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Fingelkurts, A.A.;  Fingelkurts, A.A. Quantitative Electroencephalogram. Encyclopedia. Available online: https://encyclopedia.pub/entry/31251 (accessed on 05 December 2023).
Fingelkurts AA,  Fingelkurts AA. Quantitative Electroencephalogram. Encyclopedia. Available at: https://encyclopedia.pub/entry/31251. Accessed December 05, 2023.
Fingelkurts, Alexander A., Andrew A. Fingelkurts. "Quantitative Electroencephalogram" Encyclopedia, https://encyclopedia.pub/entry/31251 (accessed December 05, 2023).
Fingelkurts, A.A., & Fingelkurts, A.A.(2022, October 25). Quantitative Electroencephalogram. In Encyclopedia. https://encyclopedia.pub/entry/31251
Fingelkurts, Alexander A. and Andrew A. Fingelkurts. "Quantitative Electroencephalogram." Encyclopedia. Web. 25 October, 2022.
Quantitative Electroencephalogram
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Over many decades, clinical, systemic, and cognitive neuroscience have produced a large and diverse body of evidence for the potential utility of brain activity (measured by electroencephalogram—EEG) for neurology and psychiatry. These data are enormous and essential information often gets buried, leaving many researchers stuck with outdated paradigms.

quantitative electroencephalogram (qEEG) neurometrics neurophysiology neuropsychopathology

1. Introduction

MEG, fMRI, and PET are expensive, non-portable, partially invasive, and are usually associated with high stress due to noise, space confinement, and the need to be motionless. Additionally, fMRI and PET scans provide indirect measures of brain activity, with poor temporal resolution. Further, many types of mental activities, brain disorders, and malfunctions of the brain cannot be registered using fMRI since its effect on the level of oxygenated blood is low [1]. In contrast to these neuroimaging techniques, EEG is, at the same time, the cheapest, fastest, and most portable technique that measures neuronal activity directly and non-invasively. EEG does not elicit feelings of claustrophobia, does not require overt cooperative behavior from the person, has a temporal resolution adequate to mental and cognitive processes, and may distinguish between different temporal scales of information processing inherent to mental and cognitive processes.
EEG is a summation of electric voltage fields produced by dendritic and postsynaptic currents of many cortical neurons firing in non-random partial synchrony [2][3][4]. The aggregate of these electric voltage fields can be detected by electrodes on the scalp. The brainstem and thalamus serve as subcortical generators to synchronize populations of neocortical neurons in both normal and abnormal conditions, thus influencing the EEG. It seems that the activity of subcortical structures can be ‘visible’ in the EEG either indirectly through their effects on cortical activity or—in contrast to popular belief—more directly via deep sources. See explanations given in [5] (p. 7): “While local field potentials indeed fall off rapidly within the brain, far less attenuation is observed when recording across skull and scalp. The reason is that the lower conductivity of the skull (compared to the brain and scalp) attenuates superficial sources more strongly than deep ones, thus acting like a spatial low-pass filter. This property causes strong blurring and attenuation of the focal superficial fields but has less of an effect on the more diffuse (“low spatial frequency”) fields from deeper sources […] Recent evidence suggests that a considerably larger range of brain structures, layers, and cell types than previously thought can contribute to spontaneous EEG phenomena”.
A quantitative electroencephalogram (qEEG) is a mathematically and algorithmically processed digitally recorded EEG that extracts information invisible to ‘naked’ eye inspections of the signal. For the rest, qEEGs will be mostly refererd to as the majority of studies are performed using qEEGs.
After decades of studies, it is becoming clear that the qEEG is closely related to brain dynamics, with millisecond temporal resolution, functional properties, and global states of brain functioning, information processing, and cognitive activity [6][7][8][9][10][11][12][13]. The interaction of large populations of neurons gives rise to rhythmic electrical events in the brain, which can be observed at several temporal scales—qEEG oscillations. They are the basis of many different behavioral patterns and sensory mechanisms (for a review, see [14]). Indeed, a large body of evidence [15][16][17][18][19][20][21][22][23] has demonstrated that qEEG oscillations constitute a mechanism by which the brain can actively regulate changes in a state in selected neuronal networks to cause qualitative transitions between modes of information processing [16]. Thus, different qEEG oscillatory patterns are indicative of different information-processing states.
The qEEG has a number of important features that make it especially useful in clinical practice.

2. qEEG Historicism

An adult human qEEG is characterized by ‘historicism’—the information about primate phylogeny, pre- and post-natal maturation (individual development), and early life events (utero characteristics and early life stress).
Indeed, phylogenetically (phylogenesis—the evolutionary development and diversification of a species or group of organisms), the proportion of power of qEEG oscillations changes as a function of primate phylogeny [1][18][24]. Likewise, ontogenetically (ontogenesis—physical and psychological development of an individual organism from inception to maturity), qEEGs undergo significant transformation as a function of pre-natal (in utero) development (maternal stress exposure, anxiety, and depression during pregnancy are considered in utero adverse experiences and have been associated with future health problems [25][26][27][28]; this is so because intrauterine life events have a much greater impact on epigenetic profiles than stressful exposures during adult life due to heightened brain plasticity that is adversely affected by exposure to environmental insults [29]) [30] as well as a function of post-natal maturation (maturation refers to the timely appearance or unfolding of brain structures, events, and processes that are the result of the interaction between genes and the environment; brain maturation can be delayed, equal, or accelerated when compared to chronological age) [31][32][33][34]. It seems that ontogenetic differences mirror those of phylogenetic differences in the cause of brain development, where there is a gradual increase in qEEG complexity and change in the qEEG oscillations’ composition and proportions [13][35][36][37]. Why is this relevant? The qEEG has been found to have a high prognostic value for identifying the functional level of ‘brain maturity’ [38][39]. The knowledge of typical qEEG oscillatory patterns for a given phylogenesis/ontogenesis stage gives one the ability to assess the level of qEEG maturation or regression, which often accompanies the development of neuropsychopathology [40]. For example, a person with an immature qEEG is more easily swayed by external influences and has a lower threshold for aggressive and/or antisocial behaviors [41].
Additionally, early life stress (ELS) has been associated with abnormalities in the qEEG of adults and is also paralleled by a range of adverse outcomes in adults, such as personality dimensions, increased vulnerability to substance abuse, depression, anxiety, psychosis, and post-traumatic stress disorder (PTSD) [42][43][44]. Indeed, ELS such as protein energy malnutrition in the first year of life, extreme social and cognitive deprivation as a result of institutional care, physical or emotional neglect, and low socioeconomic status are all associated with abnormal qEEG characteristics on one hand and with developmental lag or deviation, persistent specific cognitive and behavioral deficits in adulthood, and accelerated cognitive decline [45][46][47][48][49][50] on another hand. Further, childhood traumas (including childhood sexual abuse) are also associated with adult qEEG deviations in parallel with cognitive dysfunction [51][52]. It seems that changes in catecholamine levels following a traumatic event can impede brain regional development, which, in turn, can compromise later cognitive functioning and emotional regulation and leave a person susceptible to stress later on in life.
Additionally, traumatic brain injury may also be reflected in qEEG deviations that correspond to complaints of cognitive symptoms that can persist anywhere from 11 [53], 22 [54], or even 27 [55] years post-injury, characterizing persistent post-concussive syndrome.
This briefly suggests that the qEEG contains information that is a historical consequence of individual development, ELS, and significant life events. However, in order to adequately assess qEEG variability associated with pathology, within-subject stability over EEG recordings within an EEG session, test–retest reliability over time, and intra-subject specificity (i.e., the extent to which a qEEG pattern is uniquely associated with a given person) and specificity for different conditions need to be established.

3. qEEG Stability, Reliability, and Specificity

Studies have reportedly demonstrated that the majority of qEEG characteristics have high (up to 90%) within-subject stability (internal consistency measured by Cronbach’s alpha) within an EEG recording session, high (up to 90%) reproducibility (test–retest reliability) over a period of hours, weeks, months and years, and high (up to 99%) intra-subject specificity, meaning that qEEG can accurately identify subjects from a large group [56][57][58][59][60][61][62][63][64][65][66][67][68][69][70][71][72][73][74].
These results suggest that qEEG characteristics possess trait-like qualities (stability over time). In this context, intrinsic properties of brain activity measured by resting qEEG constitute a neural counterpart of personality traits (Section 4.2) and can be regarded as the statistical neuro-signature of a person. Such high stability, reliability, and specificity of qEEG characteristics suggest that genetic factors have a strong influence on qEEG variation.

4. qEEG Heritability

A large body of studies have suggested that qEEG characteristics and their variability are largely determined by genetics and, thus, are highly heritable (up to 90%) [75][76][77][78][79][80][81][82][83][84][85][86][87][88][89]. Additionally, it was demonstrated that the correlations for qEEG characteristics between family groups (each consisting of a biologically related father, mother, and two children) were greater than those obtained from the non-family groups (each consisting of biologically unrelated subjects) [90] (see also [85]).
Smit et al. [88] proposed several common genetic sources for EEG: (a) skull and scalp thickness may affect the conductive properties of the tissues surrounding the cortex, (b) genetic influence on cerebral rhythm generators such as the central ‘pacemaker’ in the septum for hippocampal activity or the thalamocortical and corticocortical generators of cortical rhythmicity, (c) genes directly involved in the bioelectric basis of the EEG signal itself: for example, genes influencing the number of pyramidal cells, the number of dendritic connections, or their orientation with respect to the scalp may directly influence the mass dendritic tree depolarization of pyramidal cells in the cortex that underlies the EEG. Begleiter and Porjesz [91] added another factor: regulatory genes that control the neurochemical processes of the brain and, therefore, influence neural function.
Besides high qEEG heritability, genetic loci underlying the functional organization of human neuroelectric activity and their associated conditions/behavior have also been identified. Below is a short overview of qEEG oscillations, the related genes, and the associated pathological conditions:
(a)
qEEG beta oscillations (beta rhythm is electromagnetic oscillations in the frequency range of brain activity above 13 Hz)
Winterer et al. [92] reported that three exonic variants of the gene encoding the human gamma-amino butyric acid (GABA)B receptor on chromosome 6 modify the cortical synchronization measured as scalp-recorded qEEG coherence. Another genetic study indicated the importance of GABAA receptor genes in the modulation of qEEG beta oscillations in the human brain: Porjesz et al. [93] found a significant genetic linkage between the beta frequency of the human qEEG and a cluster of GABAA receptor genes on chromosome 4p. Additionally, this same GABAA receptor gene was found to be associated with a DSM-IV diagnosis of alcohol dependence [94].
(b)
qEEG alpha oscillations (alpha rhythm is electromagnetic oscillations in the frequency range of 8–13 Hz, arising from the synchronous and coherent electrical activity of neurons in the human brain)
Low voltage qEEG alpha oscillations have also been reported to be linked to (a) the GABAergic system, as an association has been found between the exon 7 variant of the GABAB receptor gene and alpha voltage [95], (b) a serotonin receptor gene (HTR3B), associated with alcoholism and antisocial behavior [96], and (c) a corticotrophin-releasing binding hormone (CRH-BP) [97][98], associated with depression, anxiety, and alcoholism. Low voltage alpha in females has also been reported to be associated with a genetic variant that leads to low activity of the enzyme that metabolizes dopamine and norepinephrine, catechol-o-methyltransferase (COMT) [99]. Additionally, low voltage alpha has been associated with a subtype of alcohol dependence with anxiety disorders [100][101] and with the brain-derived neurotrophic factor (BDNF) Val66Met polymorphism in depression [102]. High voltage qEEG alpha oscillations are heritable in a simple autosomal dominance manner [75]. The alpha peak frequency (APF) has been associated with the COMT gene, with the Val/Val genotype being marked by a 1.4 Hz slower APF compared to the Met/Met group [103].
(c)
qEEG theta oscillations (theta rhythm is electromagnetic oscillations in the frequency range of brain activity between 4 and 7.5 Hz)
There is evidence [104] that single nucleotide polymorphisms located in brain-expressed long intergenic non-coding RNAs (lincRNAs) on chromosome 18q23 are associated with posterior interhemispheric theta EEG coherence. These same variants are also associated with alcohol use behavior and posterior corpus callosum volume. Further, the Val158Met polymorphism of the COMT gene is associated with low-frequency oscillation abnormalities in schizophrenia patients [105].
This shortly suggests that there are common genetic links between qEEG oscillation characteristics and specific health conditions. It seems that genetically influenced features of the intrinsic oscillatory activity are related to the structures and functions of the corresponding neural generators and that different features of qEEGs may predict individual differences in brain function and structures.

5. qEEG and Structural Integrity of the Brain

Indeed, numerous studies have demonstrated that qEEGs reflect the brain’s structural characteristics (or ‘hardware’), such as the number of connections between neurons, white matter density, axonal diameter, degree of myelination and white matter integrity, as well as the integrity of corticocortical and thalamocortical circuits, hippocampal volume, the number of active synapses in thalamic nuclei, and the number of potential neural pathways [3][4][106][107][108][109][110][111][112][113][114][115][116][117][118][119][120][121][122]. For example, reduced EEG amplitude is believed to be partially due to a reduced number of synaptic generators and/or reduced integrity of the protein/lipid membranes of neurons [123][124].

6. qEEG and Functional Integrity of the Brain

Decades of studies have demonstrated that brain functional characteristics (or ‘software’), such as memory performance, attention and processing speed, emotional regulation, individual capacity for information processing, cognitive preparedness, and others, including functional states of the brain, are readily reflected in qEEGs at all ages in both healthy individuals and individuals with neurological or psychiatric conditions [10][17][18][20][111][125][126][127][128][129][130][131][132][133][134][135][136][137][138][139][140][141][142][143] (for a review, see [14]). This is so because qEEG oscillatory activity is generated by synchronous neural populations that mirror the firing rate of their constituent neurons [144]: for example, during arousal, task execution, and/or a behavioral act, the underlying neuronal populations will increase spiking with respect to baseline. These increased firing rates will engage non-linear feedback loops, effectively changing the system’s response function and the specifics of its emergent oscillations. In contrast, during rest and states of quietness, the spiking activity decreases, which is also reflected by a decrease in oscillatory activity [144]. Further, qEEG oscillations are able to temporally coordinate and control neuronal firing and are proposed to be a basic principle of information processing in the human brain [145][146].
Considering that different qEEG oscillations reflect functionally different components of information processing acting on various temporal scales [136][137], it is possible to map qEEG oscillations onto specific mental and/or behavioral states [147]. qEEG oscillations from the same frequency band may express different functions depending on the conditions they are involved in [148]. This seems biologically plausible: qEEG oscillatory functional diversity creates a rich repertoire of brain activity that can meet the complex computational and communicational demands of the brain during healthy and pathological conditions.
In this context, qEEG measures can provide independent evidence of variations in alertness, attentiveness, memory, emotional regulation, or mental effort. Incorporating them into tests of cognitive function might lead to more sensitive and less ambiguous clinical assessment tools [149][150].
Since information-processing modes depend on the functional integrity of the brain, which, in turn, depends on the orchestrated oscillatory activity of neuronal pools (reflected in the characteristic qEEG rhythms); functional coupling between qEEG oscillations, cognitive functions, and vegetative processes is important.

7. qEEG and Vegetative Status/Autonomic Nervous Systems (ANS)

Several studies have demonstrated the association between qEEGs and ANS [151][152][153][154]. It seems that the brainstem mediates a functional coupling between the ANS and the central nervous system (CNS) assessed by qEEG [155][156][157]. A theoretical concept of the integration between the ANS and CNS was presented by Jennings and Coles [158]. The coordination and communication in and between the autonomic vegetative systems and the brain occur with tuned frequencies in the range of qEEG oscillations, suggesting the existence of resonant links in the brain with all organs of the body (for a review and discussion, see [1]; see also [159]). Basar [1] suggested that such mutual resonances form a coordinated dynamic system that maintains survival functions such as blood pressure, respiratory rhythms, cardiac pacemakers, and body temperature (see also [151][152][155][156][157][158]).
Since the dynamics of the physiologic variables (autonomic system) and the dynamics of brain activity depend on each other, it is reasonable to hypothesize that reduced variability in the activity of the neural networks should cause a concurrent decrease in the variability of autonomic physiologic functions. Indeed, it was demonstrated that a widespread brain injury that causes a derangement in neural networks leads to a reduced complexity of qEEG (measured by entropy, the dynamic repertoire of the probable qEEG states, and operational architectonics) [160][161][162] and reduced heart rate variability [163] in unresponsive patients compared to healthy subjects.
It seems that decreased qEEG variability is coupled with a decrease in the variability of other physiologic variables (autonomic system), which results in reduced physiological adaptability. In turn, reduced physiological adaptability can contribute to stress and weakened immunity, which may further impact the qEEG pattern, creating a downward spiral.

