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Epileptic spike and wave discharges monitored with EEG

Electroencephalography (EEG) is an electrophysiological monitoring method to record electrical activity of the brain. It is typically noninvasive, with the electrodes placed along the scalp, although invasive electrodes are sometimes used, as in electrocorticography. EEG measures voltage fluctuations resulting from ionic current within the neurons of the brain.[1] Clinically, EEG refers to the recording of the brain's spontaneous electrical activity over a period of time, as recorded from multiple electrodes placed on the scalp.[1] Diagnostic applications generally focus either on event-related potentials or on the spectral content of EEG. The former investigates potential fluctuations time locked to an event, such as 'stimulus onset' or 'button press'. The latter analyses the type of neural oscillations (popularly called "brain waves") that can be observed in EEG signals in the frequency domain.

EEG is most often used to diagnose epilepsy, which causes abnormalities in EEG readings.[2] It is also used to diagnose sleep disorders, depth of anesthesia, coma, encephalopathies, and brain death. EEG used to be a first-line method of diagnosis for tumors, stroke and other focal brain disorders,[3][4] 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 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.




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 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.[5]

In 1912, Ukrainian physiologist Vladimir Vladimirovich Pravdich-Neminsky published the first animal EEG and the evoked potential of the mammalian (dog).[6] 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.[7] Expanding on work previously conducted on animals by Richard Caton and others, Berger also invented the electroencephalogram (giving the device its name), an invention described "as one of the most surprising, remarkable, and momentous developments in the history of clinical neurology".[8] 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 Lowenback first demonstrated epileptiform spikes. In 1935, Gibbs, Davis and Lennox described interictal spike waves and the three cycles/s pattern of clinical absence seizures, which began the field of clinical electroencephalography. 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.




Chuck Kayser with electroencephalograph electrodes and a signal conditioner for use in Project Gemini, 1965

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.[9]

In 1988, report was given by Stevo Bozinovski, Mihail Sestakov, and Liljana Bozinovska on EEG control of a physical object, a robot.[10][11]

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%.[12][13]

Medical use[edit]

An EEG recording setup

EEG is one of the main diagnostic tests for epilepsy. A routine clinical EEG recording typically lasts 20–30 minutes (plus preparation time). It is a test that detects electrical activity in the brain using small, metal discs (electrodes) attached to the scalp. Routinely, EEG is used in clinical circumstances to determine changes in brain activity that might be useful in diagnosing brain disorders, especially epilepsy or another seizure disorder. An EEG might also be helpful for diagnosing or treating the following disorders:[14]

  • Brain tumor
  • Brain damage from head injury
  • Brain dysfunction that can have a variety of causes (encephalopathy)
  • Inflammation of the brain (encephalitis)
  • Stroke
  • Sleep disorders

It can also:

At times, a routine EEG is not sufficient to establish the diagnosis and/or to 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 inter-ictal 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 the disadvantage of a decreased probability of recording a clinical event.

Epilepsy monitoring is typically done to distinguish epileptic seizures from other types of spells, such as psychogenic non-epileptic seizures, syncope (fainting), sub-cortical movement disorders and migraine variants, to characterize seizures for the purposes of treatment, and to localize the region of brain from which a seizure originates for work-up of possible seizure surgery.

Additionally, EEG may be used to monitor the depth of anesthesia, as an indirect indicator of cerebral perfusion in carotid endarterectomy, or to monitor amobarbital effect during the Wada test.

EEG can also be used in intensive care units 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).


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),[15] mental emotional states (Negative, Neutral, Positive)[16] and thalamocortical dysrhythmia.[17]

EEG is not indicated for diagnosing headache.[18] Recurring headache is 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.[18]

Research use[edit]

EEG, and the related study of ERPs are used extensively in neuroscience, cognitive science, cognitive psychology, neurolinguistics and psychophysiological research, but also to study human functions such as swallowing.[19][20][21] Many EEG techniques used in research are not standardised sufficiently for clinical use. But research on mental disabilities, such as auditory processing disorder (APD), ADD, or ADHD, is becoming more widely known and EEGs are used as research and treatment.


