Electroencephalography (EEG) is a method to record an electrogram of the spontaneous electrical activity of the brain. The bio signals 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 electrodes 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". EEG is widely used both as a clinical diagnostic tool, particularly in epilepsy, and as a research tool in neuroscience. Clinical interpretation of EEG recordings is performed by visual inspection of the tracing, which is the standard method. Quantitative EEG analysis may be used as an adjunct in specific clinical settings. Visual interpretation of EEG is subject to inter-rater and intra-rater variability. Voltage fluctuations measured by the EEG bio amplifier and electrodes allow the evaluation of normal brain activity. As the electrical activity monitored by EEG originates in neurons in the underlying brain tissue, the recordings made by the electrodes on the surface of the scalp vary in accordance with their orientation and distance to the source of the activity. Furthermore, the value recorded is distorted by intermediary tissues and 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 electrodes on the scalp. Deep structures within the brain further away from the electrodes will not contribute directly to an EEG; these include the base of the cortical gyrus, medial walls of the major lobes, hippocampus, thalamus, and brain stem. A healthy human EEG will show certain patterns of activity that correlate with how awake a person is. The range of frequencies one observes are between 1 and 30 Hz, and amplitudes will vary between 20 and 100 μV. The observed frequencies are subdivided into various groups: alpha (8–13 Hz), beta (13–30 Hz), delta (0.5–4 Hz), and theta (4–7 Hz). Alpha waves are observed when a person is in a state of relaxed wakefulness and are mostly prominent over the parietal and occipital sites. During intense mental activity, beta waves are more prominent in frontal areas as well as other regions. If a relaxed person is told to open their eyes, one observes alpha activity decreasing and an increase in beta activity. Theta and delta waves 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 sleep disorders, depth of anesthesia, coma, encephalopathies, cerebral hypoxia after cardiac arrest, and brain death. EEG used to be a first-line method of diagnosis for tumors, stroke, and other focal brain disorders, 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 evoked potentials (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 psychophysiological research.
Uses
Epilepsy
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%. 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) or with a very low risk of developing epilepsy in the future. 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 Unit (EMU) 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. 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.
Other brain disorders An EEG might also be helpful for diagnosing or treating the following disorders:
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:
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 in Alzheimer's disease, changes in the frequency of the basic EEG activity as well as prolonged P300 wave latency have been observed and a combined EEG–ERP methodology for assessing cognitive decline was proposed for early detection of cognitive decline.
Intensive Care Unit (ICU) 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 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.
Home ambulatory EEG Sometimes it is more convenient or clinically necessary to perform ambulatory EEG recordings in the home of the person being tested. These studies typically have a duration of 24–72 hours.
Research use
EEG and the related study of ERPs are used extensively in neuroscience, cognitive science, cognitive psychology, neurolinguistics, and psychophysiological research, as well as to study human functions such as swallowing. Any EEG techniques used in research are not sufficiently standardised for clinical use, and many ERP studies fail to report all of the necessary processing steps for data collection and reduction, limiting the reproducibility and replicability of many studies. Based on a 2024 systematic literature review and meta analysis commissioned by the Patient-Centered Outcomes Research Institute (PCORI), EEG scans cannot be used reliably to assist in making a clinical diagnosis of ADHD. However, EEG continues to be used in research on mental disabilities, such as auditory processing disorder (APD), ADD, and ADHD. EEGs have also been studied for their utility in detecting neurophysiological changes in the brain after concussion, however, at this time there are no advanced imaging techniques that can be used clinically to diagnose or monitor recovery from concussion.
Advantages 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". Thus, EEG possesses some advantages over some of those other techniques:
Hardware costs are significantly lower than those of most other techniques EEG prevents limited availability of technologists to provide immediate care in high traffic hospitals. 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; 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. 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 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 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 EEG does not involve exposure to radioligands, unlike positron emission tomography. 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) EEG can be used in subjects who are incapable of making a motor response 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) 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. 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.
Disadvantages 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. Depending on the orientation and location of the dipole causing an EEG change, there may be a false localization due to the inverse problem. EEG poorly measures neural activity that occurs below the upper layers of the brain (the cortex). Unlike PET and MRS, EEG cannot identify specific locations in the brain at which various neurotransmitters, drugs, etc. can be found. 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 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. EEGs are not currently very compatible with individuals who have coarser or textured hair. Even protective styles can pose issues during testing. Researchers are currently trying to build better options for patients and technicians alike Furthermore, researchers are starting to implement more culturally-informed data collection practices to help reduce racial biases in EEG research.
With other neuroimaging techniques Simultaneous EEG recordings and fMRI scans have been obtained successfully, 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. 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. 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.
