On the application of quantitative EEG for characterizing autistic brain: a systematic review.
Billeci, L., Sicca, F., Maharatna, K., Apicella, F., Narzisi, A., Campatelli, G., et al. (2013). On the application of quantitative EEG for characterizing autistic brain: a systematic review. Frontiers in Human Neuroscience, 7, 1-15.

Autism-Spectrum Disorders (ASD) are thought to be associated with abnormalities in neural connectivity at both the global and local levels. Quantitative electroencephalography (QEEG) is a non-invasive technique that allows a highly precise measurement of brain function and connectivity. This review encompasses the key findings of QEEG application in subjects with ASD, in order to assess the relevance of this approach in characterizing brain function and clustering phenotypes. QEEG studies evaluating both the spontaneous brain activity and brain signals under controlled experimental stimuli were examined. Despite conflicting results, literature analysis suggests that QEEG features are sensitive to modification in neuronal regulation dysfunction which characterize autistic brain. QEEG may therefore help in detecting regions of altered brain function and connectivity abnormalities, in linking behavior with brain activity, and subgrouping affected individuals within the wide heterogeneity of ASD. The use of advanced techniques for the increase of the specificity and of spatial localization could allow finding distinctive patterns of QEEG abnormalities in ASD subjects, paving the way for the development of tailored intervention strategies.

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Quantitative electroencephalographic profiles for children with autistic spectrum disorder.
Chan, A. S., Sze, S. L., and Cheung, M. C. (2007). Quantitative electroencephalographic profiles for children with autistic spectrum disorder. Neuropsychology, 21, 74-81.

The present study examined quantitative electroencephalographic (QEEG) profile for children with autistic spectrum disorder (ASD). Five-minute QEEG data were obtained from 90 normal controls (NCs) and 66 children with ASD. Spectrum analyses revealed that ASD children showed significantly less relative alpha and more relative delta than NC. Specifically, 26% of ASD children and 2% of NCs showed 1.5 SDs of relative alpha below the normative mean. Children with this QEEG profile had 17 times the risk of having ASD than those without such a profile. Sensitivity and specificity of relative alpha were 91% and 73%, respectively. Split-half cross-validation yielded a sensitivity of 76%.

Connectivity-guided neurofeedback for autistic spectrum disorder
Coben, R. (2007). Connectivity-guided neurofeedback for autistic spectrum disorder. Biofeedback, 35, 131-135. Research on autistic spectrum disorder (ASD) has shown related symptoms to be the result of brain dysfunction in multiple brain regions. Functional neuroimaging and electroencephalography research have shown this to be related to abnormal neural connectivity problems. The brains of individuals with ASD show both areas of excessively high connectivity and areas with deficient connectivity. This article reviews emerging evidence that neurofeedback guided by connectivity data can remediate these connectivity anomalies leading to symptom reduction and functional improvement. This evidence raises the hopes for a behavioral, psychophysiological intervention moderating the severity of ASD. Both empirical data and a case example are presented to exemplify this approach.

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The importance of electroencephalogram assessment for autistic disorders.
Coben, R. (2009). The importance of electroencephalogram assessment for autistic disorders. Biofeedback, 37, 71-80. Autistic disorders are a set of complex syndromes that lead to challenges impacting communication, behavior repertoire, and social skills. The etiology of autism is unknown but is likely epigenetic in nature. It is likely associated with an inflammatory process leading to neuroinflammation in early childhood. Autistic disorders include seizures in approximately one-third of the cases and there are often regions of brain dysfunction associated with neural connectivity anomalies. The electroencephalogram (EEG) is presented as a premiere tool to assess these difficulties due to its non-invasive nature, availability and utility in detailing these difficulties. Techniques for seizure detection, monitoring, and tracing their propagation are shown. Similar approaches can then be utilized for assessing EEG oscillations, which are at the heart of these neuronal regulation dysfunctions. Autistic disorders are clearly associated with regions of dysfunction and quantitative electroencephalogram strategies for assessing these impairments are shown. These include techniques for increasing the specificity and spatial resolution of the EEG such as source localization and independent components analysis. Lastly, advanced methods for assessing the neural connectivity problems that underlie the difficulties of these children are presented. EEG assessment, when processed and analyzed with the most advanced techniques, can be invaluable in the evaluation of autistic disorders.