8. qEEG, Stress, and Immunity

There is a strong link between qEEG oscillatory patterns and stress regulatory systems: the hypothalamic–pituitary–adrenocortical (HPA) axis and the sympathetic–adrenomedullary axis [164]. For example, qEEGs recorded from stressed students before an exam revealed a correlation between greater right hemisphere (RH) activation and higher cortisol levels [165]. This is supported by the following facts: (a) the administration of cortisol to healthy participants has been shown to increase RH frontal activation [166], and (b) greater right-sided activation (measured by resting qEEG) is associated with higher levels of basal cortisol compared to their left-activated counterparts [167]. Cortisol also seems to reduce neural interactions between different areas of the brain. Indeed, an inverse relationship between basal cortisol levels and neural interaction between the frontal and parietal cortex has been demonstrated using qEEG connectivity analysis [168].
Considering the link between qEEG oscillatory patterns and stress regulatory systems, it is not surprising that the association between several factors of the immune system and qEEG activity has also been reported [169][170][171][172][173] (for a recent meta-analysis, see [174]). For example, higher levels of right-prefrontal qEEG activation (a) reliably predicted poorer immune response [172] and (b) are characterized by lower levels of natural killer cell activity [175]. These data support the hypothesis that individuals characterized by a more negative affective style have a weaker immune response and, therefore, may be at greater risk for illness than those with a more positive affective style. Additionally, RH activation is associated with hyprecortisolemia, which contributes to the deterioration of immune system functioning and puts depressed patients at a greater risk of developing other illnesses, accounting for depression’s high comorbidity with other diseases [176].

9. qEEG and Cerebral Haemodynamics and Metabolism

Studies have suggested that different qEEG characteristics are related to cerebral hemodynamics and metabolism [177][178][179][180][181][182][183][184][185][186]. Cerebral cortex metabolism disturbance is associated with and may be responsible for cortical neural synchronization anomalies that may manifest as abnormal qEEG oscillations [187]. Additionally, changes in the characteristics of qEEG oscillations (amplitude, power, frequency) are proportional to cerebrovascular damage (CVD) [115]. The qEEG has been shown to be a reliable marker of the decline in neuronal integrity associated with a decline in blood flow [188][189][190][191][192][193][194]. Additionally, studies show a sensitivity greater than 80%, false-positive rates below 5–10%, and correlations of 70% between qEEG and blood flow in ischemic and non-ischemic regions, thus suggesting that the qEEG can reliably detect focal features that can be quite abnormal even if the computer tomography (CT) or MRI scans are still normal (dysfunction without infarction) [195]. Similarly, in patients with subarachnoid hemorrhage, only qEEG could differentiate patients with and without cerebral infarction and not doppler/color-coded duplex sonography [196]. Further, recent meta-analyses have shown that qEEG has prognostic potential in predicting patient independence and stroke severity beyond that afforded by standard clinical assessments [197] (see also [198][199]). Indeed, qEEG changes precede that of multimodal monitoring or confirmation of infarction on CT [200].
Cerebral hemodynamics and metabolism are regulated by a complex interaction between different homeostatic mechanisms where neurotransmitters play a significant role.

10. qEEG and Neurotransmitters

Several studies have suggested a relation between different qEEG oscillations and neuromodulator balance [1][201]. This is because peculiarities of qEEG features result from the interaction of numerous resonance loops within the cortex and between the cortex and subcortical structures, and these interactions are significantly influenced by neurotransmitter concentrations in the brain [202]. Indeed, the levels of activity of different neurotransmitter systems (acetylcholinergic (ACh-ergic), noradrenalinergic (NA-ergic), dopaminergic (DA-ergic), serotonergic (ST-ergic), and GABA-ergic), as well as the patterns of their interaction, are important drivers of qEEG oscillations. For example, the activation of the NA-ergic system is associated with the desynchronization of qEEGs during behavioral excitation [203] and an increase in high-frequency qEEG oscillations [204]. It is also believed that the increased activity of the DA-ergic cerebral systems results in shifts of the frequencies of qEEG oscillations toward higher ranges and facilitates the reaction of desynchronization [205]. Additionally, posterior vs. anterior distribution of qEEG theta oscillations is informative on DA levels [206]. Low ST levels result in a higher power of low-frequency qEEG components [202]; conversely, high ST levels result in the decreased power of low-frequency qEEG components and the higher power of high-frequency qEEG components [207]. Higher relative levels of ACh promote qEEG alpha oscillations, whereas an increased tone of inhibitory monoamine receptors is associated with qEEG delta oscillations (delta rhythm is electromagnetic oscillations in the frequency range of brain activity between 1.5 and 3.5 Hz) [201]. It seems that for each qEEG oscillatory pattern, there is a correlated neurotransmitter mix [208].
Deficiencies or excesses of any of the neurotransmitters will produce a marked departure from homeostatically regulated normative qEEG oscillatory patterns and may contribute to neuro–psycho pathophysiology [195][201]. Indeed, a large body of data suggests that it is possible to unravel distinctive abnormal qEEG oscillatory profiles in terms of specific neurochemical imbalances in particular brain regions [209].

11. qEEG and Neuropsychopathology

The literature indicates that there is a greater proportion of abnormal EEGs in individuals with psychopathology: (a) up to 68% of qEEGs in psychiatric patients display evidence of pathophysiology, and these results have additional utility beyond simply ruling out ‘organic brain lesions’ [210][211]; (b) up to 73% of nonepileptic adults have qEEG epileptiform discharges (EDs) [212] that are attributable to underlying brain abnormalities (traumatic, vascular, tumor, metabolic), medications, and psychiatric disorders (see, for example, [213]); (c) the mean prevalence of interictal qEEG abnormalities in psychogenic nonepileptic seizures is estimated to be 26% [214][215][216][217][218][219][220][221][222]; (d) up to 30% of panic attack patients have demonstrable qEEG abnormalities, especially in atypical presentations of panic attacks, and the incidence of abnormal qEEG findings in mood disorders reaches 40% [223]; (e) up to 78% of antisocial and criminal populations have underlying qEEG abnormalities [224] that are more prevalent in subjects with violent crimes, repeated violence, and motiveless crimes; (f) up to 76% of children with reading disabilities but without severe disorders of behavior have EEG abnormalities [225], and (g) 69% of youngsters with behavior disorders with a predominance of aggressiveness have EEG deviations [226]. Additionally, there is evidence that abnormal EEGs are associated with the following clinical conditions: negative histories (13%), severe head injury or neuropsychiatric disorder (46%), psychopathic personality (88%), and family history of seizures (62%) [227].
Basic mechanisms of cerebral rhythmic activities in norm and pathology are described in detail in Steriade et al. [208]. This emphasizes that the presence of qEEG abnormalities should be inferred as ‘electrographic markers’ of underlying brain dysfunction and is suggestive of the potential usefulness of qEEGs in clinical practice.
Indeed, more recent research shows that certain neuropsychopathologies, such as attention deficit hyperactivity disorder (ADHD), specific learning disabilities, schizophrenia, obsessive–compulsive disorder (OCD), borderline personality disorder (BPD), depression, suicidal ideation, anxiety disorders, traumatic brain injury (TBI), mild cognitive impairment (MCI), Alzheimer’s disease (AD), and other disorders are associated with specific qEEG patterns and that these spontaneous electric potentials provide reliable markers of brain function and dysfunction [52][148][228][229][230][231][232][233][234][235][236][237][238][239][240][241][242] (for reviews, see [195][209][243]).
Given that patients with different disorders display abnormal and distinct qEEG-profiles, it is not surprising that they can be differentially classified utilizing qEEG-variables [244]. For example, qEEG utility in discrimination/differentiation between affective disorders and schizophrenia [245], between Alzheimer and non-Alzheimer dementias [195][246], between sub-types of dementia [247], between depression and dementia [195], between schizophrenia and unipolar and bipolar depression [195], and between panic disorder and depression [195] have been demonstrated. For sensitivity and specificity values of qEEG-based detection/discrimination of patients with specific disorders.
It is argued that the levels of specificity found in qEEG studies are often higher than those found in routinely used clinical tests, such as mammograms, cervical screenings, and brain scans such as CT or single photon emission computed tomography (SPECT) [195][248][249].
Even within the same disorder, qEEGs may be beneficial in identifying the cause of the abnormal behavior. For example, Kropotov distinguishes five reasons for the neurophysiology of ADHD, stating that “[…] mentioned dysfunctions are associated with specific patterns in spontaneous and evoked electrical potentials, recorded from the head by multiple surface electrodes” ([250] (p. 74; see also [251][252]).
Additionally, qEEGs can play a unique role when it comes to dealing with ambiguous or edge cases in clinical practice. It may help to identify/differentiate:
  • Electrical changes that precede the clinical onset of a seizure by tens of seconds to minutes—the early detection of a seizure. It has been shown that patients go through a preictal transition for approximately 0.5 to 1 h before a seizure occurs [253]. On average, the prediction rate is ~81% and has an average warning time of 63 min [254];
  • Whether a given seizure is epileptic or nonepileptic in origin: For example, there are groups of disorders that produce symptoms similar to an epileptic seizure: (a) cardiac arrhythmias causing syncope, episodes caused by cerebrovascular disease, movement disorders, and unusual manifestations of sleep disorders; (b) events of psychiatric origin (often referred to as psychogenic nonepileptic seizures (PNES)) [255];
  • Subclinical seizures: Some seizures recorded during prolonged EEG monitoring may be asymptomatic or ‘subclinical’;
  • Whether the cognitive impairments and behavioral problems in question are due to emotional, psychological, or social factors or because of brain dysfunctions or sensory deficits with quantitatively demonstrable abnormalities in brain electrical activity;
  • Whether the hyperactive sensation-seeking behavior (typical for ADHD and mania) is due to hypervigilance or vigilance autostabilization behavior, which is a compensatory behavioral pattern to counter regulate a hypovigilance state, and whether withdrawal behavior (typical for depression) is due to hypovigilance or the result of a compensatory behavioral pattern that counter-regulates hypervigilance [256][257];
  • Between a degenerative disorder such as AD and pseudodementia due to psychiatric illness [258];
  • Between normal and abnormal maturational patterns, such as brain maturation lag (characterized by a pattern of qEEG that is typical for younger age) and brain maturational deviation (characterized by a pattern of qEEG that is not normal at any age) [259];
  • Between presence or absence of consciousness in minimally and unresponsive patients [162][260][261][262][263].
In this context, different spatial–temporal qEEG patterns may reflect different underlying mechanisms/functions/symptoms; this hints at the existence of several clinical sub-types within a given diagnostic group that are not recognized by the current diagnostic systems [264].
Distinct aspects of pathophysiologic mechanisms may be elucidated depending on which qEEG oscillations or their combinations are altered in the qEEG oscillatory pattern of any given neuropsychopathology. It seems that neuropsychopathology manifests through the considerable reorganization of the composition of qEEG oscillations and their ratios over a broad frequency range of 0.5–30 Hz, which constitutes the dynamic repertoires of qEEG states. These qEEG oscillations are ‘mixed’ or superimposed in proportions that depend on the specific neuromediators and neural circuit disturbance and also depend on the presence of various symptoms and affects. Spatial analysis has revealed that different cortical areas are characterized by varying numbers of qEEG oscillations, with a statistically significant difference in their relative presence and communication within the qEEG oscillatory pattern [148][240].
One aspect that often goes unnoticed by clinicians but is nearly always affected by neuropsychopathology is the experiential Selfhood.

12. qEEG and Experiential Selfhood

Indeed, in various neuropsychopathological conditions, self-consciousness alterations dominate the patient’s phenomenological experiences and have either a long-term or permanent presence [265][266][267]. Even though experiential Selfhood (also referred to as self-consciousness or self-awareness) is a multi-layered concept that is often conceptualized in different ways by various disciplines [268], the currently emerging consensus is that self-referential processing constitutes the core of Selfhood [269][270]. Empirical evidence from neuroscience [269][271][272][273][274] indicates that such self-referential processing is instantiated by a specific self-referential network (SRN) within the brain, sometimes also referred to as the default mode network (DMN) [271][272][273][274][275]. Further, it has been documented that specific qEEG oscillations have a significant positive correlation with the SRN [276][277][278][279][280].
Recently, a three-dimensional neurophysiological model of the complex experiential Selfhood (which is based on the qEEG analysis) was proposed [274][281][282] (for a detailed description, see [283]). This triad model of Selfhood considers the neurophysiological evidence that three major spatially separate yet functionally interacting brain subnets constitute the SRN and account for the phenomenological distinctions between three major aspects of Selfhood, namely, (i) first-person agency (conceptualized as the ‘witnessing observer’ or simply the ‘Self’), (ii) embodiment (conceptualized as ‘representational–emotional agency’ or simply ‘Me’), and (iii) reflection/narration (conceptualized as ‘reflective agency’ or simply ‘I’ ), all of which commensurate with one another [284] and, together, form a unified sense of Selfhood [274] (see also [285]). Each aspect of the triad can be enhanced or weakened depending on the current physiologic and mental state [274][286], voluntary training [281][282], and neuropsychopathology [287][288][289]. Since aspects of Selfhood rarely fall under the purview of clinical practice, researchers present below a few examples of the potential application of qEEGs in the assessment of experiential Selfhood for different neuropsychopathologies.
For example, in its ‘pure’ unmedicated form, major depressive disorder is associated with functional enhancement (measured by qEEG) of all three aspects (‘Self’, ‘Me’, and ‘I’) [288], thus reflecting the well-documented excessive self-focus, increased rumination, and increased embodiment in patients with depression [290][291][292][293][294][295]. One could speculate that “these three components of complex Selfhood (indexed by (qEEG)) synergize with one another in a maladaptive loop and, over time, become habitual, leading to a vicious circle that maintains a disordered affective state that clinically manifests as depression” [288] (p. 34). It has been further proposed that the ‘Self’ plays a chief role here as it organizes, represents, and appraises the salience of interoceptive/emotional/bodily information presented by ‘Me’ and the narrative and semantic-conceptual information presented by ‘I’ [288].
PTSD is characterized by rather different Self–Me–I dynamics [289]. Increased activity (measured by qEEG) of the ‘Self’ aspect was found to be significantly associated with the increased vigilance of PTSD sufferers to their surroundings, with a concurrent shift of their first-person perspective from the current moment in time to the moment of the traumatic event (criterion E, according to DSM-5 [296]). Researchers have speculated [289] that such constant hypervigilance coupled with profound emotional arousal leads to sensory overload and further exacerbates alienation of the Self in such patients [297]. Indeed, the increased activity (measured by qEEG) of ‘Me’ was found to be significantly linked to enhanced emotional, sensory, and bodily states in PTSD sufferers (criterion D, according to DSM-5), such as fear, stress, frozenness, shivering, shaking, trembling, palpitations, and sweating [298][299][300]. These feelings and memories are usually reported as intrusive and unwanted (criterion B, according to DSM-5). Additionally, it was observed that the activity (measured by qEEG) of ‘I’ decreased and that this decrease was associated with a distinct lack of linguistic/contextual information and narrative to accompany the traumatic event (criterion C, according to DSM-5), which is a well-documented phenomenon in PTSD patients [300][301].
A six-year longitudinal analysis of a single patient’s recovery of self-consciousness (from a minimally conscious state until full self-consciousness) after a severe traumatic brain injury has revealed that the recovery of first-person agency (or ‘Self’), representational–emotional agency (or ‘Me’), and reflective agency (or ‘I’) was paralleled by restoration of functional integrity (measured by qEEG) in the three subnets of the SRN [287]. Of note, the recovery dynamic in the Self–Me–I aspects (and corresponding qEEG metrics) was not linear but followed a unique trajectory for every aspect (some recovered more quickly, while others lagged) and was tightly paralleled by (and significantly correlated with) findings from clinical exams and tests [287][302].
Further, converging evidence for a breakdown of qEEG integrity within the SRN in non- and minimally communicative patients with severe brain injuries was found, and this breakdown was proportional to the degree of expression of clinical self-consciousness [275]. More specifically, it was demonstrated that the strength of qEEG integrity within the SRN was smallest or even absent in patients in a vegetative state (VS), intermediate in patients in a minimally conscious state (MCS), and highest in healthy, fully self-conscious subjects. Curiously the strongest decrease in strength of qEEG integrity as a function of loss of self-consciousness was found in the ‘Self’ aspect compared to the ‘Me’ and ‘I’ SRN modules. The central role of ‘Self’ was also found for the prediction of self-consciousness recovery: those VS patients who later recovered stable minimal or full self-consciousness in the course of the disease (up to six years post-injury) showed stronger ‘Self’ functional integrity (measured by qEEG) in the early stage (three months post-injury) compared to those patients who continued to stay in the persistent VS [303].
This brings people to another reason for the clinical and ethical importance of qEEG utility in the assessment of the neurophysiological and neurophenomenological status of unresponsive patients.

