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 [5]. 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 [6,7,8]. 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 [9] (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 of the paper, we will mostly refer to qEEGs 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 [10,11,12,13,14,15,16,17]. 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 [18]). Indeed, a large body of evidence [19,20,21,22,23,24,25,26,27] 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 [20]. 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. In the following sections, we present a brief review of these features.

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 [5,22,28]. 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 [29,30,31,32]; 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 [33]) [34] 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) [35,36,37,38]. 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 [17,39,40,41]. Why is this relevant? The qEEG has been found to have a high prognostic value for identifying the functional level of ‘brain maturity’ [42,43]. 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 [44]. 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 [45].

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) [46,47,48]. 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 [49,50,51,52,53,54] on another hand. Further, childhood traumas (including childhood sexual abuse) are also associated with adult qEEG deviations in parallel with cognitive dysfunction [55,56]. 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 [57], 22 [58], or even 27 [59] years post-injury, characterizing persistent post-concussive syndrome.

This brief overview 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.

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 [60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78].

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.

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%) [79,80,81,82,83,84,85,86,87,88,89,90,91,92,93]. 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) [94] (see also [89]).

Smit et al. [92] 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 [95] 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. [96] 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 GABA_A receptor genes in the modulation of qEEG beta oscillations in the human brain: Porjesz et al. [97] found a significant genetic linkage between the beta frequency of the human qEEG and a cluster of GABA_A receptor genes on chromosome 4p. Additionally, this same GABA_A receptor gene was found to be associated with a DSM-IV diagnosis of alcohol dependence [98].

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 GABA_B receptor gene and alpha voltage [99], (b) a serotonin receptor gene (HTR3B), associated with alcoholism and antisocial behavior [100], and (c) a corticotrophin-releasing binding hormone (CRH-BP) [101,102], 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) [103]. Additionally, low voltage alpha has been associated with a subtype of alcohol dependence with anxiety disorders [104,105] and with the brain-derived neurotrophic factor (BDNF) Val66Met polymorphism in depression [106]. High voltage qEEG alpha oscillations are heritable in a simple autosomal dominance manner [79]. 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 [107].

There is evidence [108] 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 [109].

This short overview 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.

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 [7,8,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126]. 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 [127,128].

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 [14,21,22,24,115,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146,147] (for a review, see [18]). This is so because qEEG oscillatory activity is generated by synchronous neural populations that mirror the firing rate of their constituent neurons [148]: 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 [148]. 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 [149,150].

Considering that different qEEG oscillations reflect functionally different components of information processing acting on various temporal scales [140,141], it is possible to map qEEG oscillations onto specific mental and/or behavioral states [151]. qEEG oscillations from the same frequency band may express different functions depending on the conditions they are involved in [152]. 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 [153,154].

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.

Several studies have demonstrated the association between qEEGs and ANS [155,156,157,158]. It seems that the brainstem mediates a functional coupling between the ANS and the central nervous system (CNS) assessed by qEEG [159,160,161]. A theoretical concept of the integration between the ANS and CNS was presented by Jennings and Coles [162]. 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 [5]; see also [163]). Basar [5] 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 [155,156,159,160,161,162]).

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) [164,165,166] and reduced heart rate variability [167] 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.

There is a strong link between qEEG oscillatory patterns and stress regulatory systems: the hypothalamic–pituitary–adrenocortical (HPA) axis and the sympathetic–adrenomedullary axis [168]. For example, qEEGs recorded from stressed students before an exam revealed a correlation between greater right hemisphere (RH) activation and higher cortisol levels [169]. This is supported by the following facts: (a) the administration of cortisol to healthy participants has been shown to increase RH frontal activation [170], and (b) greater right-sided activation (measured by resting qEEG) is associated with higher levels of basal cortisol compared to their left-activated counterparts [171]. 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 [172].

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 [173,174,175,176,177] (for a recent meta-analysis, see [178]). For example, higher levels of right-prefrontal qEEG activation (a) reliably predicted poorer immune response [176] and (b) are characterized by lower levels of natural killer cell activity [179]. 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 [180].