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QEEG-guided neurofeedback: New brain-based individualized evaluation and treatment for autism.
Neubrander, J., Linden, M., Gunkelman, J., and Kerson, C. (2013). QEEG-guided neurofeedback: New brain-based individualized evaluation and treatment for autism. Autism Science Digest: The Journal of Autismone, 3, 91-100. QEEG-guided neurofeedback is based on normalizing dysregulated brain regions that relate to specific clinical presentation. With ASD, this means that the approach is specific to each individual's QEEG subtype patterns and presentation. The goal of neurofeedback with ASD is to correct amplitude abnormalities and balance brain functioning, while coherence neurofeedback aims to improve the connectivity and plasticity between brain regions. This tailored approach has implications that should not be underestimated. . . . Clinicians, including the authors, have had amazing results with ASD, including significant speech and communication improvements, calmer and less aggressive behavior, increased attention, better eye contact, and improved socialization. Many of our patients have been able to reduce or eliminate their medications after completion of QEEG-guided neurofeedback.

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Functional neuroanatomy and the rationale for using EEG biofeedback for clients with Asperger's syndrome
Thompson, L., Thompson, L., and Reid, A. (2010). Functional neuroanatomy and the rationale for using EEG biofeedback for clients with Asperger's syndrome. Applied Psychophysiology and Biofeedback, 35, 39-61.

This paper reviews the symptoms of Asperger's Syndrome (AS), a disorder along the autism continuum, and highlights research findings with an emphasis on brain differences. Existing theories concerning AS are described, including theory of mind (Hill and Frith in Phil Trans Royal Soc Lond, Bull 358:281-289, 2003), mirror neuron system (Ramachandran and Oberman in Sci Am 295(5):62-69, 2006), and Porges' (Ann N Y Acad Sci 1008:31-47, 2003, The neurobiology of autism, Johns Hopkins University Press, Baltimore, 2004) polyvagal theory. (A second paper, Outcomes using EEG Biofeedback Training in Clients with Asperger's Syndrome, summarizes clinical outcomes obtained with more than 150 clients.) Patterns seen with QEEG assessment are then presented. Single channel assessment at the vertex (CZ) reveals patterns similar to those found in Attention-Deficit/Hyperactivity Disorder. Using 19-channel data, significant differences (z-scores > 2) were found in the amplitude of both slow waves (excess theta and/or alpha) and fast waves (beta) at various locations. Differences from the norm were most often found in mirror neuron areas (frontal, temporal and temporal-parietal). There were also differences in coherence patterns, as compared to a normative database (Neuroguide). Low Resolution Electromagnetic Tomography Analysis (Pascual-Marqui et al. in Methods Find Exp Clin Pharmacol 24C:91-95, 2002) suggested the source of the abnormal activity was most often the anterior cingulate. Other areas involved included the amygdala, uncus, insula, hippocampal gyrus, parahippocampal gyrus, fusiform gyrus, and the orbito-frontal and/or ventromedial areas of the prefrontal cortex. Correspondence between symptoms and the functions of the areas found to have abnormalities is evident and those observations are used to develop a rationale for usingEEG biofeedback, called neurofeedback (NFB), intervention. NFB training is targeted to improve symptoms that include difficulty reading and mirroring emotions, poor attention to the outside world, poor self-regulation skills, and anxiety. Porges' polyvagal theory is used to emphasize the need to integrate NFB with biofeedback (BFB), particularly heart rate variability training. We term this emerging understanding the Systems Theory of Neural Synergy. The name underscores the fact that NFB and BFB influence dynamic circuits and emphasizes that, no matter where we enter the nervous system with an intervention, it will seek its own new balance and equilibrium.