13. qEEG and Disorders of Consciousness

A vegetative state (VS), recently re-termed as ‘unresponsive wakefulness syndrome’ (UWS) [304], and MCS belong to the so-called disorders of consciousness or DoCs [305]. While, by convention, VS/UWS patients are unresponsive to their external and internal environments and are thus unconscious [306], patients in MCS show some level of overt awareness and fluctuating ability to follow commands non-reflexively [307] (see also [308]).
The factual simplicity of the qEEG assessment, its portability and adaptability for longitudinal protocols, and its relatively low cost have opened up a wide area of qEEG investigations in the recent decade—these assessments aim to study the pathophysiology of DoCs as well as look for prognostication markers for the recovery of consciousness in DoC patients [309][310]. Already, the simple description of standard EEGs (guided by accurate qualitative scales) has shown a robust correlation of such patterns with both the level of consciousness impairment (VS/UWS or MCS) and the degree of short-term consciousness recovery [311]. These studies reveal that the overall electrical activity of the brain is differentially impaired in patients that fall under different DoCs and that it may be related to the degree of recovery, as follows from the group-analyses [309].
The implementation of more complex numerical computations of the EEG signal—qEEG analyses—has contributed in a much more nuanced way to the evaluation of DoC patients [310], leading to a better understanding of the neural constituents of consciousness’ impairment [162]. For example, studies on qEEG oscillations have demonstrated that patients in VS/UWS have a considerably reduced repertoire of local qEEG oscillations compared to those in MCS or a fully conscious state [260]. Additionally, unawareness in patients with VS/UWS was associated with an altered composition of qEEG oscillations and their proportions compared with a full consciousness state [260][263]. These results confirmed previous observations that loss of consciousness is associated with altered oscillatory contents of the qEEG [312][313][314].
In agreement with these findings, it has been proposed that the degree of reduction in the dynamic correlates of neuronal networks’ complexity measured by the qEEG may be useful for distinguishing patients with different levels of consciousness impairment (VS/UWS vs. MCS) or even as a prognostic measure [161][263][309][314][315][316]. Indeed, evaluation of qEEG spatial–temporal patterns (which reflect functionally connected neuronal assemblies and their dynamics over time) [162][315][317][318] in DoC patients demonstrated that neuronal assemblies become considerably smaller, with shortened life-spans, and they became highly unstable and functionally disconnected (desynchronized) in patients in VS/UWS [162]. In contrast, fluctuating (minimal) awareness in patients who are in MCS is paralleled by partial restoration of qEEG functional integrity, whose parameters approach those of the levels found in healthy, fully conscious participants [162]. These studies lead to the conclusion that consciousness is likely to vanish in the presence of many very small, extremely short-lived, and highly unstable neuronal assemblies that perform their operations completely independently of one another (functional disconnection) and, thus, are not capable of supporting any coherent content to be experienced subjectively. Importantly, it has been documented that the observed impairment in the brain’s functional integrity in DoC patients is independent of brain damage etiology and, thus, reflects functional (and potentially reversible) damage, as opposed to irreversible structural neuronal loss [261]. As a whole, these findings are in keeping with a recent study [319], where it was shown that, in contrast to MCS, the VS/UWS brain is characterized by small, disconnected networks that do not contribute to higher integrative processes [320].
Another factor that may complicate diagnosis and affect both healthy and diseased individuals is aging.

14. qEEG and Aging

Since age-related processes affect both the structural and functional integrity of the brain, it is reasonable to suggest that qEEGs possess age-dependent changes that are both pathology-independent (healthy aging) and pathology-dependent (pathological aging). Indeed, many studies have demonstrated that the aging process is reflected in qEEG changes [59][64][140][321][322][323][324][325][326][327][328][329][330][331][332][333] and is associated with age-related conditions such as cognitive decline, Alzheimer’s disease, mild cognitive impairment, vascular dementia or other dementias, multiple sclerosis, and cerebral tumors [108][112][113][115][120][181][183][334][335][336].
Aging, as is well known, eventually results in death; and death is no longer understood to be an all-or-nothing state but rather a process, the aspects of which may be captured by qEEG.

15. qEEG and Death

Death is often a tragic and somewhat baffling finale of a person’s life. Since the person is unresponsive near or during death, people know little (if anything) about it, especially from the neurophenomenological point of view (neurophenomenology is scientific research aimed at combining neuroscience with phenomenology in order to study the human experience [337]). However, recent studies suggest that qEEG may shed some light on this mysterious phenomenon. The data suggest that the mammalian brain has the potential for high levels of internal information processing (consistent with conscious processing) during clinical death [338][339], suggesting that patients near death may generate a replay of memories [340]. This is supported by electrophysiological studies that have demonstrated (a) that the post-mortem human brain may retain latent capacities to respond with potential life-like properties [341], (b) that auditory systems (measured by event-related potentials) respond similarly to those of healthy controls just hours before death [342], and (c) the resting-state default mode—task-positive network anticorrelations were present among unresponsive hospice patients [343], thus suggesting that unresponsive patients may possess functional architecture in the brain that can support internally oriented thought (mind-wandering) at the end of life. Moreover, analysis of qEEG-unresponsive patients just hours before death demonstrated that they might be able to listen to music, despite being unable to overtly indicate their awareness [344].
Furthermore, studies have shown that the prevalence of qEEGs with electrocerebral activity despite a clinical diagnosis of brain death (BD) was 3.5% [345] to 19.6% [346], thus posing a challenge for the diagnostic criteria of BD and stressing the importance of qEEG utility for the confirmation of BD. Further, the association between qEEG patterns and eventual death has been demonstrated [161][316][347], thus suggesting that the qEEG may have potential prognostic value for evaluating near-term patients’ survival or death.

16. Causality of qEEG Oscillatory Patterns in Neuropsychopathology

The above brief research of qEEG features and properties and their association with neuropsychopathology suggests the existence of circular causality, where, on the one hand, different pathological processes affect the qEEG pattern and, on the other hand, changes in the qEEG pattern affect pathological processes. This supposition is supported by converging empirical evidence: (a) central nervous system (CNS)-active drugs that affect known neuromediators change different features of the qEEG oscillatory pattern in a consistent and predictable manner, with a parallel reduction in symptoms [348][349][350][351]; (b) specific features of the qEEG oscillatory pattern have better predictive power for medication response compared to a syndrome-based diagnosis [352][353][354][355][356][357][358][359]; for example, the overall predictive accuracy in differentiating treatment responders from non-responders is 84%, with a sensitivity of 77% and a specificity of 92% [360]; (c) different features of the qEEG oscillatory pattern predict future (i) decline within the next 7 years in normal elderly people with subjective cognitive complains (no objective evidence of cognitive deficit) [361], (ii) clinical outcomes in patients in the vegetative state 6 years after brain injury [303][315], and (iii) developments of delinquent (antisocial) behavior [362]; (d) normalization of the distorted structure of the qEEG oscillatory pattern by an exogenous magnetic field stimulation changes the subjective experience of neuropsychopathology, accompanied by a clinical decrease (>50% reduction) of symptom severity [363] (see also [364][365]); (e) normalization of atypical qEEG oscillatory patterns through operant conditioning with neurofeedback results in symptom reduction in neuropsychopathologies such as epilepsy [366][367], depression and anxiety [368], schizophrenia [369], addiction [370], ADHD [371][372], sleep disorders [373], autism [374], chronic pain [375], learning difficulties [376], and dyslexia [377]; last but not least, (f) cognitive enhancement in the elderly by qEEG neurofeedback [378][379].
A substantial corpus of evidence supports the proposition that the successful treatment of psychiatric patients results in the normalization of the previously demonstrated qEEG abnormalities [209].
Such circular causality is possible because the qEEG oscillatory pattern is not just a correlate of information processing, communication, integrated phenomenal experience, and the associated neuropsychopathology but is, indeed, a constitute (substrate) of these very things [318][380].
From the above research, it is clear that the qEEG is a natural and non-invasive ‘window’ into the living brain and mind since only the qEEG permits direct observation of the ongoing dynamics and coordinated processes organized in the patterns of brain activity that reflect the overall architecture of information processing, behavior, and subjective experience during both healthy and pathological conditions [381].
To make sense of the ‘view’ from this ‘window’, many different methods have been suggested. However, when processing the qEEG signal, it is essential to remember that it is a neurophysiological phenomenon that has its own peculiarities, regularities, and complex rules of organization which are functionally relevant [9][382][383][384][385] (for reviews, see [14][317][386][387][388]). Only when one knows these characteristics is it possible to make proper use of the qEEG as a tool and to give a more neurophysiologically adequate interpretation of the data. In connection to this, a much deeper understanding of the brain dynamics reflected in the qEEG is essential for progress in psychophysiological, cognitive, and clinical sciences.

