Electroencephalography

An EEG recording setup using the 10-10 system of electrode placement.

EEG is the gold standard diagnostic procedure to confirm epilepsy. The sensitivity of a routine EEG to detect interictal epileptiform discharges at epilepsy centers has been reported to be in the range of 29–55%.[5] 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 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)[5] or with a very low risk of developing epilepsy in the future.[6]

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.[5]

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 While in the hospital, seizure medications are usually withdrawn to increase the odds that a seizure will occur during admission. For reasons of safety, medications are not withdrawn during an EEG outside of the hospital.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.

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 characterize seizures and localize/lateralize the region of the brain from which a seizure originates. This can help identify appropriate non-medication treatment options.[7] 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.

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 [8] based on the dynamics of the alpha band.

An EEG might also be helpful for diagnosing or treating the following disorders:[9]

• Brain tumor
• Brain damage from head injury
• Brain dysfunction that can have a variety of causes (encephalopathy)
• GLUT1 deficiency syndrome[10]
• Inflammation of the brain (encephalitis)
• Stroke
• Sleep disorders

It can also:

• distinguish epileptic seizures from other types of spells, such as psychogenic non-epileptic seizures, syncope (fainting), sub-cortical movement disorders and migraine variants
• differentiate "organic" encephalopathy or delirium from primary psychiatric syndromes such as catatonia
• serve as an adjunct test of brain death in comatose patients
• prognosticate in comatose patients (in certain instances) or in newborns with brain injury from various causes around the time of birth
• determine whether to wean anti-epileptic medications.

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).

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 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.[11]

EEG is not indicated for diagnosing headaches.[12] 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.[12]

Sometimes it is more convenient or clinically necessary to perform ambulatory EEG recordings in the home of the patient. These studies typically have a duration of 24–72 hours.

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, the "one-dimensional signals from localised peripheral regions on the head make it attractive for its simplistic fidelity and has allowed high clinical and basic research throughput".[17] Thus, EEG possesses some advantages over some of those other techniques:

• Hardware costs are significantly lower than those of most other techniques [18]
• EEG prevents limited availability of technologists to provide immediate care in high traffic hospitals.[19]
• 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;[20] and fMRI requires the use of a 1-ton magnet in, again, a shielded room.
• 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.[21] EEG is commonly recorded at sampling rates between 250 and 2000 Hz in clinical and research settings.
• 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 [22]
• EEG is silent, which allows for better study of the responses to auditory stimuli.
• EEG does not aggravate claustrophobia, unlike fMRI, PET, MRS, SPECT, and sometimes MEG[23]
• 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[24]
• EEG does not involve exposure to radioligands, unlike positron emission tomography.[25]
• ERP studies can be conducted with relatively simple paradigms, compared with IE block-design fMRI studies
• Relatively non-invasive, in contrast to electrocorticography, which requires electrodes to be placed on the actual 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)[26]
• EEG can be used in subjects who are incapable of making a motor response[27]
• Some ERP components can be detected even when the subject is not attending to the stimuli
• Unlike other means of studying reaction time, ERPs can elucidate stages of processing (rather than just the result)[28]
• 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.

[29]

• 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.[30]
• Depending on the orientation and location of the dipole causing an EEG change, there may be a false localization due to the inverse problem.[31]
• 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.[25]
• 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.[32]

Simultaneous EEG recordings and fMRI scans have been obtained successfully,[33][34][35][36] 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.[37][38]

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:

• 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.[39]

Recently, a combined EEG/MEG (EMEG) approach has been investigated for the purpose of source reconstruction in epilepsy diagnosis.[40]

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.[41]

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),[42] mental emotional states (Negative, Neutral, Positive)[43] and thalamocortical dysrhythmia.[44]

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.[55] 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.[56]

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.[57]

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.[58] 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.[59] Additionally, it has been observed that wet electrode sensors' performance reduces after a span of hours.[58] 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.[59][58] Some EEG systems attempt to circumvent this issue by applying a saline solution.[60] Others have a semi dry nature and release small amounts of the gel upon contact with the scalp.[59] 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.[58]

ARL also developed a visualization tool, Customizable Lighting Interface for the Visualization of EEGs or CLIVE, which showed how well two brains are synchronized.[61]

Currently, headsets are available incorporating dry electrodes with up to 30 channels.[62] 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.[63]

EEG has several limitations. Most important is its poor spatial resolution.[64] EEG is most sensitive to a particular set of post-synaptic potentials: those generated in superficial layers of the cortex, on the crests of gyri directly abutting the skull and radial to the skull. Dendrites, which are deeper in the cortex, inside sulci, in midline or deep structures (such as the cingulate gyrus or hippocampus), or producing currents that are tangential to the skull, have far less contribution to the EEG signal.