Several other methods to study brain function exist, including functional magnetic resonance imaging (fMRI), positron emission tomography (PET), magnetoencephalography (MEG), nuclear magnetic resonance spectroscopy (NMR or MRS), electrocorticography (ECoG), single-photon emission computed tomography (SPECT), near-infrared spectroscopy (NIRS), and event-related optical signal (EROS). Despite the relatively poor spatial sensitivity of EEG, it possesses multiple advantages over some of these techniques:

  • Hardware costs are significantly lower than those of most other techniques [22]
  • EEG prevents limited availability of technologists to provide immediate care in high traffic hospitals.[23]
  • EEG sensors can be used in more places than fMRI, SPECT, PET, MRS, or MEG, as these techniques 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;[24] and fMRI requires the use of a 1-ton magnet in, again, a shielded room.
  • EEG has very high temporal resolution, on the order of milliseconds rather than seconds. EEG is commonly recorded at sampling rates between 250 and 2000 Hz in clinical and research settings, but modern EEG data collection systems are capable of recording at sampling rates above 20,000 Hz if desired. MEG and EROS are the only other noninvasive cognitive neuroscience techniques that acquire data at this level of temporal resolution.[24]
  • 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 [25]
  • EEG does not aggravate claustrophobia, unlike fMRI, PET, MRS, SPECT, and sometimes MEG[26]
  • EEG does not involve exposure to high-intensity (>1 tesla) magnetic fields, as in some of the other techniques, especially MRI and MRS. These can cause a variety of undesirable issues with the data, and also prohibit use of these techniques with participants that have metal implants in their body, such as metal-containing pacemakers[27]
  • EEG does not involve exposure to radioligands, unlike positron emission tomography.[28]
  • ERP studies can be conducted with relatively simple paradigms, compared with IE block-design fMRI studies
  • Extremely uninvasive, unlike Electrocorticography, which actually requires electrodes to be placed on the surface of the brain.

EEG also has some characteristics that compare favorably with behavioral testing:

  • EEG can detect covert processing (i.e., processing that does not require a response)[29]
  • EEG can be used in subjects who are incapable of making a motor response[30]
  • Unlike other means of studying reaction time, ERPs can elucidate stages of processing (rather than just the final end result)[31]
  • EEG is a powerful tool for tracking 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.[32]
  • 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.


  • Low spatial resolution on the scalp. 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.[33]
  • EEG poorly measures neural activity that occurs below the upper layers of the brain (the cortex).
  • Unlike PET and MRS, cannot identify specific locations in the brain at which various neurotransmitters, drugs, etc. can be found.[28]
  • 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.
  • Signal-to-noise ratio is poor, so sophisticated data analysis and relatively large numbers of subjects are needed to extract useful information from EEG.[34]

With other neuroimaging techniques[edit]

Simultaneous EEG recordings and fMRI scans have been obtained successfully,[35][36][37][38] though recording both at the same time effectively requires that several technical difficulties be overcome, such as the presence of ballistocardiographic artifact, MRI pulse artifact and the induction of electrical currents in EEG wires that move within the strong magnetic fields of the MRI. While challenging, these have been successfully overcome in a number of studies.[39][40]



  • EEG requires accurate information about certain aspects of the skull that can only be estimated, such as skull radius, and conductivities of various skull locations. MEG does not have this issue, and a simultaneous analysis allows this to be corrected for.
  • MEG and EEG both detect activity below the surface of the cortex very poorly, and like EEG, the level of error increases with the depth below the surface of the cortex one attempts to examine. However, the errors are very different between the techniques, and combining them thus allows for correction of some of this noise.
  • MEG has access to virtually no sources of brain activity below a few centimetres under the cortex. EEG, on the other hand, can receive signals from greater depth, albeit with a high degree of noise. Combining the two makes it easier to determine what in the EEG signal comes from the surface (since MEG is very accurate in examining signals from the surface of the brain), and what comes from deeper in the brain, thus allowing for analysis of deeper brain signals than either EEG or MEG on its own.[41]

Recently, a combined EEG/MEG (EMEG) approach has been investigated for the purpose of source reconstruction in epilepsy diagnosis.[42]




The electric potential generated by an individual neuron is far too small to be picked up by EEG or MEG.[46] 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.[47]



Computer electroencephalograph Neurovisor-BMM 40


Electrode locations and names are specified by the International 10–20 system[49] 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.[50] A smaller number of electrodes are typically used when recording EEG from neonates. 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.



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 Hz 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 electromyographic signals. An additional 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).[1]

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.


