ArticlePDF Available

Electroencephalogram Mapping of Brain States


Abstract and Figures

Neurons of the nervous system continuously generate output signals that can be gauged indirectly by quantitative electroencephalogram readouts. This recording procedure measures changes in depolarization pulses from cortical and thalamic circuits in response to streams of abstract information provided by sensory receptors. In this brief review, we discuss the electroencephalograph in terms of its use in detecting regional differences in brain electrical activity, and describe a wide range of clinical phenomena revealed by the application of quantitative electroencephalogram mapping.
Content may be subject to copyright.
Copyright © 2014 by American Scientific Publishers
All rights reserved.
Printed in the United States of America
Journal of Neuroscience
and Neuroengineering
Vol. 3, pp. 1–5, 2014
Electroencephalogram Mapping of Brain States
German Torres, Michael P. Cinelli, Alexander T. Hynes, Ian S. Kaplan, and Joerg R. Leheste
Department of Biomedical Sciences, New York Institute of Technology,
College of Osteopathic Medicine Old Westbury, New York, 11568 USA
Neurons of the nervous system continuously generate output signals that can be gauged indirectly by quan-
titative electroencephalogram readouts. This recording procedure measures changes in depolarization pulses
from cortical and thalamic circuits in response to streams of abstract information provided by sensory receptors.
In this brief review, we discuss the electroencephalograph in terms of its use in detecting regional differences
in brain electrical activity, and describe a wide range of clinical phenomena revealed by the application of
quantitative electroencephalogram mapping.
KEYWORDS: Action Potentials, Anesthetics, Brain Pathologies, Neuronal Cell Types, Sleep-Wake Cycles.
Action potentials are the information-processing elements
of the nervous system. Although the sequence of events
underlying action potentials is relatively understood at
the microscopic level, its functional significance at the
macroscopic level is far from clear. Nevertheless, action
potentials are usually viewed as two regenerative elec-
trochemical cycles (i.e., depolarization and hyperpolariza-
tion cycles) conveying graded information to the neuron,
synapse, neural circuit and eventually to the entire brain.
Action potentials can be gauged indirectly at the macro-
scopic level through the use of brain imaging and
quantitative electroencephalogram (EEG) readouts. For
example, EEGs measure summations of excitatory (i.e.,
depolarizing) and inhibitory (i.e., hyperpolarizing) postsy-
naptic action potentials across cortical cells, more specif-
ically, pyramidal neurons of the external (Layer III) and
internal (Layer V) layers of the cerebral cortex [1]. As
pyramidal neurons represent the primary output for the
cerebral cortex to the ipsilateral or contralateral hemi-
sphere [2], EEG readouts are thought to translate local
streams of abstract information to subcortical targets in the
forebrain, brainstem or spinal cord. Because of its non-
invasive nature, and adequate temporal and spatial resolu-
tions, the EEG has become an excellent experimental tool
for deciphering global patterns of neural activity regulating
brain states.
Author to whom correspondence should be addressed.
Received: xx Xxxx xxxx
Accepted: xx Xxxx xxxx
The EEG, as first described by the German psychiatrist
Hans Berger, measures voltage or electrical potential (V )
differences between two electrodes placed on the scalp
of a human subject [3]. The mechanism behind the EEG
is based upon the theory of volume conduction which
describes the passage of ion currents from a biological sys-
tem (i.e., the brain) to a source of measurement. It should
be noted that summation of ion currents from thousands of
pyramidal neurons represents the primary source of V as
cortical interneurons (e.g., -amino-butyric acid, GABA)
or glial cells (e.g., astrocytes) provide little or no con-
tribution to voltage differences. The synaptic activity of
all pyramidal neurons having the same approximate ver-
tical orientation to the scalp is eventually relayed to the
EEG machine as a synchronous signal amplified approx-
imately 10,000×(Fig. 1). Current EEG systems measure
V differences between 4 to 256 electrodes, with most
laboratories using a 21-electrode tracing system [4]. Elec-
trodes normally detect synchronous signals ranging from
20–150 V elapsed over intervals of 0.5 to 60 Hz (cycles
per second). Electrode placement on the human scalp is
now a rigorously standardized procedure aimed to provide
reliability across studies and to develop valid hypotheses
with internal and external validity (Fig. 2). In general, the
EEG allows scientists to gauge the overall conscious state
of a patient, as each state of consciousness (e.g., asleep,
wake, anesthetized) is correlated with particular elec-
troencephalographic wave patterns. However, electroen-
cephalographic recordings do not reveal brain structures,
nor can they indicate the functioning of specific brain
J. Neurosci. Neuroeng. 2014, Vol. 3, No. 2 2168-2011/2014/3/001/005 doi:10.1166/jnsne.2014.1098 1
Electroencephalogram Mapping of Brain States Torres et al.
Fig. 1. A schematic diagram depicting an electroencephalographic
design for gauging neural activity during a state of consciousness. Elec-
trodes made of a silver/silver chloride moiety are placed on the scalp
of a human subject. Action potentials generated by pyramidal neurons
residing within cortical structures (e.g., F3, left frontal cortex; T4, right
temporal lobe) are amplified (2×) and filtered (2×) against potential
EEG artifacts (e.g., EMG, EOG, rhythmic patterns of respiratory inspi-
ration/expiration indices). The use of additional electrodes significantly
allows for the increased resolution of synchronous signals from the brain
parenchyma. A reference (A1/A2; pink) and ground (Fpz; yellow) elec-
trodes are anatomically placed on each ear to provide baseline features of
physiological activity. Eventually, the synchronous signal is transformed
to a digital index via an analog-to-digital converter. The final electroen-
cephalographic pattern depicts differences in V over time (cycles per
second; ms, milliseconds). A1, left earlobe; A2, right earlobe.