Resting state EEG abnormalities in autism spectrum disorders
Wang, J., Barstein, J., Ethridge, L. E., Mosconi, M. W., Takarae, Y., AND Sweeney, J. A. (2013). Restine state EEG abnormalities in autism spectrum disorders. Journal of Neurodevelopmental Disorders, 5, 24. Autism spectrum disorders (ASD) are a group of complex and heterogeneous developmental disorders involving multiple neural system dysfunctions. In an effort to understand neurophysiological substrates, identify etiopathophysiologically distinct subgroups of patients, and track outcomes of novel treatments with translational biomarkers, EEG (electroencephalography) studies offer a promising research strategy in ASD. Resting-state EEG studies of ASD suggest a U-shaped profile of electrophysiological power alterations, with excessive power in low-frequency and high-frequency bands, abnormal functional connectivity, and enhanced power in the left hemisphere of the brain. In this review, we provide a summary of recent findings, discuss limitations in available research that may contribute to inconsistencies in the literature, and offer suggestions for future research in this area for advancing the understanding of ASD.

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Peak alpha frequency: An electroencephalographic measure of cognitive preparedness
Angelakis, E. (2003). Peak alpha frequency: An electroencephalographic measure of cognitive preparedness. Journal of Neurotherapy, 7, 27-29.

OBJECTIVES: Electroencephalographic (EEG) peak alpha frequency (PAF) (measured in Hz) has been correlated to cognitive performance between healthy and clinical individuals, and among healthy individuals. PAF also varies within individuals across developmental stages, among different cognitive tasks, and among physiological states induced by administration of various substances. The present study suggests that, among other things, PAF reflects a trait or state of cognitive preparedness.

METHODS: Experiment 1 involved 19-channel EEG recordings from 10 individuals with traumatic brain injury (TBI) and 12 healthy matched controls, before, during, and after tasks of visual and auditory attention. Experiment 2 involved EEG recordings from 19 healthy young adults before and after a working memory task (WAIS-R Digit Span), repeated on 2 different days to measure within-individual differences. RESULTS: Experiment 1 showed significantly lower PAF in individuals with TBI, mostly during post-task rest. Experiment 2 showed PAF during pre-task baseline to be significantly correlated with Digit Span performance of the same day but not with Digit Span performance of another day. Moreover, PAF was significantly increased after Digit Span for those participants whose PAF was lower than the sample median before the task, but not for those who had it higher. Finally, both PAF and Digit Span performance were increased during the second day.

CONCLUSIONS: PAF was shown to detect both trait and state differences in cognitive preparedness, as well as to be affected by cognitive tasks. Traits are better reflected during post-task rest, whereas states are better reflected during initial resting baseline recordings.

Quantitative EEG abnormalities in a sample of dyslexic persons.
Evans, J. R., and Park, N. (1996). Quantitative EEG abnormalities in a sample of dyslexic persons. Journal of Neurotherapy, 2, 1-5. Definitions of terms such as dyslexia and specific reading disability commonly recognize a basis in central nervous system dysfunction. Past research has related this dysfunction to both structural and neural timing abnormalities. The present study used QEEG findings to provide further evidence for neural timing lcoherence abnormalities in reading disabled persons. Eight children and two adults were diagnosed with specific reading disability based on standard psychoeducational assessment. QEEGik were obtained from each using Laicor Neurosearch 24 equipment, and analyzed using the Thatcher Life Span Reference Data Base. Standard print-outs depicting coherence, phase, amplitude asymmetry, and relative power abnormalities of each subject were inspected, and tallies made of the most frequently occurring significant deviations from the norms. The folikwing abnormalities were found in 70% or more of the subjects: (1). abnormal coherence between one or more combination of sites P3, T5, T3, 01; (2) an equal to or greater than 1.4 11 ratio of left to right side coherence abnormalities; (3) coherence abnormalities between posterior sites more ofien involved decreased rather than increased coherence; (4) at least five abnormalities (or any type) involving site P3; (5) at least three abnormalities (coherence, phase, asymmetry) involving fiontal fpari - etal sites. These data appear to have relevance for neurofeedback. Phase lcoherence (neural timing) training and emphasis on site P3 may be especially useful in some cases of reading disability.

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Different brain activation patterns in dyslexic children: evidence from EEG power and coherence patterns for the double-deficit theory of dyslexia
Arns, M., Peters, S., Breteler, R., and Verhoeven, L. (2007). Different brain activation patterns in dyslexic children: evidence from EEG power and coherence patterns for the double-deficit theory of dyslexia. Journal of Integrative Neuroscience, 6, 175-190.