A typical adult human EEG signal is about 10 µV to 100 µV in amplitude when measured from the scalp[51] and is about 10–20 mV when measured from subdural electrodes.


Sequential montage
Referential montage
Each channel represents the difference between a certain electrode and a designated reference electrode. There is no standard position for this reference; it is, however, at a different position than the "recording" electrodes. Midline positions are often used because they do not amplify the signal in one hemisphere vs. the other, such as Cz, Oz, Pz etc. as online reference. The other popular offline references are:
  • REST reference: which is an offline computational reference at infinity where the potential is zero. REST (reference electrode standardization technique) takes the equivalent sources inside the brain of any a set of scalp recordings as springboard to link the actual recordings with any an online or offline( average, linked ears etc.) non-zero reference to the new recordings with infinity zero as the standardized reference. A free software can be found at (Dong L, Li F, Liu Q, Wen X, Lai Y, Xu P and Yao D (2017) MATLAB Toolboxes for Reference Electrode Standardization Technique (REST) of Scalp EEG. Front. Neurosci. 11:601. doi:10.3389/fnins.2017.00601), and for more details and its performance, pls ref to the original paper (Yao, D. (2001). A method to standardize a reference of scalp EEG recordings to a point at infinity. Physiol. Meas. 22, 693–711. doi:10.1088/0967-3334/22/4/305)
  • "linked ears": which is a physical or mathematical average of electrodes attached to both earlobes or mastoids.
Average reference montage
Laplacian montage
Each channel represents the difference between an electrode and a weighted average of the surrounding electrodes.[52]



Dry EEG electrodes[edit]



In 2018, a functional dry electrode composed of a polydimethylsiloxane elastomer filled with conductive carbon nanofibers was reported. This research was conducted at the U.S. Army Research Laboratory.[56] 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.[57] Additionally, it has been observed that wet electrode sensors’ performance reduces after a span of hours.[56] 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.[57][56] Some EEG systems attempt to circumvent this issue by applying a saline solution.[58] Others have a semi dry nature and release small amounts of the gel upon contact with the scalp.[57] 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.[56]

ARL also developed a visualization tool, Customizable Lighting Interface for the Visualization of EEGs or CLIVE, which showed how well two brains are synchronized.[59]

Currently, headsets are available incorporating dry electrodes with up to 30 channels.[60] 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. [61].





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.


EEG vs fMRI, fNIRS and PET[edit]



EEG vs MEG[edit]

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 field potentials representing the EEG.[46][66] More specifically, the scalp electrical potentials that produce EEG are generally thought to be caused by the extracellular ionic currents caused by dendritic electrical activity, whereas the fields producing magnetoencephalographic signals[24] are associated with intracellular ionic currents.[67]


Studies on numerical modeling of EEG and MEG have also been done.[68]

Normal activity[edit]


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.[69]

Comparison of EEG bands[edit]

Comparison of EEG bands
Band Frequency (Hz) Location Normally Pathologically
Delta < 4 frontally in adults, posteriorly in children; high-amplitude waves
  • subcortical lesions
  • diffuse lesions
  • metabolic encephalopathy hydrocephalus
  • deep midline lesions
Theta 4–7 Found in locations not related to task at hand
  • higher in young children
  • drowsiness in adults and teens
  • idling
  • focal subcortical lesions
  • metabolic encephalopathy
  • deep midline disorders
  • some instances of hydrocephalus
Alpha 8–15 posterior regions of head, both sides, higher in amplitude on dominant side. Central sites (c3-c4) at rest
  • relaxed/reflecting
  • closing the eyes
  • coma
Beta 16–31 both sides, symmetrical distribution, most evident frontally; low-amplitude waves
  • range span: active calm → intense → stressed → mild obsessive
  • active thinking, focus, high alert, anxious
Gamma > 32 Somatosensory cortex
  • Displays during cross-modal sensory processing (perception that combines two different senses, such as sound and sight)[72][73]
Mu 8–12 Sensorimotor cortex
  • Shows rest-state motor neurons.[74]

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.

EEG Frequency bands: Improved definitions [76]
Band Frequency (Hz)
Delta < 4
Theta ≥ 4 and < 8
Alpha ≥ 8 and < 14
Beta ≥ 14

Others sometimes divide the bands into sub-bands for the purposes of data analysis.