Studies have suggested that different qEEG characteristics are related to cerebral hemodynamics and metabolism [181,182,183,184,185,186,187,188,189,190]. Cerebral cortex metabolism disturbance is associated with and may be responsible for cortical neural synchronization anomalies that may manifest as abnormal qEEG oscillations [191]. Additionally, changes in the characteristics of qEEG oscillations (amplitude, power, frequency) are proportional to cerebrovascular damage (CVD) [119]. The qEEG has been shown to be a reliable marker of the decline in neuronal integrity associated with a decline in blood flow [192,193,194,195,196,197,198]. 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) [199]. Similarly, in patients with subarachnoid hemorrhage, only qEEG could differentiate patients with and without cerebral infarction and not doppler/color-coded duplex sonography [200]. 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 [201] (see also [202,203]). Indeed, qEEG changes precede that of multimodal monitoring or confirmation of infarction on CT [204].

Cerebral hemodynamics and metabolism are regulated by a complex interaction between different homeostatic mechanisms where neurotransmitters play a significant role.

Several studies have suggested a relation between different qEEG oscillations and neuromodulator balance [5,205]. 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 [206]. 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 [207] and an increase in high-frequency qEEG oscillations [208]. 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 [209]. Additionally, posterior vs. anterior distribution of qEEG theta oscillations is informative on DA levels [210]. Low ST levels result in a higher power of low-frequency qEEG components [206]; conversely, high ST levels result in the decreased power of low-frequency qEEG components and the higher power of high-frequency qEEG components [211]. 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) [205]. It seems that for each qEEG oscillatory pattern, there is a correlated neurotransmitter mix [212].

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 [199,205]. 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 [213].

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’ [214,215]; (b) up to 73% of nonepileptic adults have qEEG epileptiform discharges (EDs) [216] that are attributable to underlying brain abnormalities (traumatic, vascular, tumor, metabolic), medications, and psychiatric disorders (see, for example, [217]); (c) the mean prevalence of interictal qEEG abnormalities in psychogenic nonepileptic seizures is estimated to be 26% [218,219,220,221,222,223,224,225,226]; (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% [227]; (e) up to 78% of antisocial and criminal populations have underlying qEEG abnormalities [228] 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 [229], and (g) 69% of youngsters with behavior disorders with a predominance of aggressiveness have EEG deviations [230]. 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%) [231].

Basic mechanisms of cerebral rhythmic activities in norm and pathology are described in detail in Steriade et al. [212]. 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 [56,152,232,233,234,235,236,237,238,239,240,241,242,243,244,245,246] (for reviews, see [199,213,247]).

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 [248]. For example, qEEG utility in discrimination/differentiation between affective disorders and schizophrenia [249], between Alzheimer and non-Alzheimer dementias [199,250], between sub-types of dementia [251], between depression and dementia [199], between schizophrenia and unipolar and bipolar depression [199], and between panic disorder and depression [199] 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) [199,262,263].

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” ([264] (p. 74; see also [3,265]).

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 [266]. On average, the prediction rate is ~81% and has an average warning time of 63 min [267];
• 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)) [268];
• 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 [269,270];
• Between a degenerative disorder such as AD and pseudodementia due to psychiatric illness [271];
• 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) [253];
• Between presence or absence of consciousness in minimally and unresponsive patients [166,272,273,274,275].

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 [276].

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 [152,244].

One aspect that often goes unnoticed by clinicians but is nearly always affected by neuropsychopathology is the 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 [277,278,279]. 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 [280], the currently emerging consensus is that self-referential processing constitutes the core of Selfhood [281,282]. Empirical evidence from neuroscience [281,283,284,285,286] 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) [283,284,285,286,287]. Further, it has been documented that specific qEEG oscillations have a significant positive correlation with the SRN [288,289,290,291,292].