AIMS: QEEG and neuropsychological tests were used to investigate the underlying neural processes in dyslexia.

METHODS: A group of dyslexic children were compared with a matched control group from the Brain Resource International Database on measures of cognition and brain function (EEG and coherence).

RESULTS: The dyslexic group showed increased slow activity (Delta and Theta) in the frontal and right temporal regions of the brain. Beta-1 was specifically increased at F7. EEG coherence was increased in the frontal, central and temporal regions for all frequency bands. There was a symmetric increase in coherence for the lower frequency bands (Delta and Theta) and a specific right-temporocentral increase in coherence for the higher frequency bands (Alpha and Beta). Significant correlations were observed between subtests such as Rapid Naming Letters, Articulation, Spelling and Phoneme Deletion and EEG coherence profiles.

DISCUSSION: The results support the double-deficit theory of dyslexia and demonstrate that the differences between the dyslexia and control group might reflect compensatory mechanisms. INTEGRATIVE SIGNIFICANCE: These findings point to a potential compensatory mechanism of brain function in dyslexia and helps to separate real dysfunction in dyslexia from acquired compensatory mechanisms.

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EEG alpha and theta oscillations reflect cogitive and memory performance: a review and analysis
Klimesch, W. (1999). EEG alpha and theta oscillations reflect cogitive and memory performance: a review ad analysis. Brain Research Reviews, 29, 169-195.

Evidence is presented that EEG oscillations in the alpha and theta band reflect cognitive and memory performance in particular. Good performance is related to two types of EEG phenomena (i) a tonic increase in alpha but a decrease in theta power, and (ii) a large phasic (event-related) decrease in alpha but increase in theta, depending on the type of memory demands. Because alpha frequency shows large interindividual differences which are related to age and memory performance, this double dissociation between alpha vs. theta and tonic vs. phasic changes can be observed only if fixed frequency bands are abandoned. It is suggested to adjust the frequency windows of alpha and theta for each subject by using individual alpha frequency as an anchor point. Based on this procedure, a consistent interpretation of a variety of findings is made possible. As an example, in a similar way as brain volume does, upper alpha power increases (but theta power decreases) from early childhood to adulthood, whereas the opposite holds true for the late part of the lifespan. Alpha power is lowered and theta power enhanced in subjects with a variety of different neurological disorders. Furthermore, after sustained wakefulness and during the transition from waking to sleeping when the ability to respond to external stimuli ceases, upper alpha power decreases, whereas theta increases. Event-related changes indicate that the extent of upper alpha desynchronization is positively correlated with (semantic) long-term memory performance, whereas theta synchronization is positively correlated with the ability to encode new information. The reviewed findings are interpreted on the basis of brain oscillations. It is suggested that the encoding of new information is reflected by theta oscillations in hippocampo-cortical feedback loops, whereas search and retrieval processes in (semantic) long-term memory are reflected by upper alpha oscillations in thalamo-cortical feedback loops.

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Brain dynamics of mathematical problem solving
Lin, C. L., Jung, M., Wu, Y. C., Lin, C. T., and She, H. C. (2012). Brain dynamics of mathematical problem solving. Conf Proc IEEE Engineering in Medicine and Biology Society, 4768-4771.

The purpose of this study is to examine brain activities of participants solving mental math problems. The research investigated how problem difficulty affected the subjects' responses and electroencephalogram (EEG) in different brain regions. In general, it was found that solution latencies (SL) to the math problems increased with difficulty. The EEG results showed that across subjects, the right-central beta, left-parietal theta, left-occipital theta and alpha, right-parietal alpha and beta, medial-frontal beta and medial central theta power decreased as task difficulty increased. This study further explored the effects of problem-solving performance on the EEG. Slow solvers exhibited greater frontal theta activities in the right hemisphere, whereas an inverse pattern of hemispheric asymmetry was found in fast solvers. Furthermore, analyses of spatio-temporal brain dynamics during problem solving show progressively stronger alpha- and beta-power suppression and theta-power augmentation as subjects were reaching a solution. These findings provide a better understanding of cortical activities mediating math-based problem solving and knowledge acquisition that can ultimately benefit math learning and education.

