Advanced
flexible transistor developed at UW-Madison (photo credit: Jung-Hun
Seo/University at Buffalo, State University of New York)
A team of University of Wisconsin–Madison
(UW–Madison) engineers has created “the most functional flexible
transistor in the world,” along with a fast, simple, inexpensive
fabrication process that’s easily scalable to the commercial level.
The development promises to allow manufacturers to add advanced,
smart-wireless capabilities to wearable and mobile devices that curve,
bend, stretch and move.*
The UW–Madison group’s advance is based on a BiCMOS (bipolar
complementary metal oxide semiconductor) thin-film transistor, combining
speed, high current, and low power dissipation (heat and wasted energy)
on just one surface (a silicon nanomembrane, or “Si NM”).**
BiCMOS transistors are the chip of choice for “mixed-signal” devices
(combining analog and digital capabilities), which include many of
today’s portable electronic devices such as cellphones. “The [BiCMOS]
industry standard is very good,” says Zhenqiang (Jack) Ma,
the Lynn H. Matthias Professor and Vilas Distinguished Achievement
Professor in electrical and computer engineering at UW–Madison. “Now we
can do the same things with our transistor — but it can bend.”
The research was described in the inaugural issue of Nature Publishing Group’s open-access journal Flexible Electronics, published Sept. 27, 2017.***
Making traditional BiCMOS flexible electronics is difficult, in part
because the process takes several months and requires a multitude of
delicate, high-temperature steps. Even a minor variation in temperature
at any point could ruin all of the previous steps.
Ma and his collaborators fabricated their flexible electronics on a
single-crystal silicon nanomembrane on a single bendable piece of
plastic. The secret to their success is their unique process, which
eliminates many steps and slashes both the time and cost of fabricating
the transistors.
“In industry, they need to finish these in three months,” he says. “We finished it in a week.”
He says his group’s much simpler, high-temperature process can scale to industry-level production right away.
“The key is that parameters are important,” he says. “One
high-temperature step fixes everything — like glue. Now, we have more
powerful mixed-signal tools. Basically, the idea is for [the flexible
electronics platform] to expand with this.”
* Some companies (such as Samsung) have developed flexible
displays, but not other flexible electronic components in their devices,
Ma explained to KurzweilAI.
**
“Flexible electronics have mainly focused on their form factors such as
bendability, lightweight, and large area with low-cost processability….
To date, all the [silicon, or Si]-based thin-film transistors (TFTs)
have been realized with CMOS technology because of their simple
structure and process. However, as more functions are required in future
flexible electronic applications (i.e., advanced bioelectronic systems
or flexible wireless power applications), an integration of functional
devices in one flexible substrate is needed to handle complex signals
and/or various power levels.” — Jung Hun Seo et al./Flexible Electronics.
The n-channel, p-channel metal-oxide semiconductor field-effect
transistors (N-MOSFETs & P-MOSFETs), and NPN bipolar junction
transistors (BJTs) were realized together on a 340-nm thick Si NM
layer.
*** Co-authors included researchers at the University at Buffalo,
State University of New York, and the University of Texas at Arlington.
This work was supported by the Air Force Office Of Scientific Research.
Abstract of High-performance flexible BiCMOS electronics based on single-crystal Si nanomembrane
In this work, we have demonstrated for the first time integrated
flexible bipolar-complementary metal-oxide-semiconductor (BiCMOS)
thin-film transistors (TFTs) based on a transferable single crystalline
Si nanomembrane (Si NM) on a single piece of bendable plastic substrate.
The n-channel, p-channel metal-oxide semiconductor field-effect
transistors (N-MOSFETs & P-MOSFETs), and NPN bipolar junction
transistors (BJTs) were realized together on a 340-nm thick Si NM layer
with minimized processing complexity at low cost for advanced flexible
electronic applications. The fabrication process was simplified by
thoughtfully arranging the sequence of necessary ion implantation steps
with carefully selected energies, doses and anneal conditions, and by
wisely combining some costly processing steps that are otherwise
separately needed for all three types of transistors. All types of TFTs
demonstrated excellent DC and radio-frequency (RF) characteristics and
exhibited stable transconductance and current gain under bending
conditions. Overall, Si NM-based flexible BiCMOS TFTs offer great
promises for high-performance and multi-functional future flexible
electronics applications and is expected to provide a much larger and
more versatile platform to address a broader range of applications.