References

  1. Basar, E. Brain-Body-Mind in the Nebulous Cartesian System: A Holistic Approach by Oscillations; Springer: New York, NY, USA, 2011; p. 523.
  2. Motamedi-Fakhr, S.; Moshrefi-Torbati, M.; Hill, M.; Hill, C.M.; White, P.R. Signal processing techniques applied to human sleep EEG signals—A review. Biomed. Signal Process. Control. 2014, 10, 21–33.
  3. Da Silva, F.L. Neural mechanisms underlying brain waves: From neural membranes to networks. Electroencephalogr. Clin. Neurophysiol. 1991, 79, 81–93.
  4. Nunez, P.L. Neocortical Dynamics and Human EEG Rhythms; Oxford University Press: New York, NY, USA, 1995; p. 730.
  5. Michel, C.M.; Brandeis, D. The sources and temporal dynamics of scalp electric fields. In Simultaneous EEG and fMRI. Recording, Analysis, and Application; Ullsperger, M., Debener, S.S., Eds.; Oxford University Press: New York, NY, USA, 2010; pp. 1–19.
  6. Freeman, W.J. Mass Action in the Nervous System. Examination of the Neurophysiological Basis of Adaptive Behavior through the EEG; Academic Press: New York, NY, USA, 1975; p. 489.
  7. Corsi-Cabrera, M.; Herrera, P.; Malvido, M. Correlation between EEG and cognitive abilities: Sex differences. Int. J. Neurosci. 1989, 45, 133–141.
  8. Tsodyks, M.; Kenet, T.; Grinvald, A.; Arieli, A. Linking spontaneous activity of single cortical neurons and the underlying functional architecture. Science 1999, 286, 1943–1946.
  9. Nunez, P.L. Toward a quantitative description of large-scale neocortical dynamic function and EEG. Behav. Brain Sci. 2000, 23, 371–437.
  10. Bressler, S.L.; Kelso, J.A.S. Cortical coordination dynamics and cognition. Trends Cogn. Sci. 2001, 5, 26–36.
  11. Moran, R.J.; Stephan, K.E.; Kiebel, S.J.; Rombach, N.; O’Connor, W.T.; Murphy, K.J.; Reilly, R.B.; Friston, K.J. Bayesian estimation of synaptic physiology from the spectral responses of neural masses. Neuroimage 2008, 42, 272–284.
  12. Hadjipapas, A.; Casagrande, E.; Nevado, A.; Barnes, G.R.; Green, G.; Holliday, I.E. Can we observe collective neuronal activity from macroscopic aggregate signals? Neuroimage 2009, 44, 1290–1303.
  13. van Albada, S.J.; Kerr, C.C.; Chiang, A.K.I.; Rennie, C.J.; Robinson, P.A. Neurophysiological changes with age probed by inverse modelling of EEG spectra. Clin. Neurophysiol. 2010, 121, 21–38.
  14. Fingelkurts, A.A.; Fingelkurts, A.A. Short-term EEG spectral pattern as a single event in EEG phenomenology. Open Neuroimag. J. 2010, 4, 130–156.
  15. Klimesch, W. Memory processes, brain oscillations and EEG synchronization. Int. J. Psychophysiol. 1996, 24, 61–100.
  16. Da Silva, F.H.L. The generation of electric and magnetic signals of the brain by local networks. In Comprehensive Human Physiology; Greger, R., Windhorst, U., Eds.; Springer: Berlin/Heidelberg, Germany, 1996; Volume 1, pp. 509–528.
  17. Klimesch, W. EEG alpha and theta oscillations reflect cognitive and memory performance: A review and analysis. Brain Res. Rev. 1999, 29, 169–195.
  18. Basar, E. Brain Function and Oscillations. I Vol. Brain Oscillations: Principles and Approaches; Springer: Berlin/Heidelberg, Germany, 1998; p. 396.
  19. Basar, E.; Basar-Eroglu, C.; Karakas, S.; Schurmann, M. Are cognitive processes manifested in event-related gamma, alpha, theta and delta oscillations in the EEG? Neurosci. Lett. 1999, 259, 165–168.
  20. Basar, E.; Basar-Eroglu, C.; Karakas, S.; Schurmann, M. Brain oscillations in perception and memory. Int. J. Psychophysiol. 2000, 35, 95–124.
  21. Klimesch, W. Interindividual differences in oscillatory EEG activity and cognitive performance. In The Cognitive Neuroscience of Individual Differences; Reinvang, I., Greenlee, M., Herrmann, M., Eds.; BIS: Oldenburg, Germany, 2003; pp. 87–99.
  22. Basar, E.; Özgören, M.; Karakas, S.; Basar-Eroglu, C. Super-synergy in the brain: The grandmother percept is manifested by multiple oscillations. Int. J. Bifurcat. Chaos 2004, 14, 453–491.
  23. Klimesch, W.; Schack, B.; Sauseng, P. The functional significance of theta and upper alpha oscillations. Exp. Psychol. 2005, 52, 99–108.
  24. Jurko, M.F.; Giurintano, L.P.; Giurintano, S.L.; Andy, O.J. Spontaneous awake EEG patterns in three lines of primate evolution. Behav. Biol. 1974, 10, 377–384.
  25. Van den Bergh, B.R.; Mulder, E.J.; Mennes, M.; Glover, V. Antenatal maternal anxiety and stress and the neurobehavioural development of the fetus and child: Links and possible mechanisms. A review. Neurosci. Biobehav. Rev. 2005, 29, 237–258.
  26. Class, Q.A.; Lichtenstein, P.; Langstrom, N.; D’Onofrio, B.M. Timing of prenatal maternal exposure to severe life events and adverse pregnancy outcomes: A population study of 2.6 million pregnancies. Psychosom. Med. 2011, 73, 234–241.
  27. Grigoriadis, S.; VonderPorten, E.H.; Mamisashvili, L.; Tomlinson, G.; Dennis, C.L.; Koren, G.; Steiner, M.; Mousmanis, P.; Cheung, A.; Radford, K.; et al. The impact of maternal depression during pregnancy on perinatal outcomes: A systematic review and meta-analysis. J. Clin. Psychiatry 2013, 74, e321–e341.
  28. Slykerman, R.F.; Thompson, J.; Waldie, K.; Murphy, R.; Wall, C.; Mitchell, E.A. Maternal stress during pregnancy is associated with moderate to severe depression in 11-year-old children. Acta Paediatr. 2015, 104, 68–74.
  29. Papadopoulou, Z.; Vlaikou, A.M.; Theodoridou, D.; Markopoulos, G.S.; Tsoni, K.; Agakidou, E.; Drosou-Agakidou, V.; Turck, C.W.; Filiou, M.D.; Syrrou, M. Stressful newborn memories: Pre-conceptual, in utero, and postnatal events. Front. Psychiatry 2019, 10, 220.
  30. Prichep, L.S.; Kowalik, S.C.; Alper, K.; de Jesus, C. Quantitative EEG characteristics of children exposed in utero to cocaine. Clin. Electroencephalogr. 1995, 26, 166–172.
  31. Matousek, M.; Petersen, I. Frequency analysis of the EEG in normal children and adolescents. In Automation of Clinical Electroencephalography; Kellaway, P., Petersen, I., Eds.; Raven Press: New York, NY, USA, 1973; pp. 75–102.
  32. Cragg, L.; Kovacevic, N.; McIntosh, A.R.; Poulsen, C.; Martinu, K.; Leonard, G.; Paus, T. Maturation of EEG power spectra in early adolescence: A longitudinal study. Dev. Sci. 2011, 14, 935–943.
  33. Scraggs, T.L. EEG maturation: Viability through adolescence. Neurodiagn. J. 2012, 52, 176–203.
  34. Kaminska, A.; Eisermann, M.; Plouin, P. Child EEG (and maturation). In Handbook of Clinical Neurology, Clinical Neurophysiology: Basis and Technical Aspects, 3rd ed.; Levin, K.H., Chauvel, P., Eds.; Elsevier B.V.: Amsterdam, The Netherlands, 2019; Volume 160, pp. 125–142.
  35. Zhadin, M.N. Rhythmic processes in the cerebral cortex. J. Theor. Biol. 1984, 108, 565–595.
  36. Anokhin, A.P.; Birbaumer, N.; Lutzenberger, W.; Nikolaev, A.; Vogel, F. Age increases brain complexity. Electroencephalogr. Clin. Neurophysiol. 1996, 99, 63–68.
  37. Schutter, D.J.L.G.; Leitner, C.; Kenemans, J.L.; van Honk, J. Electrophysiological correlates of cortico-subcortical interaction: A cross-frequency spectral EEG analysis. Clin. Neurophysiol. 2006, 117, 381–387.
  38. Marosi, E.; Harmony, T.; Sánchez, L.; Becker, J.; Bernal, J.; Reyes, A.; de León, A.E.D.; Rodríguez, M.; Fernández, T. Maturation of the coherence of EEG activity in normal and learning-disabled children. EEG Clin. Neurophysiol. 1992, 83, 350–357.
  39. Lukashevich, I.P.; Machinskaya, R.I.; Fishman, M.N. Diagnosis of the functional state of the brain in young school-age children with learning difficulties. Hum. Physiol. (Fiziol Cheloveka) 1994, 20, 34–46.
  40. Ulrich, G. Psychiatric Electroencephalography. In Updated and Revised Edition (2002) of the Original Textbook Psychiatrische Elektroenzephalographie (in German); Gustav Fischer Verlag: New York, NY, USA, 1994; p. 343.
  41. Zentner, M. Antisocial personalities. In Adult Psychopathology. A Social Work Perspective; Turner, F.J., Ed.; The Free Press: New York, NY, USA, 1984; pp. 345–363.
  42. Davies, R.K. Incest: Some neuropsychiatric findings. Int. J. Psychiatry Med. 1979, 9, 117–121.
  43. McFarlane, A.; Clark, C.R.; Bryant, R.A.; Williams, L.M.; Niaura, R.; Paul, R.H.; Hitsman, B.L.; Stroud, L.; Alexander, D.M.; Gordon, E. The impact of early life stress on psychophysiological, personality and behavioural measures in 740 non-clinical subjects. J. Integr. Neurosci. 2005, 4, 27–40.
  44. Taylor, S.E. Mechanisms linking early life stress to adult health outcomes. Proc. Natl. Acad. Sci. USA 2010, 107, 8507–8512.
  45. Harmony, T.; Alvarez, A.; Pascual, R.; Ramos, A.; Marosi, E.; De León, A.E.D.; Valdés, P.; Becker, J. EEG maturation on children with different economic and psychosocial characteristics. Int. J. Neurosci. 1988, 41, 103–113.
  46. Otero, G.A.; Pliego-Rivero, F.B.; Fernández, T.; Ricardo, J. EEG development in children with sociocultural disadvantages: A follow-up study. Clin. Neurophysiol. 2003, 114, 1918–1925.
  47. Marshall, P.J.; Fox, N.A. Bucharest early intervention project core group. A comparison of the electroencephalogram between institutionalized and community children in Romania. J. Cogn. Neurosci. 2004, 16, 1327–1338.
  48. Howells, F.M.; Stein, D.J.; Russell, V.A. Childhood trauma is associated with altered cortical arousal: Insights from an EEG study. Front. Integr. Neurosci. 2012, 6, 120.
  49. Vanderwert, R.E.; Zeanah, C.H.; Fox, N.A.; Nelson, C.A. Normalization of EEG activity among previously institutionalized children placed into foster care: A 12-year follow-up of the Bucharest Early Intervention Project. Dev. Cogn. Neurosci. 2016, 17, 68–75.
  50. Bosch-Bayard, J.; Razzaq, F.A.; Lopez-Naranjo, C.; Wang, Y.; Li, M.; Galan-Garcia, L.; Calzada-Reyes, A.; Virues-Alba, T.; Rabinowitz, A.G.; Suarez-Murias, C.; et al. Early protein energy malnutrition impacts life-long developmental trajectories of the sources of EEG rhythmic activity. NeuroImage 2022, 254, 119144.
  51. Black, L.M.; Hudspeth, W.J.; Townsend, A.L.; Bodenhamer-Davis, E. EEG Connectivity Patterns in Childhood Sexual Abuse: A Multivariate Application Considering Curvature of Brain Space. J. Neurother. 2008, 12, 141–160.
  52. Lee, S.-H.; Park, Y.; Jin, M.J.; Lee, Y.J.; Hahn, S.W. Childhood trauma associated with enhanced high frequency band powers and induced subjective inattention of adults. Front. Behav. Neurosci. 2017, 11, 148.
  53. Thatcher, R.W.; Walker, R.A.; Gerson, I.; Geisler, F.H. EEG discriminant analysis of mild head trauma. Electroencephalogr. Clin. Neurophysiol. 1989, 73, 10–94.
  54. Hooshmand, H.; Beckner, E.; Radfar, R. Technical and clinical aspects of topographic brain mapping. Clin. Electroencephalogr. 1989, 20, 235–247.
  55. Thornton, K.E. The electrophysiological effects of a brain injury on auditory memory functioning: The QEEG correlates of impaired memory. Arch. Clin. Neuropsychol. 2003, 18, 363–378.
  56. Stassen, H.H. Computerized recognition of persons by EEG spectral patterns. Electroencephalogr. Clin. Neurophysiol. 1980, 49, 190–194.
  57. Gasser, T.; Bacher, P.; Steinberg, H. Test–retest reliability of spectral parameters of the EEG. Electroencephalogr. Clin. Neurophysiol. 1985, 60, 312–319.
  58. Salinsky, M.C.; Oken, B.S.; Morehead, L. Test–retest reliability in EEG frequency analysis. Electroencephalogr. Clin. Neurophysiol. 1991, 79, 382–392.
  59. Pollock, V.E.; Schneider, L.S.; Lyness, S.A. Reliability of topographic quantitative EEG amplitude in healthy late-middle-aged and elderly subjects. Electroencephalogr. Clin. Neurophysiol. 1991, 79, 20–26.
  60. Burgess, A.; Gruzelier, J. Individual reliability of amplitude distribution in topographical mapping of EEG. Electroencephalogr. Clin. Neurophysiol. 1993, 86, 219–223.
  61. Harmony, T.; Fernandez, T.; Rodriguez, M.; Reyes, A.; Marosi, E.; Bernal, J. Test–retest reliability of EEG spectral parameters during cognitive tasks: II. Coherence. Int. J. Neurosci. 1993, 68, 263–271.
  62. Lund, T.R.; Sponheim, S.R.; Iacono, W.G.; Clementz, B.A. Internal consistency reliability of resting EEG power spectra in schizophrenic and normal subjects. Psychophysiology 1995, 32, 66–71.
  63. Stassen, H.H.; Bomben, G.; Hell, D. Familial brain wave patterns: Study of a 12-sib family. Psychiatr. Genet. 1998, 8, 141–153.
  64. Dustman, R.E.; Shearer, D.E.; Emmerson, R.Y. Life-span changes in EEG spectral amplitude, amplitude variability and mean frequency. Clin. Neurophysiol. 1999, 110, 1399–1409.
  65. Kondacs, A.; Szabo, M. Long-term intra-individual variability of the background EEG in normals. Clin. Neurophysiol. 1999, 110, 1708–1716.
  66. Dünki, R.M.; Schmid, B.; Stassen, H.H. Intraindividual specificity and stability of human EEG: Comparing a linear vs. a onlinear approach. Methods Inf. Med. 2000, 39, 78–82.
  67. Poulos, M.; Rangoussi, M.; Alexandris, N.; Evangelou, A. Person identification from the EEG using nonlinear signal classification. Methods Inf. Med. 2002, 41, 64–75.
  68. Maltez, J.; Hyllienmark, L.; Nikulin, V.V.; Brismar, T. Time course and variability of power in different frequency bands of EEG during resting conditions. Neurophysiol. Clin. 2004, 34, 195–202.
  69. Fingelkurts, A.A.; Fingelkurts, A.A.; Ermolaev, V.A.; Kaplan, A.Y. Stability, reliability and consistency of the compositions of brain oscillations. Int. J. Psychophysiol. 2006, 59, 116–126.
  70. Vuga, M.; Fox, N.A.; Cohn, J.F.; George, C.J.; Levenstein, R.M.; Kovacs, M. Long-term stability of frontal electroencephalographic asymmetry in adults with a history of depression and controls. Int. J. Psychophysiol. 2006, 59, 107–115.
  71. Näpflin, M.; Wildi, M.; Sarnthein, J. Test-retest reliability of resting EEG spectra validates a statistical signature of persons. Clin. Neurophysiol. 2007, 118, 2519–2524.
  72. Towers, D.N.; Allen, J.J. A better estimate of the internal consistency reliability of frontal EEG asymmetry scores. Psychophysiology 2009, 46, 132–142.
  73. Cannon, R.L.; Baldwin, D.R.; Shaw, T.L.; Diloreto, D.J.; Phillips, S.M.; Scruggs, A.M.; Riehl, T.C. Reliability of quantitative EEG (qEEG) measures and LORETA current source density at 30 days. Neurosci. Lett. 2012, 518, 27–31.
  74. Grandy, T.H.; Werkle-Bergner, M.; Chicherio, C.; Schmiedek, F.; Lövdén, M.; Lindenberger, U. Peak individual alpha frequency qualifies as a stable neurophysiological trait marker in healthy younger and older adults. Psychophysiology 2013, 50, 570–582.
  75. Vogel, F. The genetic basis of the normal human electroencephalogram (EEG). Humangenetik 1970, 10, 91–114.
  76. Lykken, D.T.; Tellegen, A.; Thorkelson, K. Genetic determination of EEG frequency spectra. Biol. Psychol. 1974, 1, 245–259.
  77. Lykken, D.T.; Tellegen, A.; Iacono, W.G. EEG spectra in twins: Evidence for a neglected mechanism of genetic determination. Physiol. Psychol. 1982, 10, 60–65.
  78. Stassen, H.H.; Bomben, G.; Propping, P. Genetic aspects of the EEG: An investigation into the within-pair similarity of monozigotic and dyzigotic twins with a new method of analysis. Electroencephalogr. Clin. Neurophysiol. 1987, 66, 489–501.
  79. van Beijsterveldt, C.E.; Boomsma, D.I. Genetics of the human electroencephalogram (EEG) and event-related brain potentials (ERPs): A review. Hum. Genet. 1994, 94, 319–330.
  80. Christian, J.C.; Morzorati, S.; Norton, J.A., Jr.; Williams, C.J.; O’Connor, S.; Li, T.K. Genetic analysis of the resting electroencephalographic power spectrum in human twins. Psychophysiology 1996, 33, 584–591.
  81. van Baal, G.C.; De Geus, E.J.; Boomsma, D.I. Genetic architecture of EEG power spectra in early life. Electroencephalogr. Clin. Neurophysiol. 1996, 98, 502–514.
  82. van Beijsterveldt, C.E.; Molenaar, P.C.; De Geus, E.J.; Boomsma, D.I. Heritability of human brain functioning as assessed by electroencephalography. Am. J. Hum. Genet. 1996, 58, 562–573.