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.[20] 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.[65]

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.

It is mathematically impossible to reconstruct a unique intracranial current source for a given EEG signal,[11] 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 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.[66] Other methods of looking at brain activity, such as PET, fMRI or 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., SPECT, fMRI, fUS) or metabolic activity (e.g., PET, NIRS), which are indirect markers of brain electrical activity.

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.

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.[47][67] 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[20] are associated with intracellular ionic currents.[68]

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.

Human EEG with prominent alpha-rhythm

Delta waves

• Delta waves 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).

Theta waves

• Theta is the frequency range from 4 Hz to 7 Hz. Theta is seen normally in young children. It may be seen in drowsiness or arousal in older children and adults; it can also be seen in meditation.[76] Excess theta for age represents abnormal activity. It can be seen as a focal disturbance in focal subcortical lesions; it can be seen in generalized distribution in diffuse disorder or metabolic encephalopathy or deep midline disorders or some instances of hydrocephalus. On the contrary this range has been associated with reports of relaxed, meditative, and creative states.

Alpha waves

• Alpha is the frequency range from 8 Hz to 12 Hz.[77] 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).

Sensorimotor rhythm aka mu rhythm

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).[78][79] 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 waves

• Beta is the frequency range from 13 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.[80] 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.

Gamma waves

• 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.[11]
• 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]

"Ultra-slow" or "near-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.

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 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.[81] 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, EMG) to define 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.

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 opiates. 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.[82]

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 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.

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.[84] 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.[84]

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[85] and can be used in real-time to remove artifacts on EEG sedline monitor. This approach performs better than classical soft and hard thresholding.[86]

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.

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.[87]

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).[88]

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.[87]

Another source of artifacts are various muscle movements across the body. This particular class of artifact is usually recorded by all electrodes on the scalp due to myogenic activity (increase or decrease of blood pressure). The origin of these artifacts have no single location and arises from functionally independent muscle groups, meaning the characteristics of the artifact are not constant. The observed patterns due to muscular artifacts will change depending on the subjects sex, the particular muscle tissue and its degree of contraction. The frequency range for muscular artifacts is wide and overlaps with every classic EEG rhythm. However most of the power is concentrated in the lower range of the observed frequencies of 20 to 300 Hz making the gamma band particularly susceptible to muscular artifacts. Some muscle artifacts may have activity with a frequency as low as 2 Hz which and delta and theta bands may also be affected by muscle activity. Muscular artifacts may impact sleep studies as unconscious bruxism (grinding of teeth) or snoring can seriously impact the quality of the recorded EEG. In addition the recordings made of epilepsy patients may be significantly impacted by the existence of muscular artifacts.[89]

The potential due to cardiac activity introduces electrocardiograph (ECG) errors in the EEG.[90] Artifacts arising due to cardiac activity may be removed with the help of an ECG reference signal.[83]

Glossokinetic artifacts are caused by the potential difference between the base and the tip of the tongue. Minor tongue movements can contaminate the EEG, especially in parkinsonian and tremor disorders.

In addition to artifacts generated by the body, many artifacts originate from outside the body. Movement by the patient, or even just settling of the electrodes, may cause electrode pops, spikes originating from a momentary change in the impedance of a given electrode. Poor grounding of the EEG electrodes can cause significant 50 or 60 Hz artifact, depending on the local power system's frequency. A third source of possible interference can be the presence of an IV drip; such devices can cause rhythmic, fast, low-voltage bursts, which may be confused for spikes.

Systems for decoding imagined speech from EEG have applications such as in brain–computer interfaces.[92]

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.[93] 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.[93]

Combat personnel often develop PTSD and mTBI in correlation. Both conditions present with altered low-frequency brain wave oscillations.[94] 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.[95]

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.[93] 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.[95]

Another common measurement made from EEG data is that of complexity measures such as Lempel-Ziv complexity, fractal dimension, and spectral flatness,[17] which are associated with particular pathologies or pathology stages.