Moreover, the flexible BiCMOS process proposed and demonstrated here is
compatible with commercial microfabrication technology, making its
adaptation to future commercial use straightforward.
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 such as in electrocorticography. EEG measures voltage fluctuations resulting from ionic current within the neurons of the brain. In clinical contexts, 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. 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 like 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.
The history of EEG is detailed by Barbara E. Swartz in Electroencephalography and Clinical Neurophysiology.[5] 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 brain to test
for sensory stimulation. His observation of fluctuating brain activity
led to the conclusion of brain waves.[6]
German physiologist and psychiatrist Hans Berger (1873–1941) recorded the first human EEG in 1924.[8]
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".[9] 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.
Franklin Offner (1911–1999), professor of biophysics at Northwestern University developed a prototype of the EEG that incorporated a piezoelectric inkwriter called a Crystograph (the whole device was typically known as the Offner Dynograph).
In 1947, The American EEG Society was founded and the first
International EEG congress was held. In 1953 Aserinsky and Kleitman
described REM sleep.
In the 1950s, William Grey Walter developed an adjunct to EEG called EEG topography,
which allowed for the mapping of electrical activity across the surface
of the brain. This enjoyed a brief period of popularity in the 1980s
and seemed especially promising for psychiatry. It was never accepted
by neurologists and remains primarily a research tool.
In 1988, report was given on EEG control of a physical object, a robot.[10][11]
Medical use
An EEG recording setup
A routine clinical EEG recording typically lasts 20–30 minutes (plus
preparation time) and usually involves recording from scalp electrodes.
Routine EEG is typically used in clinical circumstances to distinguish epilepticseizures from other types of spells, such as psychogenic non-epileptic seizures, syncope (fainting), sub-cortical movement disorders and migraine variants, to differentiate "organic" encephalopathy or delirium from primary psychiatric syndromes such as catatonia, to serve as an adjunct test of brain death, to prognosticate, in certain instances, in patients with coma, and to determine whether to wean anti-epileptic medications.
At times, a routine EEG is not sufficient, particularly when it
is necessary to record a patient while he/she is having a seizure. In
this case, the patient may be admitted to the hospital for days or even
weeks, while EEG is constantly being recorded (along with
time-synchronized video and audio recording). A recording of an actual
seizure (i.e., an ictal
recording, rather than an inter-ictal recording of a possibly epileptic
patient at some period between seizures) can give significantly better
information about whether or not a spell is an epileptic seizure and the
focus in the brain from which the seizure activity emanates.
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).
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. This is because the cerebrospinal fluid, skull and scalp smear
the electrical potentials recorded 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) or intracranial EEG (icEEG)--all terms for
the same thing. 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 even
lower voltage, faster components of brain activity to be seen. Some
clinical sites record from penetrating microelectrodes.[1]
EEG is not indicated for diagnosing headache.[12]
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.[12]
Hardware costs are significantly lower than those of most other techniques [13]
EEG prevents limited availability of technologists to provide immediate care in high traffic hospitals.[14]
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;[15] 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.[15]
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 [16]
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[17]
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[18]
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)[20]
EEG can be used in subjects who are incapable of making a motor response[21]
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 final end result)[22]
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.[23]
In EEG there is a better understanding of what signal is measured as
compared to other research techniques, i.e. 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.[24]
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.[19]
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 keep them in place
(although a cap can be used). 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[25]
With other neuroimaging techniques
Simultaneous EEG recordings and fMRI scans have been obtained successfully,[26][27]
though successful simultaneous recording 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.[28]
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.[29]
Recently, a combined EEG/MEG (EMEG) approach has been investigated
for the purpose of source reconstruction in epilepsy diagnosis.[30]
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.[31]
Mechanisms
The brain's electrical charge is maintained by billions of neurons.[32] 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.[33]
The electric potential generated by an individual neuron is far too small to be picked up by EEG or MEG.[34] 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.[35]
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.[36]
Method
Computer electroencephalograph Neurovisor-BMM 40
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[37]
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.[38] 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 voltage 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).[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.