  83. Posthuma, D.; Neale, M.C.; Boomsma, D.I.; De Geus, E.J.C. Are smarter brains running faster? Heritability of alpha peak frequency, IQ, and their interrelation. Behav. Genet. 2001, 31, 567–579.
  84. van Baal, G.; van Beijsterveldt, C.; Molenaar, P.; Boomsma, D.; De Geus, E. A genetic perspective on the developing brain: Electrophysiological indices of neural functioning in young and adolescent twins. Eur. Psychol. 2001, 6, 254–263.
  85. van Beijsterveldt, C.E.M.; van Baal, G. Twin and family studies of the human electroencephalogram: A review and a meta-analysis. Biol. Psychol. 2002, 61, 111–138.
  86. Smit, D.J.A.; Posthuma, D.; Boomsma, D.I.; De Geus, E.J.C. Heritability of background EEG across the power spectrum. Psychophysiology 2005, 42, 691–697.
  87. Anokhin, A.P.; Müller, V.; Lindenberger, U.; Heath, A.C.; Myers, E. Genetic influences on dynamic complexity of brain oscillations. Neurosci. Lett. 2006, 397, 93–98.
  88. Smit, C.M.; Wright, M.J.; Hansell, N.K.; Geffen, G.M.; Martin, N.G. Genetic variation of individual alpha frequency (IAF) and alpha power in a large adolescent twin sample. Int. J. Psychophysiol. 2006, 61, 235–243.
  89. Tang, Y.; Chorlian, D.B.; Rangaswamy, M.; O’Connor, S.; Taylor, R.; Rohrbaugh, J.; Porjesz, B.; Begleiter, H. Heritability of bipolar EEG spectra in a large sib-pair population. Behav. Genet. 2007, 37, 302–313.
  90. Eischen, S.E.; Luckritz, J.Y.; Polich, J. Spectral analysis of EEG from families. Biol. Psychol. 1995, 41, 61–68.
  91. Begleiter, H.; Porjesz, B. Genetics of human brain oscillations. Int. J. Psychophysiol. 2006, 60, 162–171.
  92. Winterer, G.; Smolka, M.; Samochowiec, J.; Ziller, M.; Mahlberg, R.; Gallinat, J.; Rommelspacher, H.P.; Herrmann, W.M.; Sander, T. Association of EEG coherence and an exonic GABA(B)R1 gene polymorphism. Am. J. Med. Genet. 2003, 117B, 51–56.
  93. Porjesz, B.; Almasy, L.; Edenberg, H.; Wang, K.; Chorlian, D.B.; Foroud, T.; Goate, A.; Rice, J.P.; O’Connor, S.; Rohrbaugh, J.; et al. Linkage disequilibrium between the beta frequency of the human EEG and a GABAa receptor gene locus. Proc. Natl. Acad. Sci. USA 2002, 99, 3729–3733.
  94. Edenberg, H.J.; Dick, D.M.; Xuei, X.; Tian, H.; Almasy, L.; Bauer, L.O.; Crowe, R.R.; Goate, A.; Hesselbrock, V.; Jones, K.; et al. Variations in GABRA2, encoding the alpha 2 subunit of the GABA(A) receptor, are associated with alcohol dependence and with brain oscillations. Am. J. Hum. Genet. 2004, 74, 705–714.
  95. Winterer, G.; Mahlberg, R.; Smolka, M.N.; Samochowiec, J.; Ziller, M.; Rommelspacher, H.P.; Herrmann, W.M.; Schmidt, L.G.; Sander, T. Association analysis of exonic variants of the GABA(B)-receptor gene and alpha electroencephalogram voltage in normal subjects and alcohol-dependent patients. Behav. Genet. 2003, 33, 7–15.
  96. Ducci, F.; Enoch, M.A.; Yuan, Q.; Shen, P.H.; White, K.V.; Hodgkinson, C.; Goldman, D. HTR3B is associated with alcoholism with antisocial behavior and alpha EEG power--an intermediate phenotype for alcoholism and co-morbid behaviors. Alcohol 2009, 43, 73–84.
  97. Enoch, M.A.; White, K.V.; Waheed, J.; Goldman, D. Neurophysiological and genetic distinctions between pure and comorbid anxiety disorders. Depress. Anxiety 2008, 25, 383–392.
  98. Enoch, M.A.; Shen, P.H.; Ducci, F.; Yuan, Q.; Liu, J.; White, K.V.; Albaugh, B.; Hodgkinson, C.A.; Goldman, D. Common genetic origins for EEG, alcoholism and anxiety: The role of CRH-BP. PLoS ONE 2008, 3, e3620.
  99. Enoch, M.A.; Xu, K.; Ferro, E.; Harris, C.R.; Goldman, D. Genetic origins of anxiety in women: A role for a functional catechol-O-methyltransferase polymorphism. Psychiatr. Genet. 2003, 13, 33–41.
  100. Enoch, M.A.; Rohrbaugh, J.W.; Davis, E.Z.; Harris, C.R.; Ellingson, R.J.; Andreason, P.; Moore, V.; Varner, J.L.; Brown, G.L.; Eckardt, M.J. Relationship of genetically transmitted alpha EEG traits to anxiety disorders and alcoholism. Am. J. Med. Genet. 1995, 60, 400–408.
  101. Enoch, M.A.; White, K.V.; Harris, C.R.; Robin, R.W.; Ross, J.; Rohrbaugh, J.W.; Goldman, D. Association of low-voltage alpha EEG with a subtype of alcohol use disorders. Alcohol. Clin. Exp. Res. 1999, 23, 1312–1319.
  102. Zoon, H.F.; Veth, C.P.; Arns, M.; Drinkenburg, W.H.; Talloen, W.; Peeters, P.J.; Kenemans, J.L. EEG alpha power as an intermediate measure between brain-derived neurotrophic factor Val66Met and depression severity in patients with major depressive disorder. J. Clin. Neurophysiol. 2013, 30, 261–267.
  103. Bodenmann, S.; Rusterholz, T.; Dürr, R.; Stoll, C.; Bachmann, V.; Geissler, E.; JaggiSchwarz, K.; Landolt, H.P. The functional val158met polymorphism of COMT predicts interindividual differences in brain alpha oscillations in young men. J. Neurosci. 2009, 29, 10855–10862.
  104. Meyers, J.L.; Zhang, J.; Chorlian, D.B.; Pandey, A.K.; Kamarajan, C.; Wang, J.-C.; Wetherill, L.; Lai, D.; Chao, M.; Chan, G.; et al. A genome-wide association study of interhemispheric theta EEG coherence: Implications for neural connectivity and alcohol use behavior. Mol. Psychiatry 2021, 26, 5040–5052.
  105. Venables, N.C.; Bernat, E.M.; Sponheim, S.R. Genetic and disorder-specific aspects of resting state EEG abnormalities in schizophrenia. Schizophr. Bull. 2009, 35, 826–839.
  106. da Silva, F.H.L.; van Rotterdam, A.; Barts, P.; van Heusden, E.; Burr, W. Models of neuronal populations: The basic mechanism of rhythmicity. In Perspectives of Brain Research. Progress in Brain Research; Corner, M.A., Swaab, D.F., Eds.; Elsevier: Amsterdam, The Netherlands, 1976; Volume 45, pp. 281–308.
  107. Hughes, J.R.; Cayaffa, J.J. The EEG in patients at different ages without organic cerebral disease. Electroencephalogr. Clin. Neurophysiol. 1977, 42, 776–784.
  108. Goldensohn, E.S. Use of EEG for evaluation of focal intracranial lesions. In Current Practice of Clinical Electroencephalography; Klass, D.W., Daly, D.D., Eds.; Raven: New York, NY, USA, 1979; pp. 307–341.
  109. da Silva, F.H.L.; Vos, J.E.; Mooibroek, J.; van Rotterdam, A. Relative contributions of intracortical and thalamo-cortical processes in the generation of alpha rhythms, revealed by partial coherence analysis. Electroencephalogr. Clin. Neurophysiol. 1980, 50, 449–456.
  110. Steriade, M.; Llinas, R.R. The functional states of the thalamus and the associated neuronal interplay. Physiol. Rev. 1988, 68, 649–742.
  111. Anokhin, A.; Vogel, F. EEG alpha rhythm frequency and intelligence in normal adults. Intelligence 1996, 23, 1–14.
  112. Leocani, L.; Locatelli, T.; Martinelli, V.; Rovaris, M.; Falautano, M.; Filippi, M.; Magnani, G.; Comi, G. Electroencephalographic coherence analysis in multiple sclerosis: Correlation with clinical, neuropsychological, and MRI findings. J. Neurol. Neurosurg. Psychiatry 2000, 69, 192–198.
  113. Moretti, D.V.; Babiloni, C.; Binetti, G.; Cassetta, E.; Dal Forno, G.; Ferreric, F.; Ferri, R.; Lanuzza, B.; Miniussi, C.; Nobili, F.; et al. Individual analysis of EEG frequency and band power in mild Alzheimer’s disease. Clin. Neurophysiol. 2004, 115, 299–308.
  114. Nunez, P.L.; Srinivasan, R. A theoretical basis for standing and traveling brain waves. Clin. Neurophysiol. 2006, 117, 2425–2435.
  115. Moretti, D.V.; Miniussi, C.; Frisoni, G.; Zanetti, O.; Binetti, G.; Geroldi, C.; Galluzzi, S.; Rossini, P.M. Vascular damage and EEG markers in subjects with mild cognitive impairment. Neurophysiol. Clin. 2007, 118, 1866–1876.
  116. Babiloni, C.; Frisoni, G.B.; Pievani, M.; Vecchio, F.; Lizio, R.; Buttiglione, M.; Geroldi, C.; Fracassi, C.; Eusebi, F.; Ferri, R.; et al. Hippocampal volume and cortical sources of EEG alpha rhythms in mild cognitive impairment and Alzheimer disease. Neuroimage 2009, 44, 123–135.
  117. Valdés-Hernández, P.A.; Ojeda-González, A.; Martínez-Montes, E.; Lage-Castellanos, A.; Virués-Alba, T.; Valdés-Urrutia, L.; Valdes-Sosa, P.A. White matter architecture rather than cortical surface area correlates with the EEG alpha rhythm. NeuroImage 2010, 49, 2328–2339.
  118. Bhattacharya, B.S.; Coyle, D.; Maguire, L.P. A thalamo-cortico-thalamic neural mass model to study alpha rhythms in Alzheimer’s disease. Neural Netw. 2011, 24, 631–645.
  119. Jann, K.; Federspiel, A.; Giezendanner, S.; Andreotti, J.; Kottlow, M.; Dierks, T.; Koenig, T. Linking brain connectivity across different time scales with electroencephalogram, functional magnetic resonance imageing, and diffusion tensor imageing. Brain Connect 2012, 2, 11–20.
  120. Garcés, P.; Vicente, R.; Wibral, M.; Pineda-Pardo, J.Á.; López, M.E.; Aurtenetxe, S.; Marcos, A.; de Andrés, M.E.; Yus, M.; Sancho, M.; et al. Brain-wide slowing of spontaneous alpha rhythms in mild cognitive impairment. Front. Ageing Neurosci. 2013, 5, 100.
  121. Babiloni, C.; Carducci, F.; Lizio, R.; Vecchio, F.; Baglieri, A.; Bernardini, S.; Cavedo, E.; Bozzao, A.; Buttinelli, C.; Esposito, F.; et al. Resting state cortical electroencephalographic rhythms are related to gray matter volume in subjects with mild cognitive impairment and Alzheimer’s disease. Hum. Brain Mapp. 2013, 34, 1427–1446.
  122. Hindriks, R.; van Putten, M.J.A.M. Thalamo-cortical mechanisms underlying changes in amplitude and frequency of human alpha oscillations. NeuroImage 2013, 70, 150–163.
  123. Thatcher, R.W.; Biver, C.; McAlaster, R.; Salazar, A.M. Biophysical linkage between MRI and EEG coherence in traumatic brain injury. NeuroImage 1998, 8, 307–326.
  124. Thatcher, R.W.; Biver, C.L.; Gomez-Molina, J.F.; North, D.; Curtin, R.; Walker, R.W.; Salazar, A. Estimation of the EEG power spectrum by MRI T2 relaxation time in traumatic brain injury. Clin. Neurophysiol. 2001, 112, 1729–1745.
  125. Ray, W.; Cole, H. EEG alpha activity reflects attentional demands and beta activity reflects emotional and cognitive processes. Science 1985, 228, 750–752.
  126. Lazarev, V.V. Factorial structure of the principal EEG parameters during intellectual activity. I. Local characteristics of nonhomogeneity of functional states. Hum. Physiol. 1986, 12, 375–382, (A translation of Fiziol. Cheloveka).
  127. Lazarev, V.V. Factorial structure of the principal EEG parameters during intellectual activity. II. Topography of functional states. Hum. Physiol. 1987, 13, 9–12, (A translation of Fiziol. Cheloveka).
  128. Lazarev, V.V. On the intercorrelation of some frequency and amplitude parameters of the human EEG and its functional significance. Com. I. Multidimensional neurodynamic organization of functional states of the brain during intellectual, perceptive and motor activity in normal subjects. Int. J. Psychophysiol. 1998, 28, 77–98.
  129. Lazarev, V.V. On the intercorrelation of some frequency and amplitude parameters of the human EEG and its functional significance. Com. II. Neurodynamic imbalance in endogenous asthenic-like disorders. Int. J. Psychophysiol. 1998, 29, 277–289.
  130. Mizuki, Y. Frontal lobe: Mental function and EEG. Am. J. EEG Technol. 1987, 27, 91–101.
  131. Klimesch, W.; Schimke, H.; Ladurner, G.; Pfurtscheller, G. Alpha frequency and memory performance. J. Psychophysiol. 1990, 4, 381–390.
  132. Klimesch, W.; Schimke, H.; Pfurtscheller, G. Alpha frequency, cognitive load and memory performance. Brain Topogr. 1993, 5, 241–251.
  133. Harmony, T.; Fernandez, T.; Silva, J.; Bernal, J.; Díaz-Comas, L.; Reyes, A.; Marosi, E.; Rodríguez, M.; Rodríguez, M. EEG delta activity: An indicator of attention to internal processing during performance of mental tasks. Int. J. Psychophysiol. 1996, 24, 161–171.
  134. Doppelmayr, M.; Klimesch, W.; Schwaiger, J.; Auinger, P.; Winkler, T. Theta synchronization in the human EEG and episodic retrieval. Neurosci. Lett. 1998, 257, 41–44.
  135. Basar, E. Brain Function and Oscillations. II Vol. Integrative Brain Function. Neurophysiology and Cognitive Processes; Springer: Berlin/Heidelberg, Germany, 1999; p. 515.
  136. Basar, E.; Basar-Eroglu, C.; Karakas, S.; Schurmann, M. Gamma, alpha, delta, and theta oscillations govern cognitive processes. Int. J. Psychophysiol. 2001, 39, 241–248.
  137. Basar, E.; Schurmann, M.; Sakowitz, O. The selectively distributed theta system: Functions. Int. J. Psychophysiol. 2001, 39, 197–212.
  138. Angelakis, E.; Lubar, J.F.; Stathopoulou, S. Electroencephalographic peak alpha frequency correlates of cognitive traits. Neurosci. Lett. 2004, 371, 60–63.
  139. Angelakis, E.; Lubar, J.F.; Stathopoulou, S.; Kounios, J. Peak alpha frequency: An electroencephalographic measure of cognitive preparedness. Clin. Neurophysiol. 2004, 115, 887–897.
  140. Clark, C.R.; Veltmeyer, M.D.; Hamilton, R.J.; Simms, E.; Paul, R.; Hermens, D.; Gordon, E. Spontaneous alpha peak frequency predicts working memory performance across the age span. Int. J. Psychophysiol. 2004, 53, 1–9.
  141. Knyazev, G.G. Motivation, emotion, and their inhibitory control mirrored in brain oscillations. Neurosci. Biobehav. Rev. 2007, 31, 377–395.
  142. Basar, E. A review of alpha activity in integrative brain function: Fundamental physiology, sensory coding, cognition and pathology. Int. J. Psychophysiol. 2012, 86, 1–24.
  143. Grandy, T.H.; Werkle-Bergner, M.; Chicherio, C.; Lövdén, M.; Schmiedek, F.; Lindenberger, U. Individual alpha peak frequency is related to latent factors of general cognitive abilities. NeuroImage 2013, 79, 10–18.
  144. Mierau, A.; Klimesch, W.; Lefebvre, J. State-dependent alpha peak frequency shifts: Experimental evidence, potential mechanisms and functional implications. Neuroscience 2017, 360, 146–154.
  145. Engel, A.K.; Fries, P.; Singer, W. Dynamic predictions: Oscillations and synchrony in top-down processing. Nat. Rev. Neurosci. 2001, 2, 704–716.
  146. Buzsáki, G.; Draguhn, A. Neuronal oscillations in cortical networks. Science 2004, 304, 1926–1929.
  147. Gazzaniga, M.S.; Ivry, R.B.; Mangun, G.R. Cognitive Neuroscience: The Biology of The Mind, 2nd ed.; W.W. Norton &Company: New York, NY, USA, 2002; p. 681.
  148. Fingelkurts, A.A.; Fingelkurts, A.A. EEG oscillatory states: Universality, uniqueness and specificity across healthy-normal, altered and pathological brain conditions. PLoS ONE 2014, 9, e87507.
  149. John, E.R.; Karmel, B.Z.; Corning, W.C.; Easton, P.; Brown, D.; Ahn, H.; John, M.; Harmony, T.; Prichep, L.; Toro, A.; et al. Neurometrics: Numerical taxonomy identifies different profiles of brain functions within groups of behaviourally similar people. Science 1977, 196, 1393–1410.
  150. Gevins, A. Electrophysiological imaging of brain function. In Brain Mapping. The Methods, 2nd ed.; Toga, A.W., Mazzoitta, J.C., Eds.; Elsevier Science: New York, NY, USA, 2002; pp. 175–188.
  151. Gebber, G.L.; Zhong, S.; Barman, S.M. The functional significance of the 10-Hz sympathetic rhythm: A hypothesis. Clin. Exp. Hypertens. 1995, 17, 181–195.
  152. Osintseva, Y.V.; Nadezhdina, M.V.; Zhezher, M.N.; Kurus, O.S.; Skulskaya, N.I. The vegetative status and bioelectric activity of the brain in different terms of the remote period of a fighting craniocereberal trauma. Bull. Sib. Med. 2010, 4, 84–88.
  153. Olbrich, S.; Sander, C.; Matschinger, H.; Mergl, R.; Trenner, M.; Schönknecht, P.; Hegerl, U. Brain and body. Associations between EEG-vigilance and the autonomous nervous system activity during rest. J. Psychophysiol. 2011, 25, 190–200.
  154. Duschek, S.; Wörsching, J.; del Paso, G.A.R. Autonomic cardiovascular regulation and cortical tone. Clin. Physiol. Funct. Imaging 2014, 35, 383–392.
  155. Langhorst, P.; Stroh-Werz, M.; Dittmar, K.; Camerer, H. Facultative coupling of reticular neuronal activity with peripheral cardiovascular and central cortical rhythms. Brain Res. 1975, 87, 407–418.