Subtype analysis of learning disability by quantitative electroencephalography patterns
Thornton, K. (2006). Subtype analysis of learning disability by quantitative electroencephalography patterns. Biofeedback, 34, 106-113. This article addresses the growing prevalence and expense of learning disabilities. The author reviews the various current diagnostic classification systems based on psychoeducational, Diagnostic and Statistical Manual of Mental Disorders (DSM-IV), and other classification schemes. He argues that none of the current classifications clearly dictates precise interventions by diagnostic category. He advocates for a functional classification based on the use of modern neuroscience techniques. He reviews the emerging research using quantitative electroencephalography (EEG), showing clear EEG signatures of several learning disabilities, and advocates for developing a more precise link between classification and therapeutics.

EEG coherence in children with attention-deficit/hyperactivity disorder and comorbid oppositional defiant disorder
Barry, R. J., Clarke, A. R., McCarthy, R., and Selikowitz, M. (2007). EEG coherence in children with attention-deficit/hyperactivity disorder and comorbid oppositional defiant disorder. Clinical Neurophysiology, 118(2), 356-362.

OBJECTIVES: This study is the first to investigate EEG coherence differences between two groups of children with attention-deficit/hyperactivity disorder combined type (AD/HD), with or without comorbid oppositional defiant disorder (ODD), and normal control subjects.

METHODS: Each group consisted of 20 males. All subjects were between the ages of 8 and 12 years, and groups were matched on age. EEG was recorded during an eyes-closed resting condition from 21 monopolar derivations. Wave-shape coherence was calculated for 8 intrahemispheric electrode pairs (4 in each hemisphere), and 8 interhemispheric electrode pairs, within each of the delta, theta, alpha, and beta bands.

RESULTS: Children with comorbid AD/HD and ODD had intrahemispheric coherences at shorter inter-electrode distances significantly reduced from those apparent in children with AD/HD without comorbid ODD. Such reduced coherences in the comorbid group appeared to wash out coherence elevations previously noted in AD/HD studies. CONCLUSIONS: The present results suggest that, rather than suffering an additional deficit, children with AD/HD and comorbid ODD show significantly less CNS impairment than AD/HD patients without comorbid ODD. SIGNIFICANCE: These results have treatment implications, suggesting that behavioural training, perhaps using family-based cognitive behavioural therapy, could be useful for those children with AD/HD and comorbid ODD. This should focus on the ODD symptoms, in association with a medication regime focussed on the AD/HD symptoms.

EEG-defined subtypes of children with attention-deficit/hyperactivity disorder
Clarke, A. R., Barry, R. J., McCarthy, R., and Selikowitz, M. (2001). EEG-defined subtypes of children with attention-deficit/hyperactivity disorder. Clinical Neurophysiology, 112(11), 2098-2105.

OBJECTIVES: This study investigated the presence of EEG clusters within a sample of children with the combined type of attention-deficit/hyperactivity disorder (ADHD).

METHODS: Subjects consisted of 184 boys with ADHD and 40 age-matched controls. EEG was recorded from 21 sites during an eyes-closed resting condition and Fourier transformed to provide estimates for total power, and relative power in the delta, theta, alpha and beta bands, and for the theta/beta ratio. Factor analysis was used to group sites into 3 regions, covering frontal, central and posterior regions. These data were subjected to cluster analysis.

RESULTS: Three distinct EEG clusters of children with ADHD were found. These were characterized by (a) increased slow wave activity and deficiencies of fast wave, (b) increased high amplitude theta with deficiencies of beta activity, and (c) an excess beta group.

CONCLUSIONS: These results indicate that children with ADHD do not constitute a homogenous group in EEG profile terms. This has important implications for studies of the utility of EEG in the diagnosis of ADHD. Efforts aimed at using EEG as a tool to discriminate ADHD children from normals must recognize the variability within the ADHD population if such a tool is to be valid and reliable in clinical practice.