A neural activity commonly studied by the EEG is sleep-
wake cycles. From this particular technique, we know
that sleep cycles between two states, rapid-eye-movement
(REM) and non-REM sleep that occur at regular 90-minute
intervals. Non-REM sleep has three distinct EEG stages,
whereas REM sleep is characterized by skeletal-muscle
hypotonia [5]. It should be noted that the brain is active
during both sleep states with different bouts of neu-
ronal activity traced to each sleep state. For example,
before a person goes to sleep (i.e., eyes closed and qui-
etly resting), the brain shows prominent alpha waves
(10 Hz), whereas during non-REM sleep, the brain
exhibits EEG tracings of delta waves (1 to 4 Hz). Thus
brain activity during a relaxed, awake state is quite dif-
ferent from that of the non-REM phase of sleep where
the EEG shows high-voltage, slow wave epochs [6]. The
fact that switches of brain activity corresponds to changes
in behavioral states indicates that sleep-awake transitions
are regulated by different populations of neurons that
promote behavioral arousal or sleep [7]. For example,
acetylcholine-, norepinephrine-, serotonin-, histamine- and
dopamine-containing neurons from the ascending reticular
activating system and hypothalamus promote wakefulness,
whereas GABA- and galanin-containing neurons from the
ventro-lateral preoptic nucleus of the hypothalamus pro-
mote sleep [8, 9]. In addition, orexin (hypocretin) neurons
housed in the lateral hypothalamus maintain wakefulness
as degeneration of these neurons leads to narcolepsy with
cataplexy [10].
Based upon EEG tracings, a desynchronized state (low
voltage, fast EEG) is associated with behavioral arousal,
whereas a synchronized state (high voltage, slow EEG)
is linked to sleep. This pattern of brain activity is com-
plemented by measuring muscle tone activity through the
use of electromyography (EMG) recordings. During non-
REM sleep, skeletal muscle is reduced, whereas during
REM sleep, there is complete loss of skeletal-muscle tone
despite the EEG showing a desynchronized pattern of
neural activity that is remarkable similar to the wake
state [6, 11]. Another commonly used technique to map
sleep bouts is the electro-oculogram (EOG) which records
eye position and movement. When the brain enters a REM
state of sleep, the EOG shows bilateral eye movements
which dominate this particular state of sleep, further allow-
ing a clear-cut distinction from the wake state (Fig. 3).
Other encephalographic patterns observed during sleep
cycles, include sleep spindles (or sigma thalamic-derived
wavebands), K complexes and theta waves (4–7 Hz)
which are seen during non-REM sleep. Despite consid-
erable knowledge about the mechanisms underlying the
aforementioned brain wave patterns, their functional sig-
nificance to sleep behavior remains poorly understood.
The EEG is also used as a diagnostic tool for predicting
signs of brain pathology [12]. For instance, EEG technol-
ogy has been used for detecting abnormal neural activ-
ity in prion-based diseases such as Creutzfeldt-Jakob and
in patients with debilitating deficiencies in motor func-
tion such as those seen in hepatic encephalopathy [13].
EEG readouts from these patients often show tri-phasic
wave activity which is thought to reflect abnormalities
in cortical-thalamic connections [14]. It should be noted
that tri-phasic waves are also detected in peripheral condi-
tions, including individuals with uremia, hyponatremia and
autoimmune thyroiditis [15]. In general, tri-phasic waves
are of high-amplitude (70 V) with three negative-
positive-negative phases that repeat periodically at a lower
amplitude and at the rate of 1–2 Hz. Thus, neural activ-
ity patterns in the form of tri-phasic waves appear to occur
as abnormalities in electrical circuits with different clinical
end points.
Predicting awareness outcome in patients who survive
acute brain damage but have slipped into a comatose state
is an additional strength of EEG technology. For instance,
EEGs can confirm clinical diagnoses of brain death which
are defined as neural activity below the 2 V range. How-
ever, for patients in a permanent vegetative state, EEG
readouts cannot confirm diagnosis nor predict chances of
recovery because conscious awareness is absent from pur-
poseful motor behavior [16]. In this regard, EEGs are
2J. Neurosci. Neuroeng., 3, 1–5, 2014
Torres et al. Electroencephalogram Mapping of Brain States
Fig. 2. A semi-schematic diagram depicting dorsal and sagittal views of the human skull and brain framed with a 21-electrode tracing system.
Standardized electrode placement distribution is based on the International 10–20 EEG system as described by Khazi et al. [30]. Electrodes on the
right side of the brain are denoted with even numbered electrodes, whereas electrodes on the left size of the brain are denoted with odd numbered
electrodes. Nomenclature of brain structures is shown on adjacent table. The denotation z” is for zero or midline electrode placement. In addition to
the EEG, optical coherence tomography and functional near-infrared spectroscopy (specific spectra: 700–1000 nm) are also used to gauge brain activity
in terms of oxygenation and blood volume at micrometer (m) resolutions [31]. Diagram adapted and modified from 3D4 Medical [32].
more powerful gauges of consciousness than magnetic res-
onance imaging (MRI) or computed tomographic (CT)
scanning as these imaging techniques cannot detects signs
of conscious awareness; they only visualized structural
damage to the brain parenchyma. In general, EEGs from
comatose patients frequently resemble the high-amplitude,
low-frequency activity (13–25 Hz; paradoxical excita-
tion) seen during propofol-induced anesthesia [5].