  156. Langhorst, P.; Schulz, P.; Lambertz, M.; Schulz, G.; Camerer, H. Dynamic characteristics of the “unspecific brain stem system”. In Central Interaction between Respiratory and Cardiovascular Control System; Koepchen, H.P., Hilton, S.M., Trzebski, A., Eds.; Springer: New York, NY, USA, 1980; pp. 30–41.
  157. Achimowicz, J.Z. Evaluation of pilot psychophysiological state in real time by analysis of spectral dynamics in EEG and ERP correlates of sensory and cognitive brain functions and its possible coupling with autonomic nervous system. In Human System Division, Research Proposal Draft Version 10.5.; H.G. Armstrong Aero-Space Medical Research Laboratory, Wright–Petterson Air Force Base: Dayton, OH, USA, 1992.
  158. Jennings, J.R.; Coles, M.G.H. Handbook of Cognitive Psychophysiology, Central and Autonomic Nervous System Approaches; Wiley Psychophysiology Handbooks; Wiley: Chichester, UK, 1991; p. 762.
  159. Walker, B.B.; Walker, J.M. Phase relationship between cariotid pressure and ongoing electrocortical activity. Int. J. Psychophysiol. 1983, 1, 65–73.
  160. Sarà, M.; Pistoia, F. Complexity loss in physiological time series of patients in a vegetative state. Nonlinear Dyn. Psychol. Life Sci. 2010, 14, 1–13.
  161. Fingelkurts, A.A.; Fingelkurts, A.A.; Bagnato, S.; Boccagni, C.; Galardi, G. Life or death: Prognostic value of a resting EEG with regards to survival in patients in vegetative and minimally conscious states. PLoS ONE 2011, 6, e25967.
  162. Fingelkurts, A.A.; Fingelkurts, A.A.; Bagnato, S.; Boccagni, C.; Galardi, G. Toward operational architectonics of consciousness: Basic evidence from patients with severe cerebral injuries. Cogn. Process. 2012, 13, 111–131.
  163. Sarà, M.; Sebastiano, F.; Sacco, S.; Pistoia, F.; Onorati, P.; Albertini, G.; Carolei, A. Heart rate nonlinear dynamics in patients with persistent vegetative state: A preliminary report. Brain Inj. 2008, 22, 33–37.
  164. Wittling, W. The right hemisphere and the human stress response. Acta Physiol. Scand. Suppl. 1997, 640, 55–59.
  165. Hewig, J.; Schlotz, W.; Gerhards, F.; Breitenstein, C.; Lürken, A.; Naumann, E. Associations of the cortisol awakening response (CAR) with cortical activation asymmetry during the course of an exam stress period. Psychoneuroendocrinology 2008, 33, 83–91.
  166. Tops, M.; Wijers, A.A.; van Staveren, A.S.; Bruin, K.J.; Den Boer, J.A.; Meijman, T.F.; Korf, J. Acute cortisol administration modulates EEG alpha asymmetry in volunteers: Relevance to depression. Biol. Psychol. 2005, 69, 181–193.
  167. Buss, K.A.; Malmstadt, J.R.; Dolski, I.; Kalin, N.H.; Goldsmith, H.H.; Davidson, R.J. Right frontal brain activity, cortisol, and withdrawal behavior in 6-month-old infants. Behav. Neurosci. 2003, 117, 11–20.
  168. Schutter, D.J.L.G.; Van Honk, J.; Koppeschaar, H.P.F.; Kahn, R.S. Cortisol and reduced interhemispheric coupling between the left prefrontal and the right parietal cortex. J. Neuropsychiatry Clin. Neurosci. 2002, 14, 89–90.
  169. Birmanns, B.; Saphier, D.; Abramsky, O. a-Interferon modifies cortical EEG activity: Dose-dependence and antagonism by naloxone. J. Neurol. Sci. 1990, 100, 22–26.
  170. Saphier, D.; Ovadia, H.; Abramsky, O. Neural responses to antigenic challenges and immunomodulatory factors. Yale J. Biol. Med. 1990, 63, 109–119.
  171. Kang, D.H.; Davidson, R.J.; Coe, C.L.; Wheeler, R.F.; Tomarken, A.J.; Ershler, W. Frontal brain asymmetry and immune function. Behav. Neurosci. 1991, 105, 860–869.
  172. Rosenkranz, M.A.; Jackson, D.C.; Dalton, K.M.; Dolski, I.; Ryff, C.D.; Singer, B.H.; Muller, D.; Kalin, N.H.; Davidson, R.J. Affective style and in vivo immune response: Neurobehavioral mechanisms. Proc. Natl. Acad. Sci. USA 2003, 100, 11148–11152.
  173. Seo, S.-H.; Lee, J.-T. Stress and EEG. In Convergence and Hybrid Information Technologies; Crisan, M., Ed.; INTECH: Rijeka, Croatia, 2010; pp. 413–426.
  174. Vanhollebeke, G.; De Smet, S.; De Raedt, R.; Baeken, C.; van Mierlo, P.; Vanderhasselt, M.A. The neural correlates of psychosocial stress: A systematic review and meta-analysis of spectral analysis EEG studies. Neurobiol. Stress 2022, 18, 100452.
  175. Davidson, R.J.; Coe, C.C.; Dolski, I.; Donzella, B. Individual differences in prefrontal activation asymmetry predict natural killer cell activity at rest and in response to challenge. Brain Behav. Immun. 1999, 13, 93–108.
  176. Hecht, D. Depression and the hyperactive right-hemisphere. Neurosci. Res. 2010, 68, 77–87.
  177. Davis, P.A. Effect on the EEG of changing the blood sugar level. Arch. Neurol. Psychiatry 1943, 49, 186–194.
  178. Sulg, I.A.; Sotaniemi, K.A.; Tolonen, U.; Hokkanen, E. Dependence between cerebral metabolism and blood flow as reflected in the quantitative EEG. Adv. Biol. Psychiatry 1981, 6, 102–108.
  179. Köpruner, V.; Pfurtscheller, G.; Auer, L.M. Quantitative EEG in normals and in patients with cerebral ischemia. Prog. Brain Res. 1984, 62, 29–50.
  180. Knyazeva, M.G.; Vil’davskii, V.U. Correspondence of spectral characteristics of EEG and regional blood circulation in 9-14 years old children. Hum. Physiol. (Physiol. Cheloveka) 1986, 12, 387–394.
  181. Passero, S.; Rocchi, R.; Vatti, G.; Burgalassi, L.; Battistini, N. Quantitative EEG mapping, regional cerebral blood flow and neuropsychological function in Alzheimer’s disease. Dementia 1995, 6, 148–156.
  182. Kraaier, V.; van Huffelen, A.C.; Wieneke, G.H. Changes in quantitative EEG and blood flow velocity due to standardized hyperventilation; a model of transient ischaemia in young human subjects. Electroencephalogr. Clin. Neurophysiol. 1988, 70, 377–387.
  183. Szelies, B.; Mielke, R.; Kessler, J.; Heiss, W.D. EEG power changes are related with regional cerebral glucose metbolism in vascular dementia. Clin. Neurophysiol. 1999, 110, 615–620.
  184. Alper, K.R.; John, E.R.; Brodie, J.; Günther, W.; Daruwala, R.; Prichep, L.S. Correlation of PET and qEEG in normal subjects. Psychiatry Res. 2006, 146, 271–282.
  185. Jann, K.; Koenig, T.; Dierks, T.; Boesch, C.; Federspiel, A. Association of individual resting state EEG alpha frequency and cerebral blood flow. NeuroImage 2010, 51, 365–372.
  186. O’Gorman, R.L.; Poil, S.S.; Brandeis, D.; Klaver, P.; Bollmann, S.; Ghisleni, C.; Lüchinger, R.; Martin, E.; Shankaranarayanan, A.; Alsop, D.C.; et al. Coupling between resting cerebral perfusion and EEG. Brain Topogr. 2013, 26, 442–457.
  187. Babiloni, C.; Del Percio, C.; Caroli, A.; Salvatore, E.; Nicolai, E.; Marzano, N.; Lizio, R.; Cavedo, E.; Landau, S.; Chen, K.; et al. Cortical sources of resting state EEG rhythms are related to brain hypometabolism in subjects with alzheimer’s disease: An EEG-Pet study. Neurobiol. Aging 2016, 48, 122–134.
  188. Cohn, R.; Raines, G. Cerebral vascular lesions: Electroencephalographic and neuropathologic correlations. Arch. Neurol. Psychiatry 1948, 60, 165–181.
  189. Ingvar, D.H.; Sjolund, B.; Ardo, A. Correlation between dominant EEG frequency, cerebral oxygen uptake and blood flow. Electroencephalogr. Clin. Neurophysiol. 1976, 41, 268–276.
  190. Blume, W.T.; Ferguson, G.G.; McNeill, D.K. Significance of EEG changes at carotid endarterectomy. Stroke 1985, 17, 891–897.
  191. Jonkman, E.J.; Poortvliet, D.C.J.; Veering, M.M.; De Weerd, A.W.; John, E.R. The use of neurometrics in the study of patients with cerebral ischemia. Electroencephalogr. Clin. Neurophysiol. 1985, 61, 333–341.
  192. Nagata, K. Topographic EEG in brain ischemia: Correlation with blood flow and metabolism. Brain Topogr. 1988, 1, 97–106.
  193. Nagata, K.; Tagwa, K.; Hiroi, S.; Shishido, F.; Uemura, K. Electroencephalographic correlates of blood flow and oxygen metabolism provided by positron emission tomography in patients with cerebral infarction. Electroencephalogr. Clin. Neurophysiol. 1989, 72, 16–30.
  194. Claassen, J.; Hirsch, L.J.; Kreiter, K.T.; Du, E.Y.; Connolly, S.E.; Emerson, R.G.; Mayer, S.A. Quantitative continuous EEG for detecting delayed cerebral ischemia in patients with poor-grade subarachnoid hemorrhage. Clin. Neurophysiol. 2004, 115, 2699–2710.
  195. Hughes, J.R.; John, E.R. Conventional and quantitative electroencephalography in psychiatry. J. Neuropsychiatry Clin. Neurosci. 1999, 11, 190–208.
  196. Mueller, T.M.; Gollwitzer, S.; Hopfengärtner, R.; Rampp, S.; Lang, J.D.; Stritzelberger, J.; Madžar, D.; Reindl, C.; Sprügel, M.I.; Onugoren, M.D.; et al. Alpha power decrease in quantitative EEG detects development of cerebral infarction after subarachnoid hemorrhage early. Clin. Neurophysiol. 2021, 132, 1283–1289.
  197. Vatinno, A.A.; Simpson, A.; Ramakrishnan, V.; Bonilha, H.S.; Bonilha, L.; Seo, N.J. The prognostic utility of electroencephalography in stroke recovery: A systematic review and meta-analysis. Neurorehabil. Neural Repair 2022, 36, 255–268.
  198. Gollwitzer, S.; Groemer, T.; Rampp, S.; Hagge, M.; Olmes, D.; Huttner, H.B.; Schwab, S.; Madžar, D.; Hopfengaertner, R.; Hamer, H.M. Early prediction of delayed cerebral ischemia in subarachnoid hemorrhage based on quantitative EEG: A prospective study in adults. Clin. Neurophysiol. 2015, 126, 1514–1523.
  199. Rots, M.L.; van Putten, M.J.; Hoedemaekers, C.W.; Horn, J. Continuous EEG monitoring for early detection of delayed cerebral ischemia in subarachnoid hemorrhage: A pilot study. Neurocrit. Care 2016, 24, 207–216.
  200. Balança, B.; Dailler, F.; Boulogne, S.; Ritzenthaler, T.; Gobert, F.; Rheims, S.; Andre-Obadia, N. Diagnostic accuracy of quantitative EEG to detect delayed cerebral ischemia after subarachnoid hemorrhage: A preliminary study. Clin. Neurophysiol. 2018, 129, 1926–1936.
  201. Vakalopoulos, C. The EEG as an index of neuromodulator balance in memory and mental illness. Front. Neurosci. 2014, 8, 63.
  202. Lubar, J.F. Neocortical dynamics: Implication for understanding the role of neurofeedback and related techniques for the enhancement of attention. Appl. Psychophysiol. Biofeedback 1997, 22, 111–126.
  203. Nistico, G.; Nappy, G. Locus coeruleus, an integrative station involved in the control of several vital functions. Funct. Neurol. 1993, 8, 5–25.
  204. Panyushkina, S.V.; Kurova, N.S.; Egorov, S.F.; Koshelev, V.V. Individual EEG reactions of healthy humans to mutually antagonistic noradrenotropic influences. Zh Vyss. Nerv Deyat 1994, 44, 457–469.
  205. Sadato, N.; Nakamura, S.; Oohashi, T. Neural networks for generation and suppression of alpha rhythm: A PET study. NeuroReport 1998, 9, 893–897.
  206. Chavanon, M.-L.; Wacker, J.; Stemmler, G. Paradoxical dopaminergic drug effects in extraversion: Dose- and time-dependent effects of sulpiride on EEG theta activity. Front. Hum. Neurosci. 2013, 7, 117.
  207. Knott, V.J.; Hovson, A.L.; Perugimi, M. The effect of acute tryptophan depletion and fenfluramine on quantitative EEG and mood in healthy male subjects. Biol. Psychiatry 1999, 46, 229–238.
  208. Steriade, M.; Gloor, P.; Llinas, R.R.; da Silva, F.H.L.; Mesulam, M.-M. Basic mechanisms of cerebral rhythmic activities. Report of IFCN Committee on Basic Mechanisms. Electroencepahlogr. Clin. Neurophysiol. 1990, 76, 481–508.
  209. John, E.R.; Prichep, L.S.; Winterer, G.; Herrmann, W.M.; diMichele, F.; Halper, J.; Bolwig, T.G.; Cancro, R. Electrophysiological subtypes of psychotic states. Acta Psychiatr. Scand. 2007, 116, 17–35.
  210. Small, J.G. Psychiatric disorders and EEG. In Electroencephalography: Basic Principles, Clinical Applications, and Related Fields; Niedermeyer, E., da Silva, F.L., Eds.; Williams and Wilkins: Baltimore, MD, USA, 1993; pp. 581–596.
  211. Hughes, J.R. The EEG in psychiatry: An outline with summarized points and references. Clin. Electroencephalogr. 1995, 26, 92–101.
  212. Sam, M.C.; So, E.L. Significance of epileptiform discharges in patients without epilepsy in the community. Epilepsia 2001, 42, 1273–1278.
  213. Zivin, L.; Marsan, C.A. Incidence and prognostic significance of “epileptiform” activity in the EEG of nonepileptic subjects. Brain 1968, 91, 751–777.
  214. Standage, K.F. The etiology of hysterical seizures. Can. Psychiatr. Assoc. J. 1975, 20, 67–73.
  215. Cohen, R.J.; Suter, C. Hysterical seizures: Suggestion as a provocative EEG test. Ann. Neurol. 1982, 11, 391–395.
  216. King, D.W.; Gallagher, B.B.; Murvin, A.J.; Smith, D.B.; Marcus, D.J.; Hartlage, L.C.; Ward, L.C., 3rd. Pseudoseizures: Diagnostic evaluation. Neurology 1982, 32, 18–23.
  217. Luther, J.S.; McNamara, J.O.; Carwile, S.; Miller, P.; Hope, V. Pseudoepileptic seizures: Methods and video analysis to aid diagnosis. Ann. Neurol. 1982, 12, 458–462.
  218. Wilkus, R.J.; Dodrill, C.B.; Thompson, P.M. Intensive EEG monitoring and psychological studies of patients with pseudoepileptic seizures. Epilepsia 1984, 25, 100–107.
  219. Wilkes, R.J.; Thompson, P.M.; Vossler, D.G. Bizarre ictal automatisms: Frontal lobe epileptic or psychogenic seizures. J. Epilepsy 1990, 3, 297–313.
  220. Lelliott, P.T.; Fenwick, P. Cerebral pathology in pseudoseizures. Acta Neurol. Scand. 1991, 83, 29–132.
  221. Bowman, E.S. Etiology and clinical course of pseudoseizures: Relationship to trauma, depression, and dissociation. Psychosomatics 1993, 34, 333–342.
  222. Devinsky, O.; Sanchez-Villasenor, F.; Vazquez, B.; Kothari, M.; Alper, K.; Luciano, D. Clinical profile of patients with epileptic and nonepileptic seizures. Neurology 1996, 46, 1530–1533.
  223. Shelley, B.P.; Trimble, M.R.; Boutros, N.N. Electroencephalographic cerebral dysrhythmic abnormalities in the trinity of nonepileptic general population, neuropsychiatric, and neurobehavioral disorders. J. Neuropsychiatry Clin. Neurosci. 2008, 20, 7–22.
  224. Pillmann, F.; Rohde, A.; Ullrich, S.; Draba, S.; Sannemüller, U.; Marneros, A. Violence, criminal behavior, and the EEG: Significance of left hemispheric focal abnormalities. J. Neuropsychiatry Clin. Neurosci. 1999, 11, 454–457.
  225. Hughes, J.; Leander, R.; Ketchum, G. Electroencephalographic study of specific reading disabilities. EEG. Clin. Neurophysiol. 1949, 1, 377.
  226. Ribas, J.C.; Baptistete, E.; Fonseca, C.A.; Tiba, I.; Filho, H.S.C. Behavior disorders with predominance of aggressiveness, irritability, impulsiveness, and instability: Clinical electroencephalographic study of 100 cases. Arq. De Neuro-Psiquiatr. 1974, 32, 187–194.
  227. Harty, J.E.; Gibbs, E.L.; Gibbs, F.A. Electroencephalographic study of two hundred and seventy-five candidates for military service. War Med. 1942, 2, 923–930.
  228. Socanski, D.; Herigstad, A.; Thomsen, P.H.; Dag, A.; Larsen, T.K. Epileptiform abnormalities in children diagnosed with attention deficit/hyperactivity disorder. Epilepsy Behav. 2010, 19, 483–486.
  229. Dierks, T.; Ihl, R.; Frolich, L.; Maurer, K. Dementia of the Alzheimer type: Effects on the spontaneous EEG described by dipole sources. Psychiatry Res. 1993, 50, 51–162.
  230. Prichep, L.S.; Mas, F.; Hollander, E.; Liebowitz, M.; John, E.R.; Almas, M.; DeCaria, C.M.; Levine, R.H. Quantitative electroencephalographic (QEEG) subtyping of obsessive-compulsive disorder. Psychiatry Res. 1993, 50, 25–32.
  231. Inui, K.; Motomura, E.; Okushima, R.; Kaige, H.; Inoue, K.; Nomura, J. Electroencephalographic findings in patients with DSM-IV mood disorder, schizophrenia, and other psychotic disorders. Biol. Psychiatry 1998, 43, 69–75.
  232. Huang, C.; Wahlund, L.O.; Dierks, T.; Julin, P.; Winblad, B.; Jelic, V. Discrimination of Alzheimer’s disease and mild cognitive impairment by equivalent EEG sources: A cross-sectional and longitudinal study. Clin. Neurophysiol. 2000, 11, 1961–1967.
  233. Monastra, V.J.; Lubar, J.F.; Linden, M. The development of a quantitative electroencephalographic scanning process for attention deficit-hyperactivity disorder: Reliability and validity studies. Neuropsychology 2001, 15, 136–144.