Human EEG with prominent alpha-rhythm
Human EEG with prominent alpha-rhythm

Wave patterns[edit]

  • Delta is the frequency range up to 4 Hz. It tends to be the highest in amplitude and the slowest waves. It is seen normally in adults in slow-wave sleep. It is also seen normally in babies. It may occur focally with subcortical lesions and in general distribution with diffuse lesions, metabolic encephalopathy hydrocephalus or deep midline lesions. It is usually most prominent frontally in adults (e.g. FIRDA – frontal intermittent rhythmic delta) and posteriorly in children (e.g. OIRDA – occipital intermittent rhythmic delta).
  • Alpha is the frequency range from 7 Hz to 13 Hz.[78] Hans Berger named the first rhythmic EEG activity he observed the "alpha wave". This was the "posterior basic rhythm" (also called the "posterior dominant rhythm" or the "posterior alpha rhythm"), seen in the posterior regions of the head on both sides, higher in amplitude on the dominant side. It emerges with closing of the eyes and with relaxation, and attenuates with eye opening or mental exertion. The posterior basic rhythm is actually slower than 8 Hz in young children (therefore technically in the theta range).
In addition to the posterior basic rhythm, there are other normal alpha rhythms such as the mu rhythm (alpha activity in the contralateral sensory and motor cortical areas) that emerges when the hands and arms are idle; and the "third rhythm" (alpha activity in the temporal or frontal lobes).[79][80] Alpha can be abnormal; for example, an EEG that has diffuse alpha occurring in coma and is not responsive to external stimuli is referred to as "alpha coma".
  • Beta is the frequency range from 14 Hz to about 30 Hz. It is seen usually on both sides in symmetrical distribution and is most evident frontally. Beta activity is closely linked to motor behavior and is generally attenuated during active movements.[81] Low-amplitude beta with multiple and varying frequencies is often associated with active, busy or anxious thinking and active concentration. Rhythmic beta with a dominant set of frequencies is associated with various pathologies, such as Dup15q syndrome, and drug effects, especially benzodiazepines. It may be absent or reduced in areas of cortical damage. It is the dominant rhythm in patients who are alert or anxious or who have their eyes open.
  • 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.[75]



Note that 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 prenatal 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.[82] The EEG in childhood generally has slower frequency oscillations than the adult EEG.




Biological artifacts[edit]

Main types of artifacts in human EEG
Main types of artifacts in human EEG


  • Eye-induced artifacts (includes eye blinks, eye movements and extra-ocular muscle activity)
  • ECG (cardiac) artifacts
  • EMG (muscle activation)-induced artifacts
  • Glossokinetic artifacts

The most prominent eye-induced artifacts are caused by the potential difference between the cornea and retina, which is quite large compared to cerebral potentials. When the eyes and eyelids are completely still, this corneo-retinal dipole does not affect EEG. However, blinks occur several times per minute, the eyes movements occur several times per second. Eyelid movements, occurring mostly during blinking or vertical eye movements, elicit a large potential seen mostly in the difference between the Electrooculography (EOG) channels above and below the eyes. An established explanation of this potential regards the eyelids as sliding electrodes that short-circuit the positively charged cornea to the extra-ocular skin.[83][84] Rotation of the eyeballs, and consequently of the corneo-retinal dipole, increases the potential in electrodes towards which the eyes are rotated, and decrease the potentials in the opposing electrodes.[85] Eye movements called saccades also generate transient electromyographic potentials, known as saccadic spike potentials (SPs).[86] The spectrum of these SPs overlaps the gamma-band (see Gamma wave), and seriously confounds analysis of induced gamma-band responses,[87] requiring tailored artifact correction approaches.[86] Purposeful or reflexive eye blinking also generates electromyographic potentials, but more importantly there is reflexive movement of the eyeball during blinking that gives a characteristic artifactual appearance of the EEG (see Bell's phenomenon).


Some of these artifacts can be useful in various applications. The EOG signals, for instance, can be used to detect[86] and track eye-movements, which are very important in polysomnography, and is also in conventional EEG for assessing possible changes in alertness, drowsiness or sleep.



