455706945, Elsevier. ISBN 9781455706945 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.
Derivatives of the EEG technique include (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 (ERPs) 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 research.
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.
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 (fainting), sub-cortical movement disorders, migraine variants, stroke, etc. In cases of epileptic seizures, continuous EEG monitoring helps to seizure types and localize/lateralize the region of the brain from which a seizure originates. This can help identify appropriate non-medication treatment options. 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.
It can also:
In cases where significant brain injury is suspected, e.g., after cardiac arrest, EEG can provide some prognostic information.
If a patient with epilepsy is being considered for epilepsy surgery to treat epilepsy, 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 (SDE), intracranial EEG (iEEG), 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. (2025). 9780781751261, Lippincott Williams & Wilkins. ISBN 9780781751261
EEG also has some characteristics that compare favorably with behavioral testing:
MRI's produce detailed images created by generating strong magnetic fields that may induce potentially harmful displacement force and torque. These fields produce potentially harmful radio frequency heating and create image artifacts rendering images useless. Due to these potential risks, only certain medical devices can be used in an MR environment.
Similarly, simultaneous recordings with MEG and EEG have also been conducted, which has several advantages over using either technique alone:
Recently, a combined EEG/MEG (EMEG) approach has been investigated for the purpose of source reconstruction in epilepsy diagnosis.
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.
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), mental emotional states (Negative, Neutral, Positive) and thalamocortical dysrhythmia.
The electric potential generated by an individual neuron is far too small to be picked up by EEG or MEG. EEG activity therefore always reflects the summation of the synchronous activity of thousands or millions of neurons that have similar spatial orientation. If the cells do not have similar spatial orientation, their ions do not line up and create waves to be detected. Pyramidal neurons of the cortex are thought to produce the most EEG signal because they are well-aligned and fire together. Because voltage field gradients fall off with the square of distance, activity from deep sources is more difficult to detect than currents near the skull. (2006). 9780716799221, Worth. ISBN 9780716799221
Scalp EEG activity shows oscillations at a variety of frequencies. Several of these oscillations have characteristic , spatial distributions and are associated with different states of brain functioning (e.g., waking and the various Sleep cycle). These oscillations represent synchronized activity over a network of neurons. The neuronal networks underlying some of these oscillations are understood (e.g., the thalamocortical resonance underlying sleep spindles), while many others are not (e.g., the system that generates the posterior basic rhythm). Research that measures both EEG and neuron spiking finds the relationship between the two is complex, with a combination of EEG power in the Gamma wave band and phase in the delta wave band relating most strongly to neuron spike activity.
Electrode locations and names are specified by the International 10–20 system for most clinical and research applications (except when high-density arrays are used). This system ensures that the naming of electrodes is consistent across laboratories. In most clinical applications, 19 recording electrodes (plus ground and system reference) are used. A smaller number of electrodes are typically used when recording EEG from infant. Additional electrodes can be added to the standard set-up when a clinical or research application demands increased spatial resolution for a particular area of the brain. High-density arrays (typically via cap or net) can contain up to 256 electrodes more-or-less evenly spaced around the scalp.
Each electrode is connected to one input of a differential amplifier (one amplifier per pair of electrodes); a common system reference electrode is connected to the other input of each differential amplifier. These amplifiers amplify the voltage between the active electrode and the reference (typically 1,000–100,000 times, or 60–100 decibel of power gain). In analog EEG, the signal is then filtered (next paragraph), and the EEG signal is output as the deflection of pens as paper passes underneath. Most EEG systems these days, however, are digital, and the amplified signal is digitized via an analog-to-digital converter, after being passed through an anti-aliasing filter. Analog-to-digital sampling typically occurs at 256–512 Hz in clinical scalp EEG; sampling rates of up to 20 kHz are used in some research applications.
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.
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 and 35–70 Hz respectively. The high-pass filter typically filters out slow artifact, such as electrogalvanic signals and movement artifact, whereas the low-pass filter filters out high-frequency artifacts, such as electromyography signals. An additional band-stop 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).
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.
As part of an evaluation for epilepsy surgery, it may be necessary to insert electrodes near the surface of the brain, under the surface of the dura mater. This is accomplished via burr hole or craniotomy. This is referred to variously as "electrocorticography (ECoG)", "intracranial EEG (I-EEG)" or "subdural EEG (SD-EEG)". Depth electrodes may also be placed into brain structures, such as the amygdala or hippocampus, structures, which are common epileptic foci and may not be "seen" clearly by scalp EEG. The electrocorticographic signal is processed in the same manner as digital scalp EEG (above), with a couple of caveats. ECoG is typically recorded at higher sampling rates than scalp EEG because of the requirements of Nyquist theorem – the subdural signal is composed of a higher predominance of higher frequency components. Also, many of the artifacts that affect scalp EEG do not impact ECoG, and therefore display filtering is often not needed.
A typical adult human EEG signal is about 10 μV to 100 μV in amplitude when measured from the scalp.