Mechanisms The brain's electrical charge is maintained by billions of neurons. Neurons are electrically charged (or "polarized") by membrane transport proteins that pump ions across their membranes. Neurons are constantly exchanging ions with the extracellular milieu, for example to maintain resting potential and to propagate action potentials. Ions of similar charge repel each other, and when many ions are pushed out of many neurons at the same time, they can push their neighbours, who push their neighbours, and so on, in a wave. This process is known as volume conduction. When the wave of ions reaches the electrodes on the scalp, they can push or pull electrons on the metal in the electrodes. Since metal conducts the push and pull of electrons easily, the difference in push or pull voltages between any two electrodes can be measured by a voltmeter. Recording these voltages over time gives us the EEG. 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. Scalp EEG activity shows oscillations at a variety of frequencies. Several of these oscillations have characteristic frequency ranges, spatial distributions and are associated with different states of brain functioning (e.g., waking and the various sleep stages). 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 band and phase in the delta band relating most strongly to neuron spike activity.
Method
In conventional scalp EEG, the recording is obtained by placing electrodes on the scalp with a conductive gel or paste, usually after preparing the scalp area by light abrasion to reduce impedance due to dead skin cells. Many systems typically use electrodes, each of which is attached to an individual wire. Some systems use caps or nets into which electrodes are embedded; this is particularly common when high-density arrays of electrodes are needed. 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 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. 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 dB 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 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). 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.
Sequential montage Each channel (i.e., waveform) represents the difference between two adjacent electrodes. The entire montage consists of a series of these channels. For example, the channel "Fp1-F3" represents the difference in voltage between the Fp1 electrode and the F3 electrode. The next channel in the montage, "F3-C3", represents the voltage difference between F3 and C3, and so on through the entire array of electrodes. 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. "linked ears": which is a physical or mathematical average of electrodes attached to both earlobes or mastoids. Average reference montage The outputs of all of the amplifiers are summed and averaged, and this averaged signal is used as the common reference for each channel. Laplacian montage Each channel represents the difference between an electrode and a weighted average of the surrounding electrodes. 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).
Dry EEG electrodes 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. 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. 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 nanofibers 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. Hybrid dry–wet EEG electrode systems have also been developed to combine the rapid setup of dry electrodes with the signal stability of gel-based recordings. One example is the g.SAHARA Hybrid EEG electrode system, developed by g.tec medical engineering GmbH, an Austria-based neurotechnology company. The system uses active electrodes made of a conductive polymer with multiple pins designed to maintain contact through hair, allowing EEG recordings to be performed either without gel or with optional conductive gel applied through a central opening. This hybrid approach is intended to reduce electrode–skin impedance while preserving setup flexibility, and it has been used in research settings across standard EEG positions defined by the international 10–20 system. 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.
Limitations EEG has several limitations. Most important is its poor spatial resolution. 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, make 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. 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's benefits over fMRI, fNIRS, fUS and PET EEG has several strong points as a tool for exploring brain activity. EEG have excellent temporal resolution and can detect changes over milliseconds, which is particularly useful to assess a reaction to a stimulus considering an action potential takes approximately 0.5–130 milliseconds to propagate across a single neuron, depending on the type of neuron. 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 is also frequently combined with functional near-infrared spectroscopy (fNIRS) to enable simultaneous measurement of fast neuronal electrical activity and slower hemodynamic responses associated with neurovascular coupling. In such multimodal approaches, EEG provides millisecond-level temporal resolution, while fNIRS contributes complementary information on changes in oxygenated and deoxygenated hemoglobin with moderate spatial resolution. Combined EEG–fNIRS measurements can be performed without major technical interference between modalities and are therefore well suited for studies conducted outside highly controlled imaging environments. Integrated wireless EEG–fNIRS research systems have been developed to support synchronized data acquisition in a single wearable setup, including platforms such as the g.Nautilus wireless EEG and fNIRS headset produced by g.tec medical engineering GmbH, which enables concurrent recording of multi-channel EEG and fNIRS signals for applications in cognitive neuroscience, brain–computer interface research, and neurorehabilitation.
EEG vis-à-vis MEG 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. 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 are associated with intracellular ionic currents.
Normal activity
The EEG is typically described in terms of (1) rhythmic activity and (2) transients. The rhythmic activity is divided into bands by frequency. To some degree, these frequency bands are a matter of nomenclature (i.e., any rhythmic activity between 8–12 Hz can be described as "alpha"), but these designations arose because rhythmic activity within a certain frequency range was noted to have a certain distribution over the scalp or a certain biological significance. Frequency bands are usually extracted using spectral methods (for instance Welch) as implemented for instance in freely available EEG software such as EEGLAB or the Neurophysiological Biomarker Toolbox. Computational processing of the EEG is often named quantitative electroencephalography (qEEG). Most of the cerebral signal observed in the scalp EEG falls in the range of 1–20 Hz (activity below or above this rang