The EEG is also isoelectric (a completely flat EEG read-
out) during the most profound state of general anesthesia
(i.e., Phase 4). During Phase 1 of general anesthesia, the
EEG readout shows a significant decrease of beta waves
(13–30 Hz) and increases of delta waves (1–4 Hz).
This light state of general anesthesia is followed by Phase
2 which is characterized by increases in alpha and delta
waves, a phenomenon known as anteriorization [17, 18].
Of interest, the EEG in this particular Phase of gen-
eral anesthesia (the intermediate state) resembles that of
stage 3 of the non-REM sleep cycle. Phase 3 is a deeper
state of general anesthesia and the EEG is characterized
by isoelectric periods intermingled with periods of alpha
and beta waves, a phenomenon known as burst suppres-
sion [19]. Surgery is usually performed during Phases 2
and 3 [5]. From these sequences of brain EEG events, it is
clear that general anesthesia is a reversible state of drug-
induced coma.
Another brain disorder in which EEGs provide
information on certain aspects of pathogenesis and patho-
physiology is epilepsy. Although there are more than
a dozen clinical phenotypes of epilepsy (e.g., absence
seizures), they are generally classified as generalized
epilepsy (e.g., tonic-clonic epilepsy), partial epilepsy (usu-
ally limited to a specific brain region), frontal-lobe
epilepsy and temporal-lobe epilepsy [20, 21]. Regard-
less of pathogenesis, epilepsy is characterized by neurons
whose resting potential parameters are closely aligned to
thresholds of depolarization. Under these aberrant con-
ditions, the probability of action potentials significantly
increases causing overtly-excited glutamate neurons to fire
action potentials uncontrollably. Alternatively, a decline
in GABAAreceptor signaling could also trigger hyper-
active states of depolarization cycles. The end result of
unimpeded action potentials is an epileptic seizure with a
synchronized, high frequency “spike wave” pattern across
broad populations of cortical and thalamic neurons [22].
It should be noted that an isoelectric EEG is often induced
by the administration of a barbiturate (e.g., pentobarbital),
J. Neurosci. Neuroeng., 3, 1–5, 2014 3
Electroencephalogram Mapping of Brain States Torres et al.
Fig. 3. Representative EEG, EMG and EOG readouts during an awake
and sleep cycle. Beta waves (15–40 Hz) occur during a wake state,
whereas alpha waves (10 Hz) occur during a drowsy-like state. Stage 1
of the sleep cycle often shows theta waves in the range of 3–7 Hz,
whereas stage 2 exhibits brain waves in the range of 12–14 Hz. Stage 2
of the sleep cycle is characterized by the presence of sleep spindles and K
complexes. Delta waves in the range of 1–4 Hz predominates stages 3
and 4 of the sleep cycle. Note that during REM sleep, the EEG and EOG
readouts resemble the wave patterns of the wake state. The EMG trac-
ing is highest during waking and lowest during REM sleep. In general,
periods of non-REM and REM sleep are interwoven during a sleep bout.
propofol or etomidate to minimize cell damage during neu-
rosurgery or to stop generalized seizures [23].
The striking changes of ensemble neural activity
observed in epilepsy, unfortunately, are not seen in other
brain disorders such as Alzheimer’s disease (AD), Parkin-
son’s disease (PD) or clinical depression. In severely
demented patients, the EEG is dominated by slow wave
activity (e.g., delta wave, 1 to 4 Hz) over frontal and
temporal lobes [24]. It should be noted, however, that AD
appears to be caused by multiple pathological processes
and therefore electroencephalographic readouts may vary
in terms of etiologies and mechanisms underlying each
pathological process. This complexity also extends to clin-
ical depression where the definition of pathology, source
of pathology (e.g., genomes, epigenetic reprogramming
and/or environments) and mechanisms of disease are par-
ticularly challenging as well. Regardless, frontal and pari-
etal electroencephalographic band asymmetries (<50 V,
8–13 Hz) are considered biomarkers of vulnerability to
endogenous depression and generalized anxiety [25]. How-
ever, longitudinal studies are required to determine more
precisely whether band asymmetries can serve as abso-
lute biomarkers for affective disorders, particularly during
pre-clinical stages of the disease. This EEG information
could have enormous diagnostic and prognostic value for
selecting appropriate therapeutic interventions for the ail-
ing brain.
The EEG is also an important diagnostic instrument
for studying REM sleep parasomnias, in particularly REM
sleep behavior disorder (RBD). In this syndrome, somatic
muscle atonia, which is the defining feature of REM sleep,
is lost from the sleep cycle thus allowing the acting out
of dream mentation often with violent or injurious con-
sequences to the patient or the bed-partner [26]. Interest-
ingly, chronic forms of RBD are associated with PD and
dementia with Lewy body disease as well as in vascular
dementia and fronto-temporal lobar degeneration [27, 28].