EEG abnormalities in adolescent males with AD/HD.
Hobbs, M. J., Clarke, A. R., Barry, R. J., McCarthy, R., and Selikowitz, M. (2007). EEG abnormalities in adolescent males with AD/HD. Clinical Neurophysiology, 118, 363-371.

OBJECTIVES: This study investigated EEG abnormalities in adolescents with attention-deficit/hyperactivity disorder (AD/HD).

METHODS: Fifteen AD/HD subjects and 15 control subjects participated in this study. All subjects were between 14 and 17 years of age. The EEGwas recorded from 19 electrode sites and was analysed to provide estimates of both absolute and relative power in the delta, theta, alpha and beta bands. Theta/alpha and theta/beta ratio coefficients were also calculated.

RESULTS: Across the scalp, AD/HD subjects were characterised by greater absolute delta and theta activity, and an increased theta/beta ratio compared to controls. No group differences were found for either absolute or relative alpha, or absolute beta. However, AD/HD subjects demonstrated a reduction in relative beta activity in the posterior regions. CONCLUSIONS: The AD/HD group showed significant deviations from normal CNS development, in particular in posterior regions. This supports previous suggestions that individuals with an EEG profile that is not indicative of a maturational lag are more likely to have AD/HD during adolescence.

SIGNIFICANCE: This is the first study to investigate EEG abnormalities in adolescents with AD/HD during an eyes-closed resting condition.

Clinical utility of EEG in attention deficit hyperactivity disorder
4. Loo, S. K., and Barkley, R. A. (2005). Clinical utility of EEG in attention deficit hyperactivity disorder. Applied Neuropsychology, 12(2), 64-76.

Electrophysiological measures were among the first to be used to study brain processes in children with attention deficit hyperactivity disorder (ADHD; Diagnostic and Statistical Manual of Mental Disorders [4th ed.], American Psychiatric Association, 1994) and have been used as such for over 30 years (see Hastings & Barkley, 1978, for an early review). More recently, electroencephalography (EEG) has been used both in research to describe and quantify the underlying neurophysiology of ADHD, but also clinically in the assessment, diagnosis, and treatment of ADHD. This review will first provide a brief overview of EEG and then present some of the research findings of EEG correlates in ADHD. Then, the utility of EEG in making an ADHD diagnosis and predicting stimulant response will be examined. Finally, and more controversially, we will review the results of the most recent studies on EEG biofeedback (neurofeedback) as a treatment for ADHD and the issues that remain to be addressed in the research examining the efficacy this therapeutic approach.

The development of a quantitative electroencephalographic scanning process for attention deficit-hyperactivity disorder: reliability and validity studies.
Monastra, V. J., Lubar, J. F., and Linden, M. (2001). The development of a quantitative electroencephalographic scanning process for attention deficit-hyperactivity disorder: reliability and validity studies. Neuropsychology, 15, 136-144. The development of a quantitative electroencephalographic (QEEG)-based procedure for use in the assessment of attention deficit-hyperactivitydisorder (ADHD) was examined through a series of studies investigating test reliability and validation issues. This process, involving a spectral analysis of the electrophysiological power output from a single, midline, central location (the vertex), was conducted in 469 participants, 6 to 20 years of age, classified as ADHD, inattentive type; ADHD, combined type; or control. The results indicated that the QEEG scanning procedure was reliable (r = .96), was consistent with the Attention Deficit Disorders Evaluation Scale (S. B. McCarney, 1995) and the Test of Variables of Attention(L. M. Greenberg, 1994; chi-square, p < .01), and differentiated participants with ADHD from a nonclinical control group (p < .001). The sensitivity of the QEEG-derived attentional index was 90%; the specificity was 94%.