Since an EEG voltage signal represents a difference between the voltages at two electrodes, the display of the EEG for the reading electroencephalographer may be set up in one of several ways. The representation of the EEG channels is referred to as a montage.
When analog (paper) EEGs are used, the technologist switches between montages during the recording in order to highlight or better characterize certain features of the EEG. With digital EEG, all signals are typically digitized and stored in a particular (usually referential) montage; since any montage can be constructed mathematically from any other, the EEG can be viewed by the electroencephalographer in any display montage that is desired.
The EEG is read by a clinical neurophysiologist or neurologist (depending on local custom and law regarding medical specialities), optimally one who has specific training in the interpretation of EEGs for clinical purposes. This is done by visual inspection of the waveforms, called graphoelements. The use of computer signal processing of the EEG – so-called quantitative electroencephalography – is somewhat controversial when used for clinical purposes (although there are many research uses).
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.
In 2018, a functional dry electrode composed of a polydimethylsiloxane elastomer filled with conductive carbon was reported. This research was conducted at the U.S. Army Research Laboratory. EEG technology often involves applying a gel to the scalp which facilitates strong signal-to-noise ratio. This results in more reproducible and reliable experimental results. Since patients dislike having their hair filled with gel, and the lengthy setup requires trained staff on hand, utilizing EEG outside the laboratory setting can be difficult. Additionally, it has been observed that wet electrode sensors' performance reduces after a span of hours. Therefore, research has been directed to developing dry and semi-dry EEG bioelectronic interfaces.
Dry electrode signals depend upon mechanical contact. Therefore, it can be difficult getting a usable signal because of impedance between the skin and the electrode. Some EEG systems attempt to circumvent this issue by applying a saline solution. Others have a semi dry nature and release small amounts of the gel upon contact with the scalp. Another solution uses spring loaded pin setups. These may be uncomfortable. They may also be dangerous if they were used in a situation where a patient could bump their head since they could become lodged after an impact trauma incident.
Currently, headsets are available incorporating dry electrodes with up to 30 channels. 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.
EEG recordings do not directly capture axonal action potentials. An action potential can be accurately represented as a current quadrupole, meaning that the resulting field decreases more rapidly than the ones produced by the current dipole of post-synaptic potentials. In addition, since EEGs represent averages of thousands of neurons, a large population of cells in synchronous activity is necessary to cause a significant deflection on the recordings. Action potentials are very fast and, as a consequence, the chances of field summation are slim. However, neural backpropagation, as a typically longer dendritic current dipole, can be picked up by EEG electrodes and is a reliable indication of the occurrence of neural output.
Not only do EEGs capture dendritic currents almost exclusively as opposed to axonal currents, they also show a preference for activity on populations of parallel dendrites and transmitting current in the same direction at the same time. Pyramidal neurons of cortical layers II/III and V extend apical dendrites to layer I. Currents moving up or down these processes underlie most of the signals produced by electroencephalography.
EEG thus provides information with a large bias in favor of particular neuron types, locations and orientations. So it 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.
It is mathematically impossible to reconstruct a unique intracranial current source for a given EEG signal, 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.
EEG can be used simultaneously with fMRI or fUS so that high-temporal-resolution data can be recorded at the same time as high-spatial-resolution data, however, since the data derived from each occurs over a different time course, the data sets do not necessarily represent exactly the same brain activity. There are technical difficulties associated with combining EEG and fMRI including the need to remove the MRI gradient artifact present during MRI acquisition. Furthermore, currents can be induced in moving EEG electrode wires due to the magnetic field of the MRI.
EEG can be used simultaneously with 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.
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.
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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.
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"Ultra-slow" or "near-Direct current" 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.
Some features of the EEG are transient rather than rhythmic. Spikes and sharp waves may represent seizure activity or interictal activity in individuals with epilepsy or a predisposition toward epilepsy. Other transient features are normal: vertex waves and sleep spindles are seen in normal sleep.
There are types of activity that are statistically uncommon, but not associated with dysfunction or disease. These are often referred to as "normal variants". The mu rhythm is an example of a normal variant.
The normal electroencephalogram (EEG) varies by age. The Fetal EEG and neonatal EEG is quite different from the adult EEG. Fetuses in the third trimester and newborns display two common brain activity patterns: "discontinuous" and "trace alternant." "Discontinuous" electrical activity refers to sharp bursts of electrical activity followed by low frequency waves. "Trace alternant" electrical activity describes sharp bursts followed by short high amplitude intervals and usually indicates quiet sleep in newborns. The EEG in childhood generally has slower frequency oscillations than the adult EEG.
The normal EEG also varies depending on state. The EEG is used along with other measurements (EOG, electromyography) to define Sleep cycle 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.
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 . 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. Recent algorithms based on state-chart representation using EEG signals can now to monitor the brain states during general anesthesia allowing to classify the brain depth under various sedation.