At the EEG and EMG level, RBD is characterized by
alpha wave patterns (10 Hz), prominent muscle activity
and body movements, which are accompanied by REM as
measured by EOG readouts [25]. The association between
RBD and PD suggests that degeneration of particular auto-
nomic neurons that promote behavioral arousal or sleep
often precede the motor component of PD. Regardless,
idiopathic RBD may foreshadow neurodegenerative dis-
eases in newly diagnosed, unmedicated patients. EEG-
driven analysis of sleep may contribute to the evaluation
of neurodegenerative patients, and it may have potential
sensitivity as a clinical biomarker of the premotor phase
of PD [29].
In conclusion, quantitative EEG studies have revealed
that brain electrical activity is a regulated and dynamic
process that can be reorganized as a function of con-
sciousness, disease or anesthesia for surgery. These stud-
ies also confirm that the generation and propagation of
action potentials, synaptic transmission and the production
of graded sensory information is a hierarchical processing
theme used by the brain for its own functional purposes.
A major new challenge is to understand how brain electri-
cal activity can be used for objective methods of diagnosis
and prognosis.
Conflict of Interest
The authors report no conflict of interest. Some sections
from the Sleep-Wake Cycles section have been previ-
ously published in non-peered review form (Torres and
Horowitz, Kopf Carrier #81, 2014).
References and Notes
1. J. L. Sirven and W. O. Tatum, Does Electroencephalography Provide
a “Window to the Brain” for the Neurologically Ill? Mayo Clinic
Proceedings 88, 312 (2013).
2. D. E. Haines, Fundamental Neuroscience. Churchill Livingstone 2
3. H. Berger, Über das Elektrenkephalogramm des Menschen. Archiv
für Psychiatrie und Nervenkrankheiten 87, 527 (1929).
4. T. M. Lau, J. T. Gwin, and D. P. Ferris, How many electrodes
are really needed for EEG-based mobile brain imaging? Jour na l of
Behavioral and Brain Science 2, 387 (2012).
5. E. N. Brown, R. Lydic, and N. D. Schiff, General anesthesia, sleep
and coma. N. Engl. J. Med. 363, 2638 (2010).
6. J. A. Hobson, Sleep is of the brain, by the brain and for the brain.
Nature 437, 1254 (2012).
7. M. Steriade, D. A. McCormick, and T. J. Sejnowski, Thalamocorti-
cal oscillations in the sleeping and aroused brain. Science 262, 679
8. C. B. Saper, T. E. Scammell, and J. Lu, Hypothalamic regulation of
sleep and circadian rhythms. Nature 437, 1257 (2005).
9. C. B. Saper, The neurobiology of sleep. Continuum (Minneap Minn).
(1 Sleep Disorders). 19–31 (2013).
4J. Neurosci. Neuroeng., 3, 1–5, 2014
Torres et al. Electroencephalogram Mapping of Brain States
10. C. R. Burgess and T. E. Scammell, Narcolepsy: Neural mechanisms
of sleepiness and cataplexy. J. Neurosci. 32, 12305 (2012).
11. S. H. Lee and Y. Dan, Neuromodulation of brain states. Neuron
76, 209 (2012).
12. J. Snaedal, G. H. Johannesson, T. E. Gudmundsson, T. H. Paidak,
and K. Johnson, The use of EEG in Alzheimer’s disease, with
and without scopolamine–A pilot study. Clinical Neurophysiology
121, 836 (2010).
13. P. Amodio, F. Campagna, S. Olianas, P. Iannizzi, D. Mapelli,
M. Penzo, P. Angeli, and A. Gatta, Detection of minimal hepatic
encephalopathy: Normalization and optimization of the Psychomet-
ric Hepatic Encephalopathy Score. A neuropsychological and quan-
tified EEG study. J. Hepatology 49, 346 (2008).
14. R. Prakash, K. Mullen, and A. Norden, Hepatic Encephalopathy.
First Consult. (2011).
15. R. Sutter, R. Stevens, and P. W. Kaplan, Significance of triphasic
waves in patients with acute encephalopathy: A nine-year cohort
study. Clinical Neurophysiology 124, 935 (2013).
16. P. L. Purdon, E. T. Pierce, E. A. Mukamel, M. J. Prerau, J. L. Walsh,
K. F. Wong, A. F. Salazar-Gomez, P. G. Harrell, A. L. Sampson,
A. Cimenser, S. Ching, N. J. Kopell, C. Tavares-Stoeckel, K. Habeeb,
R. Merhar, and E. N. Brown, Electroencephalogram signatures of
loss and recovery of consciousness from propofol. Proc. Natl. Acad.
Sci. 110, E1142 (2013).
17. J. H. Tinker, F. W. Sharbrough, and J. D. Michenfelder, Anterior
shift of the dominant EEG rhythm during anesthesia in the Java
monkey: Correlation with anesthetic potency. Anesthesiology 46, 252
18. V. A. Feshchenko, R. A. Veselis, and R. A. Reinsel, Propofol-
induced alpha rhythm. Neuropsychobiology 50, 257 (2004).
19. D. L. Clark and B. S. Rosner, Neurophysiologic effect of gen-
eral anesthetics. I. The electroencephalogram and sensory evoked
responses in man. Anesthesiology 38, 564 (1973).
20. T. R. Browne and G. L. Holmes, Epilepsy. N. Engl. J. Med.
344, 1145 (2001).