EEG brain mapping and brain connectivity index for subtypes classification of attention deficit hyperactivity disorder children during the eye-opened period.
Rodrak, S., and Wongsawat, Y. (2013). EEG brain mapping and brain connectivity index for subtypes classification of attention deficit hyperactivity disorder children during the eye-opened period. Conf Proc IEEE Engineering in Medicine and Biology Society, 7400-7403. Attention deficit hyperactivity disorder (ADHD) is one of the most prevalent neurological disorders. It is classified by the DSM-IV into three subtypes, i.e. 1) predominately inattentive type, 2) predominately hyperactive-impulsive type, and (3) combined type. In order to make the treatment via the neurofeedback or the occupational therapy, quantitative evaluations as well as ADHD subtype classification are the important problems to be solved to enhance an alternative way to treat ADHD. Hence, in this paper, we systematically classify all of these three subtypes by the 19-channel EEG data. Three brain mapping (QEEG) techniques, i.e. absolute power of frequency bands, coherence, and phase lag, are employed to visualize each type of the ADHD. ADHD children with combined type have deficit in delta theta and alpha activity. For the inattentive type, there are excessive delta and theta absolute power in the frontal area as well as the excessive coherence in beta and high beta frequency bands. For the hyperactivity and impulsive type, the behavior is dominated by the slow wave. This information will give benefits to the psychiatrist, psychologist, neurofeedback therapist as well as the occupational therapist for quantitatively planning and analyzing the treatment.

The effect of QEEG-guided neurofeedback treatment in decreasing of OCD symptoms.
Barzegary, L., Yaghubi, H., and Rostami, R. (2011). The effect of QEEG-guided neurofeedback treatment in decreasing of OCD symptoms. Procedia - Social and Behavioral Sciences, 30, 2659-2662.

The main purpose of this research is to determine effectiveness of QEEG-Guided Neurofeedback therapy in decreasing OCD symptoms. Twelve patients were selected from Atiyeh institution in Tehran - Iran and they are placed in 3 situations randomly which are neurofeedback, drug therapy and waiting list. Padua Inventory is administered for all patients as pre-test and post - test in 10 weeks. The results of this research using kuruskal - Wallis and Mann-whitney U test were analysed. It's resulted that neurofeedback treatment may be used as a new treatment approach for treating OCD.

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QEEG-Guided neurofeedback in the treatment of obsessive compulsive disorder
Hammond, C. (2003). QEEG-Guided neurofeedback in the treatment of obsessive compulsive disorder. Journal of Neurotherapy, 7, 25-52. Introduction. Blinded, placebo-controlled research (e.g., Sterman, 2000) has documented the ability of brainwave biofeedback to recondition brain wave patterns. Neurofeedback has been used successfully with uncontrolled epilepsy, ADD/ADHD, learning disabilities, anxiety, and head injuries. However, nothing has been published on the treatment of obsessive-compulsive disorder (OCD) with neurofeedback. Method. Quantitative EEGs were gathered on two consecutive OCD patients who sought treatment. This assessment guided protocol selection for subsequent neurofeedback training. Results. Scores on the Yale-Brown Obsessive-Compulsive Scale and the Padua Inventory normalized following treatment. An MMPI was administered pre-post to one patient, and she showed dramatic improvements not only in OCD symptoms, but also in depression, anxiety, somatic symptoms, and in becoming extroverted rather than introverted and withdrawn. Discussion. In follow-ups of the two cases at 15 and 13 months after completion of treatment, both patients were maintaining improvements in OCD symptoms as measured by the Padua Inventory and as externally validated through contacts with family members. Since research has ound that pharmacologic treatment of OCD produces only very modest improvements and behavior therapy utilizing exposure with response prevention is experienced as quite unpleasant and results in treatment dropouts, neurofeedback appears to have potential as a new treatment modality

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Quantitative electoencephalographic subtyping of obsessive-compulsive disorder
Prichep, L. S., Francis, M., Hollander, E., Liebowitz, M., John, E. R., Almas, M., et al. (1993). Quantitative electoencephalographic subtyping of obsessive-compulsive disorder. Psychiatry Research: Neuroimaging, 50, 25-32.