Recently, a three-dimensional neurophysiological model of the complex experiential Selfhood (which is based on the qEEG analysis) was proposed [286,293,294] (for a detailed description, see [295]). 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 [296] and, together, form a unified sense of Selfhood [286] (see also [297]). Each aspect of the triad can be enhanced or weakened depending on the current physiologic and mental state [286,298], voluntary training [293,294], and neuropsychopathology [299,300,301]. Since aspects of Selfhood rarely fall under the purview of clinical practice, we 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’) [300], thus reflecting the well-documented excessive self-focus, increased rumination, and increased embodiment in patients with depression [302,303,304,305,306,307]. 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” [300] (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’ [300].

PTSD is characterized by rather different Self–Me–I dynamics [301]. 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 [308]). We have speculated [301] that such constant hypervigilance coupled with profound emotional arousal leads to sensory overload and further exacerbates alienation of the Self in such patients [309]. 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 [310,311,312]. 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 [312,313].

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 [299]. 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 [299,314].

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 [287]. 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 [315].

This brings us to another reason for the clinical and ethical importance of qEEG utility in the assessment of the neurophysiological and neurophenomenological status of unresponsive patients.

A vegetative state (VS), recently re-termed as ‘unresponsive wakefulness syndrome’ (UWS) [316], and MCS belong to the so-called disorders of consciousness or DoCs [317]. While, by convention, VS/UWS patients are unresponsive to their external and internal environments and are thus unconscious [318], patients in MCS show some level of overt awareness and fluctuating ability to follow commands non-reflexively [319] (see also [320]).

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 [321,322]. 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 [323]. 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 [321].

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 [322], leading to a better understanding of the neural constituents of consciousness’ impairment [166]. 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 [272]. 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 [272,275]. These results confirmed previous observations that loss of consciousness is associated with altered oscillatory contents of the qEEG [324,325,326].

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 [165,275,321,326,327,328]. Indeed, evaluation of qEEG spatial–temporal patterns (which reflect functionally connected neuronal assemblies and their dynamics over time) [166,327,329,330] 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 [166]. 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 [166]. 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 [273]. As a whole, these findings are in keeping with a recent study [331], 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 [332].

Another factor that may complicate diagnosis and affect both healthy and diseased individuals is 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 [63,68,144,333,334,335,336,337,338,339,340,341,342,343,344,345] 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 [112,116,117,119,124,185,187,346,347,348].

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.

Death is often a tragic and somewhat baffling finale of a person’s life. Since the person is unresponsive near or during death, we 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 [349]). 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 [350,351], suggesting that patients near death may generate a replay of memories [352]. 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 [353], (b) that auditory systems (measured by event-related potentials) respond similarly to those of healthy controls just hours before death [354], and (c) the resting-state default mode—task-positive network anticorrelations were present among unresponsive hospice patients [355], 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 [356].

Furthermore, studies have shown that the prevalence of qEEGs with electrocerebral activity despite a clinical diagnosis of brain death (BD) was 3.5% [357] to 19.6% [358], 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 [165,328,359], thus suggesting that the qEEG may have potential prognostic value for evaluating near-term patients’ survival or death.

The above brief review 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 [360,361,362,363]; (b) specific features of the qEEG oscillatory pattern have better predictive power for medication response compared to a syndrome-based diagnosis [364,365,366,367,368,369,370,371]; 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% [372]; (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) [259], (ii) clinical outcomes in patients in the vegetative state 6 years after brain injury [315,327], and (iii) developments of delinquent (antisocial) behavior [373]; (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 [374] (see also [375,376]); (e) normalization of atypical qEEG oscillatory patterns through operant conditioning with neurofeedback results in symptom reduction in neuropsychopathologies such as epilepsy [377,378], depression and anxiety [379], schizophrenia [380], addiction [381], ADHD [382,383], sleep disorders [384], autism [385], chronic pain [386], learning difficulties [387], and dyslexia [388]; last but not least, (f) cognitive enhancement in the elderly by qEEG neurofeedback [389,390].

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 [213].

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 [330,391].

From the above review, 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 [392].

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 [13,393,394,395,396] (for reviews, see [18,329,397,398,399]). 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.