  234. Thatcher, R.W.; North, D.M.; Curtin, R.T.; Walker, R.A.; Biver, C.J.; Gomez, J.F.; Salazar, A.M. An EEG severity index of traumatic brain injury. J. Neuropsychiatry Clin. Neurosci. 2001, 13, 77–87.
  235. Karadag, F.; Oguzhanoglu, N.K.; Kurt, T.; Oguzhanoglu, A.; Atesci, F.; Ozdel, O. Quantitative EEG analysis in obsessive compulsive disorder. Int. J. Neurosci. 2003, 113, 833–847.
  236. Boutros, N.N.; Torello, M.; McGlashan, T.H. Electrophysiological aberrations in borderline personality disorder: State of the evidence. J. Neuropsychiatry Clin. Neurosci. 2003, 15, 145–154.
  237. Rowe, D.L. Biophysical modeling of tonic cortical electrical activity in attention deficit hyperactivity disorder. Int. J. Neurosci. 2005, 115, 1273–1305.
  238. Babiloni, C.; Benussi, L.; Binetti, G.; Cassetta, E.; Dal Forno, G.; Del Percio, C.; Ferreri, F.; Ferri, R.; Frisoni, G.; Ghidoni, R.; et al. Apolipoprotein E and alpha brain rhythms in mild cognitive impairment: A multicentric electroencephalogram study. Ann. Neurol. 2006, 59, 323–334.
  239. Başar, E.; Güntekin, B. A review of brain oscillations in cognitive disorders and the role of neurotransmitters. Brain Res. 2008, 1235, 172–193.
  240. Fingelkurts, A.A.; Fingelkurts, A.A. Alpha rhythm operational architectonics in the continuum of normal and pathological brain states: Current state of research. Int. J. Psychophysiol. 2010, 76, 93–106.
  241. Schultz, E.V.; Baburin, I.N.; Karavaeva, T.A.; Karvasarsky, B.D.; Slezin, V.B. Bioelectric brain activity in patients with neurotic and neurosis-like disorders (according to a spectral analysis). Bekhterev. Rev. Psychiatry Med. Psychol. 2010, 3, 26–31.
  242. Lee, S.M.; Jang, K.-I.; Chae, G.-H. Electroencephalographic correlates of suicidal ideation in the theta band. Clin. EEG Neurosci. 2017, 48, 316–321.
  243. Kanda, P.A.M.; Anghinah, R.; Smidth, M.T.; Silva, J.M. The clinical use of quantitative EEG in cognitive disorders. Dement. Neuropsychol. 2009, 3, 195–203.
  244. John, E.R. The role of quantitative EEG topographic mapping or ‘neurometrics’ in the diagnosis of psychiatric and neurological disorders: The pros. Electroencephalogr. Clin. Neurophysiol. 1989, 73, 2–4.
  245. Abrams, R.; Taylor, M.A. Differential EEG patterns in affective disorder and schizophrenia. Arch. Gen. Psychiatry 1979, 36, 1355–1358.
  246. Giannitrapani, D.; Collins, J. EEG differentiation between Alzheimer’s and non-Alzheimer’s dementias. In The EEG of Mental Activities; Giannitrapani, D., Murri, L., Eds.; Karger: New York, NY, USA, 1988; pp. 26–41.
  247. Goodin, D.S.; Aminoff, M.J. Electrophysiological differences between subtypes of dementia. Brain 1986, 109, 1102–1113.
  248. Ritchlin, C.T.; Chabot, R.J.; Alper, K.; Buyon, J.; Belmont, H.M.; Roubey, R.; Abramson, S.B. Quantitative electroencephalography: A new approach to the diagnosis of cerebral dysfunction in systemic lupus erythematosus. Arth. Rheumat. 1992, 35, 1330–1342.
  249. Sloan, E.P.; Fenton, G.W.; Kennedy, J.S.J.; MacLennan, J.M. Electroencephalography and single photon emission computed tomography in dementia: A comparative study. Psychol. Med. 1995, 25, 631–638.
  250. Kropotov, J.D.; Pąchalska, M.; Mueller, A. New neurotechnologies for the diagnosis and modulation of brain dysfunctions. Health Psychol. Rep. 2014, 2, 73–82.
  251. Kropotov, J.D. Quantitative EEG, Event-Related Potentials and Neurotherapy; Elsevier: Oxford, UK, 2009; p. 531.
  252. Kropotov, J.D.; Müller, A.; Candrian, G.; Valery, P. Neurobiology of ADHD: A New Diagnostic Approach Based on Electrophysiological Endophenotypes; Springer: London, UK, 2013; p. 300.
  253. Fisher, N.K.; Talathi, S.S.; Cadotte, A.; Carney, P.R. Epilepsy detection and monitoring. In Quantitative EEG Analysis Methods and Clinical Applications; Tong, S., Thakor, N.V., Eds.; Artech House: Norwood, MA, USA, 2009; pp. 141–167.
  254. Pardalos, P.M. Seizure warning algorithm based on optimization and nonlinear dynamics. Math. Program 2004, 101, 365–385.
  255. Drislane, F.W. The clinical use of ambulatory EEG. In Atlas of Ambulatory EEG; Chang, B.S., Schachter, S.C., Schomer, D.L., Eds.; Elsevier: Amsterdam, The Netherlands, 2005; pp. 17–25.
  256. Hegerl, U.; Hensch, T. The vigilance regulation model of affective disorders and ADHD. Neurosci. Biobehav. Rev. 2014, 44, 45–57.
  257. Wittekind, D.A.; Spada, J.; Gross, A.; Hensch, T.; Jawinski, P.; Ulke, C.; Sander, C.; Hegerl, U. Early report on brain arousal regulation in manic vs. depressive episodes in bipolar disorder. Bipolar Disord. 2016, 18, 502–510.
  258. Brenner, R.P. EEG and dementia, Chapter 19. In Electroencephalography, Basic Principles, Clinical Applications, and Related Fields, 4th ed.; Niedermeyer, E., da Silva, F.L., Eds.; Williams and Wilkins: Baltimore, MD, USA, 1999; pp. 349–359.
  259. John, E.R.; Prichep, L.; Ahn, H.; Easton, P.; Fridman, J.; Kaye, H. Neurometric evaluation of cognitive dysfunctions and neurological disorders in children. Prog. Neurobiol. 1983, 21, 239–290.
  260. Fingelkurts, A.A.; Fingelkurts, A.A.; Bagnato, S.; Boccagni, C.; Galardi, G. EEG oscillatory states as neuro-phenomenology of consciousness as revealed from patients in vegetative and minimally conscious states. Conscious Cogn. 2012, 21, 149–169.
  261. Fingelkurts, A.A.; Fingelkurts, A.A.; Bagnato, S.; Boccagni, C.; Galardi, G. Dissociation of vegetative and minimally conscious patients based on brain operational architectonics: Factor of etiology. Clin. EEG Neurosci. 2013, 44, 209–220.
  262. Fingelkurts, A.A.; Fingelkurts, A.A.; Bagnato, S.; Boccagni, C.; Galardi, G. Prognostic value of resting-state electroencephalography structure in disentangling vegetative and minimally conscious states: A preliminary study. Neurorehabil. Neural Repair 2013, 27, 345–354.
  263. Fingelkurts, A.A.; Fingelkurts, A.A.; Bagnato, S.; Boccagni, C.; Galardi, G. The value of spontaneous EEG oscillations in distinguishing patients in vegetative and minimally conscious states, chapter 5. In Application of Brain Oscillations in Neuropsychiatric Diseases (Supplements to Clinical Neurophysiology); Basar, E., Basar-Eroglu, C., Ozerdem, A., Rossini, P.M., Yener, G.G., Eds.; Elsevier B.V.: Amsterdam, The Netherlands, 2013; Volume 62, pp. 81–99.
  264. Fingelkurts, A.A.; Fingelkurts, A.A. Brain space and time in mental disorders: Paradigm shift in biological psychiatry. Int. J. Psychiatry Med. 2019, 54, 53–63.
  265. Dittrich, A. The standardized psychometric assessment of altered states of consciousness (ASCs) in humans. Pharmacopsychiatry 1998, 31, 80–84.
  266. Parnas, J.; Møller, P.; Kircher, T.; Thalbitzer, J.; Jansson, L.; Handest, P.; Zahavi, D. EASE: Examination of anomalous self-experience. Psychopathology 2005, 38, 236–258.
  267. Beck, A.T. The evolution of the cognitive model of depression and its neurobiological correlates. Am. J. Psychiatry 2008, 165, 969–977.
  268. Musholt, K. Thinking about Oneself: From Nonconceptual Content to the Concept of a Self; MIT Press: Cambridge, UK, 2015; p. 232.
  269. Northoff, G.; Heinzel, A.; de Greck, M.; Bermpohl, F.; Dobrowolny, H.; Panksepp, J. Self-referential processing in our brain. A meta-analysis of imaging studies on the self. NeuroImage 2006, 31, 440–457.
  270. Northoff, G. Is the self a higher-order or fundamental function of the brain? The ‘basis model of self-specificity’ and its encoding by the brain’s spontaneous activity. Cogn. Neurosci. 2016, 7, 203–222.
  271. Raichle, M.E.; MacLeod, A.M.; Snyder, A.Z.; Powers, W.J.; Gusnard, D.A.; Shulman, G.L. A default mode of brain function. Proc. Natl. Acad. Sci. USA 2001, 98, 676–682.
  272. Gusnard, D.A. Being a self: Considerations from functional imaging. Conscious Cogn. 2005, 14, 679–697.
  273. Schilbach, L.; Eickhoff, S.B.; Rotarska-Jagiela, A.; Fink, G.R.; Vogeley, K. Minds at rest? Social cognition as the default mode of cognizing and its putative relationship to the “default system” of the brain. Conscious Cogn. 2008, 17, 457–467.
  274. Fingelkurts, A.A.; Fingelkurts, A.A. Persistent operational synchrony within brain default-mode network and self-processing operations in healthy subjects. Brain Cogn. 2011, 75, 79–90.
  275. Fingelkurts, A.A.; Fingelkurts, A.A.; Bagnato, S.; Boccagni, C.; Galardi, G. DMN Operational Synchrony Relates to Self-Consciousness: Evidence from Patients in Vegetative and Minimally Conscious States. Open Neuroimag. J. 2012, 6, 55–68.
  276. Laufs, H.; Kleinschmidt, A.; Beyerle, A.; Eger, E.; Salek-Haddadi, A.; Preibisch, C.; Krakow, K. EEG-correlated fMRI of human alpha activity. Neuroimage 2003, 19, 1463–1476.
  277. Mantini, D.; Perrucci, M.G.; Del Gratta, C.; Romani, G.L.; Corbetta, M. Electrophysiological signatures of resting state networks in the human brain. Proc. Natl. Acad. Sci. USA 2007, 104, 13170–13175.
  278. Jann, K.; Dierks, T.; Boesch, C.; Kottlow, M.; Strik, W.; Koenig, T. BOLD correlates of EEG alpha phase-locking and the fMRI default mode network. Neuroimage 2009, 45, 903–916.
  279. Knyazev, G.G.; Slobodskoj-Plusnin, J.Y.; Bocharov, A.V.; Pylkova, L.V. The default mode network and EEG α oscillations: An independent component analysis. Brain Res. 2011, 1402, 67–79.
  280. Knyazev, G.G.; Savostyanov, A.N.; Volf, N.V.; Liou, M.; Bocharov, A.V. EEG correlates of spontaneous self-referential thoughts: A cross-cultural study. Int. J. Psychophysiol. 2012, 86, 173–181.
  281. Fingelkurts, A.A.; Fingelkurts, A.A.; Kallio-Tamminen, T. Long-term meditation training induced changes in the operational synchrony of default mode network modules during a resting state. Cogn. Process. 2016, 17, 27–37.
  282. Fingelkurts, A.A.; Fingelkurts, A.A.; Kallio-Tamminen, T. Trait lasting alteration of the brain default mode network in experienced meditators and the experiential selfhood. Self Identity 2016, 15, 381–393.
  283. Fingelkurts, A.A.; Fingelkurts, A.A.; Kallio-Tamminen, T. Selfhood triumvirate: From phenomenology to brain activity and back again. Conscious Cogn. 2020, 86, 103031.
  284. Gallagher, S. A pattern theory of self. Front. Hum. Neurosci. 2013, 7, 443.
  285. Gallagher, S.; Daly, A. Dynamical relations in the self-pattern. Front. Psychol. 2018, 9, 664.
  286. Fingelkurts, A.A.; Fingelkurts, A.A.; Kallio-Tamminen, T. Self, Me and I in the repertoire of spontaneously occurring altered states of Selfhood: Eight neurophenomenological case study reports. Cogn. Neurodyn. 2022, 16, 255–282.
  287. Fingelkurts, A.A.; Fingelkurts, A.A. Longitudinal dynamics of 3-dimensional components of selfhood after severe traumatic brain injury: A qEEG case study. Clin. EEG Neurosci. 2017, 48, 327–337.
  288. Fingelkurts, A.A.; Fingelkurts, A.A. Three-dimensional components of selfhood in treatment-naive patients with major depressive disorder: A resting-state qEEG imaging study. Neuropsychologia 2017, 99, 30–36.
  289. Fingelkurts, A.A.; Fingelkurts, A.A. Alterations in the three components of selfhood in persons with post-traumatic stress disorder symptoms: A pilot qEEG neuroimaging study. Open Neuroimag. J. 2018, 12, 42–54.
  290. Beck, A.T. Cognitive models of depression. J. Cogn. Psychother. 1987, 1, 5–37.
  291. Damasio, A.R. The Feeling of What Happens: Body and Emotion in the Making of Consciousness; Harcourt Brace: San Diego, CA, USA, 1999; p. 400.
  292. Rimes, K.A.; Watkins, E. The effects of self-focused rumination on global negative self-judgements in depression. Behav. Res. Ther. 2005, 43, 1673–1681.
  293. Northoff, G. Psychopathology and pathophysiology of the self in depression-neuropsychiatric hypothesis. J. Affect. Disord. 2007, 104, 1–14.
  294. Nolen-Hoeksema, S.; Wisco, B.E.; Lyubomirsky, S. Rethinking rumination. Perspect. Psychol. Sci. 2008, 3, 400–424.
  295. Paulus, M.P.; Stein, M.B. Interoception in anxiety and depression. Brain Struct. Funct. 2010, 214, 451–463.
  296. Diagnostic and Statistical Manual of Mental Disorders, 5th ed.; The American Psychiatric Association: Arlington, VA, USA, 2013.
  297. Zepinic, V. Understanding and Treating Complex Trauma; Xlibris: London, UK, 2011.
  298. van der Kolk, B.A. The body keeps the score: Memory and the evolving psychobiology of posttraumatic stress. Harv. Rev. Psychiatry 1994, 1, 253–265.
  299. McNally, R.J. Remembering Trauma; Belknap Press/Harvard University Press: Cambridge, MA, USA, 2003; p. 448.
  300. Ataria, Y. Traumatic memories as black holes: A qualitative-phenomenological approach. Qual. Psychol. 2014, 1, 123–140.
  301. van der Kolk, B.A.; Fisler, R. Dissociation and the Fragmentary Nature of Traumatic Memories: Overview and Exploratory Study. 1995. Available online: http://www.trauma-pages.com/a/vanderk2.php (accessed on 1 May 2022).
  302. Kullberg-Turtiainen, M.; Vuorela, K.; Huttula, L.; Turtiainen, P.; Koskinen, S. Individualized goal directed dance rehabilitation in chronic state of severe traumatic brain injury: A case study. Heliyon 2019, 5, e01184.
  303. Fingelkurts, A.A.; Fingelkurts, A.A.; Bagnato, S.; Boccagni, C.; Galardi, G. The chief role of frontal operational module of the brain default mode network in the potential recovery of consciousness from the vegetative state: A preliminary comparison of three case reports. Open Neuroimag. J. 2016, 10 (Suppl. S1, M4), 41–51.
  304. Laureys, S.; Celesia, G.G.; Cohadon, F.; Lavrijsen, J.; León-Carrión, J.; Sannita, W.G.; Sazbon, L.; Schmutzhard, E.; von Wild, K.R.; Zeman, A.; et al. European Task Force on disorders of consciousness, unresponsive wakefulness syndrome: A new name for the vegetative state or apallic syndrome. BMC Med. 2010, 8, 68.
  305. Peterson, A.; Bayne, T. Post-comatose disorders of consciousness. In The Routledge Handbook of Consciousness; Gennaro, R., Ed.; Routledge: Abingdon, UK, 2018; pp. 351–365.
  306. Jennett, B.; Plum, F. Persistent vegetative state after brain damage. A syndrome in search of a name. Lancet 1972, 1, 734–737.
  307. Giacino, J.T.; Ashwal, S.; Childs, N.; Cranford, R.; Jennett, B.; Katz, D.I.; Kelly, J.P.; Rosenberg, J.H.; Whyte, J.; Zafonte, R.D.; et al. The minimally conscious state: Definition and diagnostic criteria. Neurology 2002, 58, 349–353.
  308. Naccache, L. Minimally conscious state or cortically mediated state? Brain 2018, 141, 949–960.
  309. Bagnato, S.; Boccagni, C.; Sant’Angelo, A.; Fingelkurts, A.A.; Fingelkurts, A.A.; Galardi, G. Emerging from an unresponsive wakefulness syndrome: Brain plasticity has to cross a threshold level. Neurosci. Biobehav. Rev. 2013, 37, 2721–2736.
  310. Porcaro, C.; Nemirovsky, I.E.; Riganello, F.; Mansour, Z.; Cerasa, A.; Tonin, P.; Stojanoski, B.; Soddu, A. Diagnostic developments in differentiating unresponsive wakefulness syndrome and the minimally conscious state. Front. Neurol. 2022, 12, 778951.
  311. Bagnato, S.; Boccagni, C.; Prestandrea, C.; Sant’Angelo, A.; Castiglione, A.; Galardi, G. Prognostic value of standard EEG in traumatic and non-traumatic disorders of consciousness following coma. Clin. Neurophysiol. 2010, 121, 274–280.
  312. Brenner, R.P. The interpretation of the EEG of stupor and coma. Neurologist 2005, 11, 271–284.
  313. Gosseries, O.; Schnakers, C.; Ledoux, D.; Vanhaudenhuyse, A.; Bruno, M.A.; Demertzi, A.; Noirhomme, Q.; Lehembre, R.; Damas, P.; Goldman, S.; et al. Automated EEG entropy measurements in coma, vegetative state/unresponsive wakefulness syndrome and minimally conscious state. Funct. Neurol. 2011, 26, 25–30.
  314. Lehembre, R.; Gosseries, O.; Lugo, Z.; Jedidi, Z.; Chatelle, C.; Sadzot, B.; Laureys, S.; Noirhomme, Q. Electrophysiological investigations of brain function in coma, vegetative and minimally conscious patients. Arch. Ital. Biol. 2012, 150, 122–139.
  315. Fingelkurts, A.A.; Fingelkurts, A.A.; Bagnato, S.; Boccagni, C.; Galardi, G. Long-term (six years) clinical outcome discrimination of patients in the vegetative state could be achieved based on the operational architectonics EEG analysis: A pilot feasibility study. Open Neuroimag. J. 2016, 10 (Suppl. S1, M6), 69–79.