21. D. N. Lenkov, A. B. Volnova, A. R. Pope, and V. Tsytsarev, Advan-
tages and limitations of brain imaging methods in the research of
absence epilepsy in humans and animal models. J. Neurosci. Meth-
ods 212, 195 (2013).
22. M.B.Westover,M.M.Sha,M.T.Bianchi,L.M.V.R.Moura,
D. O’Rourke, E. S. Rosenthal, C. J. Chu, S. Donovan, D. B. Hoch,
R. D. Kilbride, A. J. Cole, and S. S. Cash, The probability of seizures
during EEG monitoring in critically ill adults. Clin. Neurophys. Arti-
cle in Press (2014).
23. J. Claassen, L. J. Hirsch, R. G. Emerson, and S. A. Mayer, Treat-
ment of refractory status epilepticus with pentobarbital, propofol or
midazolam: A systemic review. Epilepsia 43, 146 (2002).
24. C. Micanovic and S. Pal, The diagnostic utility of EEG in early-
onset dementia: A systemic review of the literature with narrative
analysis. J. Neural Transm. 121, 59 (2014).
25. G. M. Grimshaw, J. J. Foster, and P. M. Corballis, Frontal and pari-
etal EEG asymmetries interact to predict attentional bias to threat.
Brain Cogn. 90C, 76 (2014).
26. M. W. Mahowald and C. H. Schenck, Insights from studying human
sleep disorders. Nature 437, 1279 (2005).
27. J. Peever, P. H. Luppi, and J. Montplaisir, Breakdown in REM sleep
circuitry underlies REM sleep behavior disorder. Trends Neurosci.
37, 279 (2014).
28. M. Pistacchi, M. Gioulis, F. Contin, F. Sanson, and S. Z. Marsala,
Sleep disturbance and cognitive-disorder: Epidemiological analysis
in a cohort of 263 patients. Neurol Sci. Epub ahead of Print (2014).
29. J. A. Palma and H. Kaufmann, Autonomic disorders predict-
ing Parkinson’s disease. Parkinsonism Relat Disord. Suppl: S94-8
30. M. Khazi, A. Kumar, and M. J. Vidya, Analysis of EEG using 10:20
Electrode System. IJIRSET 2, 185 (2012).
31. V. Tsytsarev, C. Bernardelli, and K. I. Maslov, Living Brain Optical
Imaging: Technology, Methods and Applications. Journal of Neuro-
science and Neuroengineering 1, 180 (2012).
32. Essential Anatomy. 3D4 Medical. San Diego, CA (2014).
J. Neurosci. Neuroeng., 3, 1–5, 2014 5
... During deep sleep ( Fig. 9 A, B, E) slow waves are predominant and high frequency activity is almost non-existing. For instance, in Fig. 9 B, one can easily observe predominant 7Hz brain activity usually related to stage 1 non-REM sleep in healthy subjects [74]Furthermore, in Fig 9 E, it can be observed that there is no relevant brain activity above 40 Hz and low frequency waves are predominant (below 15Hz, with peaks around 2Hz and 9Hz), which correspond to either sleep stage 3 (predominant Delta waves in the 1-4Hz range) or a drowsy-like non-awake state (predominant alpha waves in the 9,10Hz range) [74]. ...
... During deep sleep ( Fig. 9 A, B, E) slow waves are predominant and high frequency activity is almost non-existing. For instance, in Fig. 9 B, one can easily observe predominant 7Hz brain activity usually related to stage 1 non-REM sleep in healthy subjects [74]Furthermore, in Fig 9 E, it can be observed that there is no relevant brain activity above 40 Hz and low frequency waves are predominant (below 15Hz, with peaks around 2Hz and 9Hz), which correspond to either sleep stage 3 (predominant Delta waves in the 1-4Hz range) or a drowsy-like non-awake state (predominant alpha waves in the 9,10Hz range) [74]. ...
... In contrast, we can observe in Fig. 9 C a greater range of frequencies coexisting in the plot, as well as some artifacts related to eye-blinks (−2s and −3.5s), which indicate that the subject is awake [74]. The same happens in Fig. 9 F where an even distribution of amplitudes between all frequencies can be observed. ...
Full-text available
Electroencephalography (EEG) has a wide range of applications in medical diagnosis, and novel form of Human Machine Interfaces (HMI) for controlling prosthetic implants, wheelchairs, and home appliances in various forms of paralysis. However, the current EEG setups are composed of many wires hanging down from the system, and individual electrodes that must be set manually, which is time-consuming. As a result, the overall system is neither comfortable, nor aesthetically appealing. Here, we introduce for the first time, a comfortable textile-based EEG headband system that is soft, conformal to the skin, and comfortable. We present materials and methods for fabrication of multi-layer stretchable e-textile, that interfaces the human epidermis from one side through printed electrodes, and interfaces a rigid PCB island on the second layer. We as well demonstrate a method that allows creation of VIAs (vertical interconnect access) between the layers, using a CO2 laser. All Electrodes are integrated into the headband, and thus there is no need for individual electrode placement, and individual wiring. By screen printing a home-made conductive stretchable ink, patient-specific EEG headbands can be tailor made considering the optimal positioning of the electrodes for each patient. We show that these printed electrodes benefit from a very low skin-electrode impedance, comparable to gold standard Ag/AgCl, or gold cup electrodes, thanks to the high surface area silver flakes used in this work. The e-textile headband interfaces with an EEG acquisition device that captures, amplifies, and transmits the data to an external mobile phone or a PC. Furthermore, the integrated amplification in the textile and the use of an EMF rejection layer on top of the electrodes were shown to reduce the unwanted EM noise that is picked up by the system. We as well show application of the developed headband for usage in Human Machine Interfaces and Sleep Data Acquisition. Altogether, this device is step toward wider use of EEG acquisition devices for daily-use applications.