Environmental artifacts[edit]


Motion artifacts introduce signal noise that can mask the neural signal of interest.[89] Therefore, effective signal noise processing measures were of great interest in the scientific community.[90]

An EEG equipped phantom head can be placed on a motion platform and moved in a sinusoidal fashion. This contraption enabled researchers to study the effectiveness of motion artifact removal algorithms.[89][90] Using the same model of phantom head and motion platform, it was determined that cable sway was a major attributor to motion artifacts. However, increasing the surface area of the electrode had a small but significant effect on reducing the artifact.[89] This research was sponsored by the U.S. Army Research Laboratory as a part of the Cognition and Neuroergonomics Collaborative Technical Alliance.

Artifact correction[edit]

Recently, independent component analysis (ICA) techniques have been used to correct or remove EEG contaminants.[86][91][92][93][94][95] These techniques attempt to "unmix" the EEG signals into some number of underlying components. There are many source separation algorithms, often assuming various behaviors or natures of EEG. Regardless, the principle behind any particular method usually allow "remixing" only those components that would result in "clean" EEG by nullifying (zeroing) the weight of unwanted components. Fully automated artifact rejection methods, which use ICA, have also been developed.[96]

In the last few years, by comparing data from paralysed and unparalysed subjects, EEG contamination by muscle has been shown to be far more prevalent than had previously been realized, particularly in the gamma range above 20 Hz.[97] However, Surface Laplacian has been shown to be effective in eliminating muscle artefact, particularly for central electrodes, which are further from the strongest contaminants.[98] The combination of Surface Laplacian with automated techniques for removing muscle components using ICA proved particularly effective in a follow up study.[99]

Abnormal activity[edit]








Remote communication[edit]


EEG Diagnostics[edit]

The Department of Defense (DoD) and Veteran’s Affairs (VA), and U.S Army Research Laboratory (ARL), collaborated on EEG diagnostics in order to detect mild to moderate Traumatic Brain Injury (mTBI) in combat soldiers.[102] Between 2000 and 2012 seventy-five 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.[102]

Combat personnel often suffer PTSD and mTBI in correlation. Both conditions present with altered low-frequency brain wave oscillations.[103] 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.[104]

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.[102] 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.[104]


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.

  • In 2004 OpenEEG released its ModularEEG as open source hardware. Compatible open source software includes a game for balancing a ball.
  • In 2008 the Final Fantasy developer Square Enix announced that it was partnering with NeuroSky to create a game, Judecca.[106][107]
  • 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.[106][109]
  • In 2009 Emotiv released the EPOC, a 14 channel EEG device. The EPOC is the first commercial BCI to not use dry sensor technology, requiring users to apply a saline solution to electrode pads (which need remoistening after an hour or two of use).[110]
  • In 2010, NeuroSky added a blink and electromyography function to the MindSet.[111]
  • In 2011, NeuroSky released the MindWave, an EEG device designed for educational purposes and games.[112] The MindWave won the Guinness Book of World Records award for "Heaviest machine moved using a brain control interface".[113]
  • 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 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 EMG. The OpenBCI is based on the Texas Instruments ADS1299 IC and the Arduino or PIC microcontroller, and costs $399 for the basic version. It uses standard metal cup electrodes and conductive paste.
  • 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.[114]
  • In 2015, A Chinese-based company Macrotellect released BrainLink Pro and BrainLink Lite, a consumer grade EEG wearable product providing 20 brain fitness enhancement Apps on Apple and Android App Stores.[115]

Future research[edit]

The EEG has been used for many purposes besides the conventional uses of clinical diagnosis and conventional cognitive neuroscience. An early use was during World War II by the U.S. Army Air Corps to screen out pilots in danger of having seizures;[116] long-term EEG recordings in epilepsy patients are still used today for seizure prediction. Neurofeedback remains an important extension, and in its most advanced form is also attempted as the basis of brain computer interfaces.[117] The EEG is also used quite extensively in the field of neuromarketing.

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.


EEGs have been used as evidence in criminal trials in the Indian state of Maharashtra.[119][120] 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 poisoning her ex-fiancé, although the reliability and scientific basis of BEOS is disputed.[121]

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.[122] 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.[123] 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. For example, in the context of epilepsy diagnosis, data reduction has been used to extend the battery lifetime of Wearable EEG devices by intelligently selecting, and only transmitting, diagnostically relevant EEG data.[124]

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.

See also[edit]


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Further reading[edit]

External links[edit]