  316. Sarà, M.; Pistoia, F.; Pasqualetti, P.; Sebastiano, F.; Onorati, P.; Rossini, P.M. Functional isolation within the cerebral cortex in the vegetative state: A nonlinear method to predict clinical outcomes. Neurorehabil. Neural Repair 2011, 25, 35–42.
  317. Fingelkurts, A.A.; Fingelkurts, A.A. Operational architectonics methodology for EEG analysis: Theory and results. Neuromethods 2015, 91, 1–59.
  318. Fingelkurts, A.A.; Fingelkurts, A.A.; Neves, C.F.H. Natural world physical, brain operational, and mind phenomenal space–time. Phys. Life Rev. 2010, 7, 195–249.
  319. Cacciola, A.; Naro, A.; Milardi, D.; Bramanti, A.; Malatacca, L.; Spitaleri, M.; Leo, A.; Muscoloni, A.; Cannistraci, C.V.; Bramanti, P.; et al. Functional brain network topology discriminates between patients with minimally conscious state and unresponsive wakefulness syndrome. J. Clin. Med. 2019, 8, 306.
  320. Tononi, G.; Boly, M.; Massimini, M.; Koch, C. Integrated information theory: From consciousness to its physical substrate. Nat. Rev. Neurosci. 2016, 17, 450–461.
  321. Hubbard, O.; Sunde, D.; Goldensohn, E.S. The EEG in centenarians. Electroencephalogr. Clin. Neurophysiol. 1976, 40, 407–417.
  322. Matejcek, M. Some relationships between occipital EEG activity and age. A spectral analytic study. Rev. Electroencephalogr. Neurophysiol. Clin. 1980, 10, 122–130.
  323. Matthis, P.; Scheffner, D.; Benninger, C.; Lipinski, C.; Stolzis, L. Changes in the background activity of the electroencephalogram according to age. Electroencephalogr. Clin. Neurophysiol. 1980, 49, 626–635.
  324. Marciani, M.G.; Maschio, M.; Spanedda, F.; Caltagirone, C.; Gigli, G.; Bernardi, G. Quantitative EEG evaluation in normal elderly subjects during mental processes: Age-related changes. Int. J. Neurosci. 1994, 76, 131–140.
  325. Shigeta, M.; Julin, P.; Almkvist, O.; Basun, H.; Rudberg, U.; Wahlund, L.-O. EEG in successful ageing; a 5-year follow-up study from the eighth to ninth decade of life. Electroencephalogr. Clin. Neurophysiol. 1995, 95, 77–83.
  326. Li, D.; Sun, F.; Jiao, Y. Frontal EEG characters in ageing and the correlativity with some cognitive abilities. Acta Psychol. Sin. 1996, 28, 76–81.
  327. Widagdo, M.; Pierson, J.; Helme, R. Age-related changes in qEEG during cognitive tasks. Int. J. Neurosci. 1998, 95, 63–75.
  328. Van Sweden, B.; Wauquier, A.; Niedermeyer, E. Normal ageing and transient cognitive disorders in the elderly. In Electroencephalography: Basic Principles, Clinical Applications, and Related Fields; Niedermeyer, E., da Silva, F.H.L., Eds.; Williams and Wilkins: Baltimore, MD, USA, 1999; pp. 340–348.
  329. Kikuchi, M.; Wada, Y.; Koshino, Y.; Nanbu, Y.; Hashimoto, T. Effect of normal ageing upon interhemispheric EEG coherence: Analysis during rest and photic stimulation. Clin. Electroencephalogr. 2000, 31, 170–174.
  330. Babiloni, C.; Binetti, G.; Cassarino, A.; Dal Forno, G.; Del Percio, C.; Ferreri, F.; Ferri, R.; Frisoni, G.; Galderisi, S.; Hirata, K.; et al. Sources of cortical rhythms in adults during physiological ageing: A multicentric EEG study. Hum. Brain Mapp. 2006, 27, 162–172.
  331. Boha, R.; Stam, C.J.; Molnár, M. Age-dependent features of EEG-reactivity-spectral, complexity, and network characteristics. Neurosci. Lett. 2010, 479, 79–84.
  332. Peltz, C.B.; Kim, H.L.; Kawas, C.H. Abnormal EEGs in cognitively and physically healthy oldest-old: Findings from the 90þ study. J. Clin. Neurophysiol. 2010, 27, 292–295.
  333. Knyazeva, M.G.; Barzegaran, E.; Vildavski, V.Y.; Demonet, J.-F. Ageing of human alpha rhythm. Neurobiol. Ageing 2018, 69, 261–273.
  334. Markand, O.N. Electroencephalogram in dementia. Am. J. EEG Technol. 1986, 26, 3–17.
  335. Samson-Dollfus, D.; Delapierre, G.; Do Marcolino, C.; Blondeau, C. Normal and pathological changes in alpha rhythms. Int. J. Psychophysiol. 1997, 26, 395–409.
  336. Fernández, A.; Hornero, R.; Mayo, A.; Poza, J.; Gil-Gregorio, P.; Ortiz, T. EEG spectral profile in Alzheimer’s disease and mild cognitive impairment. Clin. Neurophysiol. 2006, 117, 306–314.
  337. Varela, F. Neurophenomenology: A methodological remedy for the hard problem. J. Conscious Stud. 1996, 3, 330–349.
  338. Borjigin, J.; Lee, U.; Liu, T.; Pal, D.; Huff, S.; Klarr, D.; Sloboda, J.; Hernandez, J.; Wang, M.M.; Mashour, G.A. Surge of neurophysiological coherence and connectivity in the dying brain. Proc. Natl. Acad. Sci. USA 2013, 110, 14432–14437.
  339. Vicente, R.; Rizzuto, M.; Sarica, C.; Yamamoto, K.; Sadr, M.; Khajuria, T.; Fatehi, M.; Moien-Afshari, F.; Haw, C.S.; Llinas, R.R.; et al. Enhanced interplay of neuronal coherence and coupling in the dying human brain. Front. Aging Neurosci. 2022, 14, 813531.
  340. Chawla, L.S.; Akst, S.; Junker, C.; Jacobs, B.; Seneff, M.G. Surges of electroencephalogram activity at the time of death: A case series. J. Palliat. Med. 2009, 12, 1095–1100.
  341. Persinger, M.A.; Rouleau, N.; Murugan, N.J.; Tessaro, L.W.E.; Costa, J.N. When is the brain dead? Living-like electrophysiological responses and photon emissions from applications of neurotransmitters in fixed post-mortem human brains. PLoS ONE 2016, 11, e0167231.
  342. Blundon, E.G.; Gallagher, R.E.; Ward, L.M. Electrophysiological evidence of preserved hearing at the end of life. Sci. Rep. 2020, 10, 10336.
  343. Blundon, E.G.; Gallagher, R.E.; Ward, L.M. Resting state network activation and functional connectivity in the dying brain. Clin. Neurophysiol. 2022, 135, 166–178.
  344. Blundon, E.G.; Gallagher, R.E.; Ward, L.M. Electrophysiological evidence of sustained attention to music among conscious participants and unresponsive hospice patients at the end of life. Clin. Neurophysiol. 2022, 139, 9–22.
  345. Fernández-Torre, J.L.; Hernández-Hernández, M.A.; Muñoz-Esteban, C. Non confirmatory electroencephalography in patients meeting clinical criteria for brain death: Scenario and impact on organ donation. Clin. Neurophysiol. 2013, 124, 2362–2367.
  346. Grigg, M.M.; Kelly, M.A.; Celesia, G.G.; Ghobrial, M.W.; Ross, E.R. Electroencephalographic activity after brain death. Arch. Neurol. 1987, 44, 948–954.
  347. Sutter, R.; Stevens, R.D.; Kaplan, P.W. Significance of triphasic waves in patients with acute encephalopathy: A nine-year cohort study. Clin. Neurophysiol. 2013, 124, 1952–1958.
  348. Itil, T.M. Quantitative pharmacoelectroencephalography. In Psychotropic Drugs and the Human EEG: Modern Problems in Pharmachopsychiatry; Itil, T.M., Ed.; Karger: New York, NY, USA, 1974; Volume 8, pp. 43–75.
  349. Herrmann, W.M.; Schaerer, E. Pharmaco-EEG: Computer EEG analysis to describe the projection of drug effects on a functional cerebral level in humans. In Handbook of Electroencephalography and Clinical Neurophysiology; Silva, F.H.L., Leeuwen, W.S., Rémond, A., Eds.; Elsevier: Amsterdam, The Netherlands, 1986; Volume 2, pp. 386–445.
  350. Saletu, B. The use of pharmaco-EEG in drug profiling. In Human Psychopharmacology Measures and Methods; Hindmarch, I., Stonier, P.D., Eds.; John Wiley: New York, NY, USA, 1987; Volume 1, pp. 173–200.
  351. Mandema, J.W.; Danhof, M. Electroencephalogram effect measures and relationships between pharmacokinetics and pharmacodynamics of centrally acting drugs. Clin. Pharmacokinet. 1992, 23, 191–215.
  352. Bruder, G.E.; Stewart, J.W.; Tenke, C.E.; McGrath, P.J.; Leite, P.; Bhattacharya, N.; Quitkin, F.M. Electroencephalographic and perceptual asymmetry differences between responders and nonresponders to an SSRI antidepressant. Biol. Psychiatry 2001, 49, 416–425.
  353. Hermens, D.F.; Cooper, N.J.; Kohn, M.; Clarke, S.; Gordon, E. Predicting stimulant medication response in ADHD: Evidence from an integrated profile of neuropsychological, psychophysiological and clinical factors. J. Integr. Neurosci. 2005, 4, 107–121.
  354. Arns, M.; Gunkelman, J.; Breteler, M.; Spronk, D. EEG phenotypes predict treatment outcome to stimulants in children with ADHD. J. Integr. Neurosci. 2008, 7, 421–438.
  355. Bruder, G.E.; Sedoruk, J.P.; Stewart, J.W.; McGrath, P.J.; Quitkin, F.M.; Tenke, C.E. Electroencephalographic alpha measures predict therapeutic response to a selective serotonin reuptake inhibitor antidepressant: Pre- and post-treatment findings. Biol. Psychiatry 2008, 63, 1171–1177.
  356. Iosifescu, D.V.; Greenwald, S.; Devlin, P.; Perlis, R.H.; Denninger, J.W.; Alpert, J.E.; Fava, M. Pretreatment frontal EEG and changes in suicidal ideation during SSRI treatment in major depressive disorder. Acta Psychiatr. Scand. 2008, 117, 271–276.
  357. Leuchter, A.F.; Cook, I.A.; Gilmer, W.S.; Marangell, L.B.; Burgoyne, K.S.; Howland, R.H.; Trivedi, M.H.; Zisook, S.; Jain, R.; Fava, M.; et al. Effectiveness of a quantitative electroencephalographic biomarker for predicting differential response or remission with escitalopram and bupropion in major depressive disorder. Psychiatry Res. 2009, 169, 132–138.
  358. Leuchter, A.F.; Cook, I.A.; Marangell, L.B.; Gilmer, W.S.; Burgoyne, K.S.; Howland, R.H.; Trivedi, M.H.; Zisook, S.; Jain, R.; McCracken, J.T.; et al. Comparative effectiveness of biomarkers and clinical indicators for predicting outcomes of SSRI treatment in Major Depressive Disorder: Results of the BRITE-MD study. Psychiatry Res. 2009, 169, 124–131.
  359. Iznak, A.F.; Iznak, E.V. EEG predictors of therapeutic responses in psychiatry. Neurosci. Behav. Physiol. 2022, 52, 207–212.
  360. Cook, I.A.; Hunter, A.M.; Korb, A.; Farahbod, H.; Leuchter, A.F. EEG signals in psychiatry: Biomarkers for depression management. In Quantitative EEG Analysis Methods and Clinical Applications; Tong, S., Thakor, N.V., Eds.; Artech House: Norwood, MA, USA, 2009; pp. 289–315.
  361. Prichep, L.S.; John, E.R.; Ferris, S.H.; Rausch, L.; Fang, Z.; Cancro, R.; Torossian, C.; Reisberg, B. Prediction of longitudinal cognitive decline in normal elderly with subjective complaints using electrophysiological imaging. Neurobiol. Aging 2006, 27, 471–481.
  362. Mednick, S.A.; Vka, J.V.; Gabrielli, J.W.F.; Itil, T.M. EEG as a predictor of antisocial behavior. Criminology 1981, 19, 219–230.
  363. Fingelkurts, A.A.; Fingelkurts, A.A.; Neves, C.F.H. The structure of brain electromagnetic field relates to subjective experience: Exogenous magnetic field stimulation study. In Proceedings of the Neuroscience Finland 2013 Meeting: Optogenetics and Brain Stimulation, Helsinki, Finland, 22 March 2013.
  364. Daskalakis, Z.J.; Levinson, A.J.; Fitzgerald, P.B. Repetitive transcranial magnetic stimulation for major depressive disorder: A review. Can. J. Psychiatry 2008, 53, 555–566.
  365. Lam, R.W.; Chan, P.; Wilkins-Ho, M.; Yatham, L.N. Repetitive transcranial magnetic stimulation for treatment-resistant depression: A systematic review and meta-analysis. Can. J. Psychiatry 2008, 53, 621–631.
  366. Walker, J.E.; Kozlowski, G.P. Neurofeedback treatment of epilepsy. Child Adolesc. Psychiatr. Clin. N. Am. 2005, 14, 163–176.
  367. Tan, G.; Thornby, J.; Hammond, D.C.; Strehl, U.; Canady, B.; Arnemann, K.; Kaiser, D.A. Meta-analysis of EEG biofeedback in treating epilepsy. Clin. EEG Neurosci. 2009, 40, 173–179.
  368. Hammond, D.C. Neurofeedback with anxiety and affective disorders. Child Adolesc. Psychiatr. Clin. N. Am. 2005, 14, 105–123.
  369. Surmeli, T.; Ertem, A.; Eralp, E.; Kos, I.H. Schizophrenia and the efficacy of qEEG-guided neurofeedback treatment: A clinical case series. Neurosci. Lett. 2011, 500S, e16.
  370. Dehghani-Arani, F.; Rostami, R.; Nadali, H. Neurofeedback training for opiate addiction: Improvement of mental health and craving. Appl. Psychophysiol. Biofeedback 2013, 38, 133–141.
  371. Arns, M.; De Ridder, S.; Strehl, U.; Breteler, M.; Coenen, A. Efficacy of neurofeedback treatment in ADHD: The effects on inattention, impulsivity and hyperactivity: A meta-analysis. Clin. EEG Neurosci. 2009, 40, 180–189.
  372. Gevensleben, H.; Holl, B.; Albrecht, B.; Schlamp, D.; Kratz, O.; Studer, P.; Wangler, S.; Rothenberger, A.; Moll, G.H.; Heinrich, H. Distinct EEG effects related to neurofeedback training in children with ADHD: A randomized controlled trial. Int. J. Psychophysiol. 2009, 74, 149–157.
  373. Hammer, B.U.; Colbert, A.P.; Brown, K.A.; Ilioi, E.C. Neurofeedback for insomnia: A pilot study of Z-score SMR and individualized protocols. Appl. Psychophysiol. Biofeedback 2011, 36, 251–264.
  374. Kouijzer, M.E.J.; van Schie, H.T.; de Moor, J.M.H.; Gerrits, B.J.L.; Buitelaar, J.K. Neurofeedback treatment in autism. Preliminary findings in behavioral, cognitive, and neurophysiological functioning. Res. Autism Spectr. Disord. 2010, 4, 386–399.
  375. Ibric, V.L.; Dragomirescu, L.G. Neurofeedback in pain management. In Introduction to Quantitative EEG Neurofeedback Advanced Theory and Application, 2nd ed.; Budzynski, T.H., Budzynski, H.K., Evans, J.R., Abarbanel, A., Eds.; Elsevier: New York, NY, USA, 2009; pp. 417–451.
  376. Orlando, P.C.; Rivera, R.O. Neurofeedback for elementary students with identified learning problems. J. Neurother. 2004, 8, 5–19.
  377. Breteler, M.H.; Arns, M.; Peters, S.; Giepmans, I.; Verhoeven, L. Improvements in spelling after QEEG-based neurofeedback in dyslexia: A randomized controlled treatment study. Appl. Psychophysiol. Biofeedback 2010, 35, 5–11.
  378. Hanslmayr, S.; Sauseng, P.; Doppelmayr, M.; Schabus, M.; Klimesch, W. Increasing individual upper alpha power by neurofeedback improves cognitive performance in human subjects. Appl. Psychophysiol. Biofeedback 2005, 30, 1–10.
  379. Angelakis, E.; Stathopoulou, S.; Frymiare, J.L. EEG neurofeedback: A brief overview and an example of peak alpha frequency training for cognitive enhancement in the elderly. Clin. Neuropsychol. 2007, 21, 110–129.
  380. Fingelkurts, A.A.; Fingelkurts, A.A.; Neves, C.F.H. Consciousness as a phenomenon in the operational architectonics of brain organization: Criticality and self-organization considerations. Chaos Solitons Fractals 2013, 55, 13–31.
  381. Thatcher, R.W.; John, E.R. Functional Neuroscience: I. Foundations of Cognitive Processes; Lawrence Erlbaum: Boca Raton, NJ, USA, 1977; p. 370.
  382. Bodunov, M.V. The EEG “alphabet”: The typology of human EEG stationary segments. In Individual and Psychological Differences and Bioelectrical Activity of Human Brain; Rusalov, V.M., Ed.; Nauka: Moscow, Russia, 1988; pp. 56–70. (In Russian)
  383. Jansen, B.H.; Cheng, W.-K. Structural EEG analysis: An explorative study. Int. J. Biomed. Comput. 1988, 23, 221–237.
  384. Fingelkurts, A.A.; Fingelkurts, A.A.; Kaplan, A.Y. The regularities of the discrete nature of multi-variability of EEG spectral patterns. Int. J. Psychophysiol. 2003, 47, 23–41.
  385. Fingelkurts, A.A.; Fingelkurts, A.A.; Krause, C.M.; Kaplan, A.Y. Systematic rules underlying spectral pattern variability: Experimental results and a review of the evidences. Int. J. Neurosci. 2003, 113, 1447–1473.
  386. Fingelkurts, A.A.; Fingelkurts, A.A. Operational Architectonics of the human brain biopotential field: Towards solving the mind-brain problem. BrainMind 2001, 2, 261–296. Available online: http://www.bm-science.com/team/art18.pdf (accessed on 1 May 2022).
  387. Kaplan, A.Y.; Fingelkurts, A.A.; Fingelkurts, A.A.; Borisov, S.V.; Darkhovsky, B.S. Nonstationary nature of the brain activity as revealed by EEG/MEG: Methodological, practical and conceptual challenges. Signal Process. 2005, 85, 2190–2212.
  388. Fingelkurts, A.A.; Fingelkurts, A.A. Brain-mind Operational Architectonics imaging: Technical and methodological aspects. Open Neuroimag. J. 2008, 2, 73–93.
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