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[39] and is about 10–20 mV when measured from subdural electrodes.
Since an EEG voltage signal represents a difference between the
voltages at two electrodes, the display of the EEG for the reading
encephalographer 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. Another
popular reference is "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.[40]
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).
Limitations
EEG has several limitations. Most important is its poor spatial resolution.[41]
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.[42]
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.[43]
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,[1] as some currents produce potentials that cancel each other out. This is referred to as the inverse problem. However, much work has been done to produce remarkably good estimates of, at least, a localized electric dipole that represents the recorded currents.[citation needed]
EEG vs fMRI, fNIRS and PET
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.[44] Other methods of looking at brain activity, such as PET and fMRI
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) or metabolic activity (e.g., PET, NIRS), which are indirect markers of brain electrical activity. EEG can be used simultaneously with fMRI
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 these two modalities,
including the need to remove the MRI gradient artifact present
during MRI acquisition and the ballistocardiographic artifact (resulting
from the pulsatile motion of blood and tissue) from the EEG.
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
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 local hemodynamics.
EEG vs 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.[34][45]
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[15] are associated with intracellular ionic currents.[46]
EEG can be recorded at the same time as MEG so that data from these complementary high-time-resolution techniques can be combined.
Studies on numerical modeling of EEG and MEG have also been done.[47]
Normal activity
One second of EEG signal
The sample of human EEG with in resting state. Left: EEG traces
(horizontal – time in seconds; vertical – amplitudes, scale 100 μV).
Right: power spectra of shown signals (vertical lines – 10 and 20 Hz,
scale is linear). 80–90% of people have prominent sinusoidal-like waves
with frequencies in 8–12 Hz range – alpha rhythm. Others (like this)
lack this type of activity.
The sample of human EEG with prominent resting state activity –
alpha-rhythm. Left: EEG traces (horizontal – time in seconds; vertical –
amplitudes, scale 100 μV). Right: power spectra of shown signals
(vertical lines – 10 and 20 Hz, scale is linear). Alpha-rhythm consists
of sinusoidal-like waves with frequencies in 8–12 Hz range (11 Hz in
this case) more prominent in posterior sites. Alpha range is red at
power spectrum graph.
The samples of main types of artifacts in human EEG. 1:
Electrooculographic artifact caused by the excitation of eyeball's
muscles (related to blinking, for example). Big-amplitude, slow,
positive wave prominent in frontal electrodes. 2: Electrode's artifact
caused by bad contact (and thus bigger impedance) between P3 electrode
and skin. 3: Swallowing artifact. 4: Common reference electrode's
artifact caused by bad contact between reference electrode and skin.
Huge wave similar in all channels.
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 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.[48]
Associated with inhibition of elicited responses (has been found to
spike in situations where a person is actively trying to repress a
response or action).[49]
Displays during cross-modal sensory processing (perception that combines two different senses, such as sound and sight)[51][52]
Also is shown during short-term memory matching of recognized objects, sounds, or tactile sensations
A decrease in gamma-band activity may be associated with
cognitive decline, especially when related to the theta band; however,
this has not been proven for use as a clinical diagnostic measurement
Mu suppression could indicate that motor mirror neurons are working. Deficits in Mu suppression, and thus in mirror neurons, might play a role in autism.[54]
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.
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).
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.[56]
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 is the frequency range from 7 Hz to 13 Hz.[57]Hans Berger
named the first rhythmic EEG activity he saw as 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).[58][59]
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.[60]
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
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.[1]
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
of the normal neuronal system and the mirror neuron system "go out of
sync" and interfere with each other.[54]
"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.
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 electroencephalography (EEG) varies by age. The
neonatal EEG is quite different from the adult EEG. 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 (see: http://www.stanford.edu/group/maciverlab/).
Artifacts
Biological artifacts
Main types of artifacts in human EEG
Electrical signals detected along the scalp by an EEG, but that originate from non-cerebral origin are called artifacts.
EEG data is almost always contaminated by such artifacts. The amplitude
of artifacts can be quite large relative to the size of amplitude of
the cortical signals of interest. This is one of the reasons why it
takes considerable experience to correctly interpret EEGs clinically.