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 against which modern algorithms are evaluated. Blind source separation (BSS) algorithms employed to remove artifacts include principal component analysis (PCA) and independent component analysis (ICA) and several algorithms in this class have been successful at tackling most physiological artifacts. Recent real-time algorithms based on wavelet transport called WQN can now be used to find and replace artifact segment in real-time in the absence of artifact information. These classes of algorithms depend on the continuity of spectral energy in the different frequency bands
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.
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). (1983). 9780397505982, J. B. Lippincott Co.. ISBN 9780397505982
The propagation of the ocular artifact is impacted by multiple factors including the properties of the subject's skull, neuronal tissues and skin but the signal may be approximated as being inversely proportional to the distance from the eyes squared. The electrooculogram (EOG) consists of a series of electrodes measuring voltage changes close to the eye and is the most common tool for dealing with the eye movement artifact in the EEG signal.
Focal epileptiform discharges represent fast, synchronous potentials in a large number of neurons in a somewhat discrete area of the brain. These can occur as interictal activity, between seizures, and represent an area of cortical irritability that may be predisposed to producing epileptic seizures. Interictal discharges are not wholly reliable for determining whether a patient has epilepsy nor where his/her seizure might originate. (See focal epilepsy.)
Generalized epileptiform discharges often have an anterior maximum, but these are seen synchronously throughout the entire brain. They are strongly suggestive of a generalized epilepsy.
Focal non-epileptiform abnormal activity may occur over areas of the brain where there is focal damage of the cortex or white matter. It often consists of an increase in slow frequency rhythms or a loss of normal higher frequency rhythms. It may also appear as focal or unilateral decrease in amplitude of the EEG signal.
Diffuse non-epileptiform abnormal activity may manifest as diffuse abnormally slow rhythms or bilateral slowing of normal rhythms, such as the PBR.
Intracortical Encephalogram electrodes and sub-dural electrodes can be used in tandem to discriminate and discretize artifact from epileptiform and other severe neurological events.
More advanced measures of abnormal EEG signals have also recently received attention as possible biomarkers for different disorders such as Alzheimer's disease.
Combat personnel often develop PTSD and mTBI in correlation. Both conditions present with altered low-frequency brain wave oscillations. 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.
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. 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.
Another common measurement made from EEG data is that of complexity measures such as Lempel-Ziv complexity, fractal dimension, and spectral flatness, which are associated with particular pathologies or pathology stages.
The EEG is altered by drugs that affect brain functions, the chemicals that are the basis for psychopharmacology. Berger's early experiments recorded the effects of drugs on EEG. The science of pharmaco-electroencephalography has developed methods to identify substances that systematically alter brain functions for therapeutic and recreational use.
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.
EEGs have been used as evidence in criminal trials in the state of Maharashtra. 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.
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. 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. 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.
In research, currently EEG is often used in combination with machine learning. EEG data are pre-processed then passed on to machine learning algorithms. These algorithms are then trained to recognize different diseases like schizophrenia, epilepsy or dementia. Furthermore, they are increasingly used to study seizure detection. 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 and to provide further insights into diseases. In this vein, complexity measures of EEG data are often calculated, such as Lempel-Ziv complexity, fractal dimension, and spectral flatness. It has been shown that combining or multiplying such measures can reveal previously hidden information in EEG data.
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. Similarly, an hour-long recording of the brainwaves of Ann Druyan was included on the Voyager Golden Record, launched on the Voyager program probes in 1977, in case any extraterrestrial intelligence could decode her thoughts, which included what it was like to fall in love.
In 1912, Ukrainian physiologist Vladimir Vladimirovich Pravdich-Neminsky published the first animal EEG and the evoked potential of the (dog). In 1914, Napoleon Cybulski and Jelenska-Macieszyna photographed EEG recordings of experimentally induced seizures.
German physiologist and psychiatrist Hans Berger (1873–1941) recorded the first human EEG in 1924. 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". His discoveries were first confirmed by British scientists Edgar Douglas Adrian and B. H. C. Matthews in 1934 and developed by them.
In 1934, Fisher and Lowenbach first demonstrated epileptiform spikes. In 1935, Gibbs, Hallowell Davis and Lennox described interictal spike waves and the three cycles/s pattern of clinical , which began the field of clinical electroencephalography. Subsequently, in 1936 Gibbs and Herbert Jasper reported the interictal spike as the focal signature of epilepsy. The same year, the first EEG laboratory opened at Massachusetts General Hospital.
Franklin Offner (1911–1999), professor of biophysics at Northwestern University developed a prototype of the EEG that incorporated a piezoelectric inkwriter called a Crystograph (the whole device was typically known as the Offner Dynograph).
In 1947, The American EEG Society was founded and the first International EEG congress was held. In 1953 Eugene Aserinsky and Kleitman described REM sleep.
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.
An electroencephalograph system manufactured by Beckman Instruments was used on at least one of the Project Gemini crewed 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.
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. (2025). 9783642371691, Springer. ISBN 9783642371691
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