... Besides high temporal resolution, EEG acquisition is relatively inexpensive and convenient in real-time applications [19]. EEG recordings have been widely used in diagnostic, clinical, and sleep-related research settings [20][21][22][23]. EEG signals may therefore be a means to identify and monitor alcoholic patients. ...
Following the research question and the relevant dataset, feature extraction is the most important component of machine learning and data science pipelines. The wavelet scattering transform (WST) is a recently developed knowledge-based feature extraction technique and is structurally like a convolutional neural network (CNN). It preserves information in high-frequency, is insensitive to signal deformations, and generates low variance features of real-valued signals generally required in classification tasks. With data from a publicly-available UCI database, we investigated the ability of WST-based features extracted from multichannel electroencephalogram (EEG) signals to discriminate 1.0-s EEG records of 20 male subjects with alcoholism and 20 male healthy subjects. Using record-wise 10-fold cross-validation, we found that WST-based features, inputted to a support vector machine (SVM) classifier, were able to correctly classify all alcoholic and normal EEG records. Similar performances were achieved with 1D CNN. In contrast, the highest independent-subject-wise mean 10-fold cross-validation performance was achieved with WST-based features fed to a linear discriminant (LDA) classifier. The results achieved with two 10-fold cross-validation approaches suggest that the WST together with a conventional classifier is an alternative to CNN for classification of alcoholic and normal EEGs. WST-based features from occipital and parietal regions were the most informative at discriminating between alcoholic and normal EEG records.
... These two channels are not only accessible but are also related to aggression 46 . In our study, EEG was recorded through two electrodes on Fp2 (right side of prefrontal cortex) and F8 (right side of dorsolateral prefrontal cortex), with ground and reference electrodes on the neck, in accordance with the BIOPAC EEG electrode placement guidelines and the International 10-20 EEG system [145][146][147] . For EOG (Electrooculogram), an active electrode was placed above the left eye, with a ground electrode on the mid-forehead and a reference electrode on the left cheek bone for removal of artefacts from eye-movement or gross muscle movement. ...
Full-text available
Among the genetic variations in the monoamine oxidase A (MAOA) gene, upstream variable number tandem repeats (uVNTRs) of the promoter have been associated with individual differences in human physiology and aggressive behaviour. However, the evidence for a molecular or neural link between MAOA uVNTRs and aggression remains ambiguous. Additionally, the use of inconsistent promoter constructs in previous studies has added to the confusion. Therefore, it is necessary to demonstrate the genetic function of MAOA uVNTR and its effects on multiple aspects of aggression. Here, we identified three MAOA alleles in Koreans: the predominant 3.5R and 4.5R alleles, as well as the rare 2.5R allele. There was a minor difference in transcriptional efficiency between the 3.5R and 4.5R alleles, with the greatest value for the 2.5R allele, in contrast to existing research. Psychological indices of aggression did not differ among MAOA genotypes. However, our electroencephalogram and electrocardiogram results obtained under aggression-related stimulation revealed oscillatory changes as novel phenotypes that vary with the MAOA genotype. In particular, we observed prominent changes in frontal γ power and heart rate in 4.5R carriers of men. Our findings provide genetic insights into MAOA function and offer a neurobiological basis for various socio-emotional mechanisms in healthy individuals.
... Neurobiological signals were measured via four channels using MP36 (BIOPAC), which is useful for quick and simple measurements in large populations (sampling rate: 1.000 kHz, bandpass filtering rate: 0.5~100 Hz). EEG signals were measured via two channels according to the BIOPAC EEG electrode placement guidelines and International 10~20 EEG system, and the EOG and ECG signals (Einthoven' s leads) were simultaneously measured [66]. Theories of frontal α asymmetry in aggression suggest that relatively greater resting neural activity in the left frontal cortex correlates with approach motivation, while greater resting neural activity in the right frontal cortex correlates with avoidance motivation [43,45]. ...
... Additionally, electrooculography (EOG) and ECG (Einthoven's leads) were simultaneously performed. Electrodes were placed in accordance with the BIOPAC EEG electrode placement guidelines and the International 10-20 EEG system [39,40]. Noises from blinking or eye movements were removed using independent component analysis. ...
Aggression is a complex, ubiquitous phenomenon that impacts behavioral traits and psychological health. Assessing aggression is challenging because aggression constitutes multiple subtraits, such as anger, reactive aggression, and overt aggression. Conventional methods of assessing aggression are susceptible to bias because they mainly rely upon self-reports. Thus, more objective methods that provide a multifaceted understanding of aggression in individuals are required. Here, we propose a supportive method of assessing specific aggression subtraits in Koreans using electroencephalography (EEG) and electrocardiography (ECG). Our evaluations and statistical analyses revealed that EEG and ECG signals in subjects responding to video cues that induced aggression are associated with aggression subtraits. In particular, we identified spectral differences in EEG signals in response to stimuli with situation-dependent aggression. The α and β signals of the Fp2 site (the right ventromedial prefrontal region) are highly associated with anger, reactive aggression, and overt aggression. Moreover, ECG signals are associated with anger and overt aggression. These results link neurobiological findings to psychological explanations of aggression and multiple aspects of human behavior. Our findings can potentially be applied to supportive assessment methods for psychological counseling or psychiatric diagnoses.