Some of the most common types of biological artifacts include:
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.[61][62]
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.[63] Eye movements called saccades also generate transient electromyographic potentials, known as saccadic spike potentials (SPs).[64] The spectrum of these SPs overlaps the gamma-band (see Gamma wave), and seriously confounds analysis of induced gamma-band responses,[65] requiring tailored artifact correction approaches.[64] 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).
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).[66]
Some of these artifacts can be useful in various applications. The EOG signals, for instance, can be used to detect[64] 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.
ECG
artifacts are quite common and can be mistaken for spike activity.
Because of this, modern EEG acquisition commonly includes a one-channel ECG from the extremities. This also allows the EEG to identify cardiac arrhythmias that are an important differential diagnosis to syncope or other episodic/attack disorders.
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.
Environmental artifacts
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.
Artifact correction
Recently, independent component analysis (ICA) techniques have been used to correct or remove EEG contaminants.[64][67][68][69][70][71]
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.[72]
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.[73] However, Surface Laplacian
has been shown to be effective in eliminating muscle artefact,
particularly for central electrodes, which are further from the
strongest contaminants.[74]
The combination of Surface Laplacian with automated techniques for
removing muscle components using ICA proved particularly effective in a
follow up study.[75]
Abnormal activity
Abnormal activity can broadly be separated into epileptiform and non-epileptiform activity. It can also be separated into focal or diffuse.
Focal epileptiform discharges represent fast, synchronous
potentials in a large number of neurons in a somewhat discrete area of
the brain. These can occur as interictal activity, between seizures, and
represent an area of cortical irritability that may be predisposed to
producing epileptic seizures. Interictal discharges are not wholly
reliable for determining whether a patient has epilepsy nor where
his/her seizure might originate.
Generalized epileptiform discharges often have an anterior
maximum, but these are seen synchronously throughout the entire brain.
They are strongly suggestive of a generalized epilepsy.
Focal non-epileptiform abnormal activity may occur over areas of the brain where there is focal damage of the cortex or white matter.
It often consists of an increase in slow frequency rhythms and/or a
loss of normal higher frequency rhythms. It may also appear as focal or
unilateral decrease in amplitude of the EEG signal.
Diffuse non-epileptiform abnormal activity may manifest as
diffuse abnormally slow rhythms or bilateral slowing of normal rhythms,
such as the PBR.
Intracortical Encephalogram electrodes and sub-dural electrodes
can be used in tandem to discriminate and discretize artifact from
epileptiform and other severe neurological events.
More advanced measures of abnormal EEG signals have also recently
received attention as possible biomarkers for different disorders such
as Alzheimer's disease.[76]
Remote communication
The
United States Army Research Office budgeted $4 million in 2009 to
researchers at the University of California, Irvine to develop EEG
processing techniques to identify correlates of imagined speech
and intended direction to enable soldiers on the battlefield to
communicate via computer-mediated reconstruction of team members' EEG
signals, in the form of understandable signals such as words.[77]
Economics
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 $100
USD.
In 2004 OpenEEG released its ModularEEG as open source hardware. Compatible open source software includes a game for balancing a ball.
In 2007 NeuroSky
released the first affordable consumer based EEG along with the game
NeuroBoy. This was also the first large scale EEG device to use dry
sensor technology.[78]
In 2008 the Final Fantasy developer Square Enix announced that it was partnering with NeuroSky to create a game, Judecca.[79][80]
In 2009 Mattel partnered with NeuroSky to release the Mindflex, a game that used an EEG to steer a ball through an obstacle course. By far the best selling consumer based EEG to date.[79][81]
In 2009 Uncle Milton Industries partnered with NeuroSky to release the Star WarsForce Trainer, a game designed to create the illusion of possessing the Force.[79][82]
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).[83]
In 2010, NeuroSky added a blink and electromyography function to the MindSet.[84]
In 2011, NeuroSky released the MindWave, an EEG device designed for educational purposes and games.[85][86] The MindWave won the Guinness Book of World Records award for "Heaviest machine moved using a brain control interface".[87]
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.[88]
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.[89]
Future research
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;[90] 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. 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.
Honda is attempting to develop a system to enable an operator to control its Asimo robot using EEG, a technology it eventually hopes to incorporate into its automobiles.[91]
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.[94]
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.[95]
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.[96]
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