Full-text available
Public littering has become an ongoing problem, especially in Thailand. Littering is caused by the human behavior of disposing of waste improperly. A key influence on littering is a sense of responsibility depending on internal feelings, beliefs, and morality to their societal experiences, which are generated by biological processes, primarily within their brain functions. The research rationale aimed to study the empathetic process of human beings related to mirror neuron morality (MNM). Interestingly, if this research knows how to develop moral awareness and readiness of waste management, this study revealed that a short video clip was one activator for the MNM. The evidence in this study confirmed that the MNM could be activated by watching the one minute of video affective and somatic empathy with silent speech and human movement, which can effectively activate the MNM in terms of cognitive empathy and readiness to act. Furthermore, in this study, habitual activities such as brain exercise were practiced in waste segregation at home could induce the mirror neurons activation of the self- engagement index, which is consisted of selfless behavior or altruism prompted by many vicarious learning cycles and want to act as self-moral readiness. However, to create an effective moral development, further dimensions of the real action of waste segregation are required, such as public engagement by providing the different levels of public responses, environmental actions are taken, and environmental policies after watching the valuable information.
Principles of quantitative electroencephalography (EEG) relevant to neurotherapy are reviewed. A brief history of EEG, the general properties of human EEG, and the issues and obstacles associated with quantitative methods are discussed. Fourier analysis is also described.
Full-text available
Within the last few decades, optical imaging methods have yielded revolutionary results when applied to all parts of the central nervous system. The purpose of this review is to analyze research possibilities and limitations of several novel imaging techniques and show some of the most interesting achievements obtained by these methods. Here we covered intrinsic optical imaging, voltage-sensitive dye, photoacoustic, optical coherence tomography, near-infrared spectroscopy and some other techniques. All of them are mainly applicable for experimental neuroscience but some of them also suitable for the clinical studies.
Full-text available
During rapid eye movement (REM) sleep, skeletal muscles are almost paralyzed. However, in REM sleep behavior disorder (RBD), which is a rare neurological condition, muscle atonia is lost, leaving afflicted individuals free to enact their dreams. Although this may sound innocuous, it is not, given that patients with RBD often injure themselves or their bed-partner. A major concern in RBD is that it precedes, in 80% of cases, development of synucleinopathies, such as Parkinson's disease (PD). This link suggests that neurodegenerative processes initially target the circuits controlling REM sleep. Clinical and basic neuroscience evidence indicates that RBD results from breakdown of the network underlying REM sleep atonia. This finding is important because it opens new avenues for treating RBD and understanding its link to neurodegenerative disorders.
Full-text available
A noninvasive method for imaging the human brain during mobile activities could have far reaching benefits for studies of human motor control, for research and treatment of neurological disabilities, and for brain-controlled powered prosthetic limbs or orthoses. Several recent studies have demonstrated that electroencephalography (EEG) can be used to image the brain during locomotion provided that signal processing techniques, such as independent Component Analysis (ICA), are used to parse electrocortical activity from artifact contaminated EEG. However, these studies used high-density 256-channel EEG sensor arrays, which are likely too time-consuming to setup in a clinical or field setting. Therefore, it is important to evaluate how reducing the number of EEG channel signals affects the electrocortical source signals that can be parsed from EEG recorded during standing and walking while concurrently performing a visual oddball discrimination task. Specifically, we computed temporal and spatial correlations between electrocortical sources parsed from high-density EEG and electrocortical sources parsed from reduced-channel subsets of the original high-density EEG. For this task, our results indicate that on average an EEG montage with as few as 35 channels may be sufficient to record the two most dominate electrocortical sources (temporal and spatial R2 > 0.9). Correlations for additional electrocortical sources decreased linearly such that the least dominant sources extracted from the 35 channel dataset had temporal and spatial correlations of approximately 0.7. This suggests that for certain applications the number of EEG sensors used for mobile brain imaging could be vastly reduced, but researchers and clinicians must consider the expected distribution of relevant electrocortical sources when determining the number of EEG sensors necessary for a particular application.
Full-text available
Early-onset dementia (EOD) is characterized by functionally impairing deterioration in memory, language, personality or visuospatial skills emerging under the age of 65. Cerebral functioning can be assessed by visual electroencephalography (EEG) interpretation. The aim of this systematic review is to evaluate the diagnostic utility of visual EEG in EOD focusing on Alzheimer's disease (AD), vascular dementia (VAD), dementia with Lewy bodies (DLB), and frontotemporal dementia (FTD). Medline, Embase, Scopus, Web of Knowledge, and Google Scholar were systematically searched for studies where EEGs were included in the diagnostic evaluation of patients with dementia under the age of 65. Each paper was quality assessed and the results grouped according to dementia cause with a narrative summary. 4,157 papers were screened, 12 studies met the eligibility criteria with a total of 965 patients. An abnormal EEG was common to all causes of EOD. EEG abnormalities are more severe in early-onset AD patients. EEG severity grade is independent of disease duration. Slow wave activity is common to all dementias, but is most prominent in DLB. Frontal intermittent rhythmic delta activity could be considered as supportive for the diagnosis of DLB as can a Grand Total EEG score of over 9.5. EEG is usually normal in FTD. Focal changes can be seen in advanced VAD. Studies employed small patient groups, varying diagnostic criteria, and only a minority of patient diagnoses was pathologically confirmed. EEG may be useful as an adjunct in the diagnosis of DLB and AD. Further prospective well-powered studies are required to investigate diagnostic utility more robustly.
Sleep is characterized by synchronized events in billions of synaptically coupled neurons in thalamocortical systems. The activation of a series of neuromodulatory transmitter systems during awakening blocks low-frequency oscillations, induces fast rhythms, and allows the brain to recover full responsiveness. Analysis of cortical and thalamic networks at many levels, from molecules to single neurons to large neuronal assemblies, with a variety of techniques, ranging from intracellular recordings in vivo and in vitro to computer simulations, is beginning to yield insights into the mechanisms of the generation, modulation, and function of brain oscillations
Purpose of review: The basic circuitries that regulate wake-sleep cycles are described, along with how these are affected by different disease states and how those alterations lead to the clinical manifestations of those disorders. Recent findings: The discovery of both sleep-promoting neurons in the ventrolateral preoptic nucleus and wake-promoting neurons, such as the lateral hypothalamic orexin (also called hypocretin) neurons, has allowed us to recognize that these two populations of neurons are mutually antagonistic (ie, inhibit each other) and form a "flip-flop switch," a type of circuit that results in rapid and complete transition in behavioral state. The same principle applies to the circuitry controlling transitions between REM sleep and non-REM (NREM) sleep. Summary: The flip-flop switch circuitry of the wake-sleep regulatory system produces the typical sleep pattern seen in healthy adults, with consolidated waking during the day and alternation between NREM and REM sleep at night. Breakdown in this circuitry both results in and explains the manifestations of a variety of sleep disorders including insomnia, narcolepsy with cataplexy, and REM sleep behavior disorder.
The aim of this study was to investigate and describe frequency and characteristics of sleep disorders in a large cohort of community dwelling persons with several degrees and typologies of cognitive disorders. 236 patients (78 men and 158 women) were enrolled with different subtypes of dementia: Alzheimer's disease (AD), vascular dementia (VaD), mixed dementia, mild cognitive impairment (MCI), dementia with Lewy bodies (DLB), parkinson's disease dementia (PDD), and frontotemporal lobar degeneration (FTLD), respectively. The sleep disturbances evaluated were: insomnia, excessive daytime sleepiness (EDS), REM behavior disorder (RBD), restless legs syndrome (RLS), and nightmares. Every type of sleep disorder was present in each type of dementia but with significant differences. Insomnia is found to be more present and specific for AD; EDS was associated with the presence of dementia in the elderly with LBD or PDD; RLS and nightmares that were recognized mainly in FTD, LBD, and PDD patients scores; patients with MCI had a frequency of sleep disturbances of any type equal to that of patients with AD presenting mostly insomnia, nightmares or RLS more frequently; nightmares were more frequent among LBD and PDD patients. Frequency of RDB was more frequent in FTD, AD, and VaD. Our findings demonstrate that sleep disturbance was related to dementia. A careful clinical evaluation of sleep disorders should be performed routinely in the clinical setting of persons with cognitive decline.
Objective To characterize the risk for seizures over time in relation to EEG findings in hospitalized adults undergoing continuous EEG monitoring (cEEG). Methods Retrospective analysis of cEEG data and medical records from 625 consecutive adult inpatients monitored at a tertiary medical center. Using survival analysis methods, we estimated the time-dependent probability that a seizure will occur within the next 72-hours, if no seizure has occurred yet, as a function of EEG abnormalities detected so far. Results Seizures occurred in 27% (168/625). The first seizure occurred early (<30 minutes of monitoring) in 58% (98/168). In 527 patients without early seizures, 159 (30%) had early epileptiform abnormalities, versus 368 (70%) without. Seizures were eventually detected in 25% of patients with early epileptiform discharges, versus 8% without early discharges. The 72-hour risk of seizures declined below 5% if no epileptiform abnormalities were present in the first two hours, whereas 16 hours of monitoring were required when epileptiform discharges were present. 20% (74/388) of patients without early epileptiform abnormalities later developed them; 23% (17/74) of these ultimately had seizures. Only 4% (12/294) experienced a seizure without preceding epileptiform abnormalities. Conclusions Seizure risk in acute neurological illness decays rapidly, at a rate dependent on abnormalities detected early during monitoring. This study demonstrates that substantial risk stratification is possible based on early EEG abnormalities. Significance These findings have implications for patient-specific determination of the required duration of cEEG monitoring in hospitalized patients.
It is now well recognized that there is a premotor phase of Parkinson's disease (PD) with hyposmia and REM sleep behavior disorder caused by degeneration of specific CNS neurons. Most patients with PD describe autonomic symptoms at the time of diagnosis suggesting that these features may have potential sensitivity as clinical biomarkers of the premotor phase. The recognition that damage to peripheral autonomic neurons is present in the early stages of PD has led to a search for specific abnormalities in autonomic function that could serve as predictive biomarkers. There is evidence that constipation, urinary and sexual dysfunction and more recently decreased cardiac chronotropic response during exercise, are part of the premotor parkinsonian phenotype. The sensitivity and specificity of these features has yet to be accurately assessed. We briefly review the evidence for autonomic dysfunction as biomarker of premotor PD.