Current neuropsychological, electrophysiological, and other imaging data strongly suggest the existence of a neurobiological basis for obsessive-compulsive disorder (OCD), which was long considered to be exclusively of psychogenic origin. The positive response of some OCD patients to neurosurgery, as well as the efficacy of agents that selectively block serotonin reuptake, lends further support to a biological involvement. However, a survey of the treatment literature reveals that only 45 - 62% of OCD patients improve with these specific medications. In a pilot study using a quantitative electroencephalographic (QEEG) method known as neurometrics, in which QEEG data from OCD patients were compared statistically with those from an age-appropriate normative population, we previously reported the existence of two subtypes of OCD patients within a clinically homogeneous group of patients who met DSM-III-Rcriteria for OCD. Following pharmacological treatment, a clear relationship was found between treatment response and neurometric cluster membership. In this study, we have expanded the OCD population, adding patients from a second site, and have replicated the existence of two clusters of patients in an enlarged, statistically more robust population. Cluster 1 was characterized by excess relative power in theta, especially in the frontal and frontotemporal regions; cluster 2 was characterized by increased relative power in alpha. Further, 80.0% of the members of cluster 1 were found to be nonresponders to drug treatment, while 82.4% of the members of cluster 2 were found to be treatment responders. These findings suggest the existence of at least two pathophysiological subgroups within the OCD population that share a common clinical expression, but show a differential response to treatment with serotonin reuptake inhibitors.

Obsessive compulsive disorder and the efficacy of QEEG-guided neurofeedback treatment: a case series.
Surmeli, T., and Ertem, A. (2011). Obsessive compulsive disorder and the efficacy of QEEG-guided neurofeedback treatment: a case series. Clinical EEG and Neuroscience, 42(3), 195-201.

While neurofeedback (NF) has been extensively studied in the treatment of many disorders, there have been only three published reports, by D.C. Hammond, on its clinical effects in the treatment of obsessive compulsive disorder (OCD). In this paper the efficacy of qEEG-guided NF for subjects with OCD was studied as a case series. The goal was to examine the clinical course of the OCD symptoms and assess the efficacy of qEEG guidedNF training on clinical outcome measures. Thirty-six drug resistant subjects with OCD were assigned to 9-84 sessions of QEEG-guided NFtreatment. Daily sessions lasted 60 minutes where 2 sessions with half-hour applications with a 30 minute rest given between sessions were conducted per day. Thirty-three out of 36 subjects who received NF training showed clinical improvement according to the Yale-Brown obsessive-compulsive scale (Y-BOCS). The Minnesota multiphasic inventory (MMPI) was administered before and after treatment to 17 of the subjects. The MMPI results showed significant improvements not only in OCD measures, but all of the MMPI scores showed a general decrease. Finally, according to the physicians' evaluation of the subjects using the clinical global impression scale (CGI), 33 of the 36 subjects were rated as improved. Thirty-six of the subjects were followed for an average of 26 months after completing the study. According to follow-up interviews conducted with them and/or their family members 19 of the subjects maintained the improvements in their OCD symptoms. This study provides good evidence for the efficacy of NF treatment in OCD. The results of this study encourage further controlled research in this area.

QEEG-Guided neurofeedback for recurrent migraine headaches.
Walker, J. E. (2011). QEEG-Guided neurofeedback for recurrent migraine headaches. Clinical EEG and Neuroscience, 42, 59-61.

Seventy-one patients with recurrent migraine headaches, aged 17-62, from one neurological practice, completed a quantitativeelectroencephalogram (QEEG) procedure. All QEEG results indicated an excess of high-frequency beta activity (21-30 Hz) in 1-4 cortical areas. Forty-six of the 71 patients selected neurofeedback training while the remaining 25 chose to continue on drug therapy. Neurofeedback protocols consisted of reducing 21-30 Hz activity and increasing 10 Hz activity (5 sessions for each affected site). All the patients were classified as migraine without aura. For the neurofeedback group the majority (54%) experienced complete cessation of their migraines, and many others (39%) experienced a reduction in migraine frequency of greater than 50%. Four percent experienced a decrease in headache frequency of < 50%. Only one patient did not experience a reduction in headache frequency. The control group of subjects who chose to continue drug therapy as opposed to neurofeedback experienced no change in headache frequency (68%), a reduction of less than 50% (20%), or a reduction greater than 50% (8%). QEEG-guided neurofeedback appears to be dramatically effective in abolishing or significantly reducing headache frequency in patients with recurrent migraine.

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EEG characteristics of generalized anxiety disorder in childhood.
Demerdzieva, A. (2011). EEG characteristics of generalized anxiety disorder in childhood. Acta Informatica Medica, 19, 9-15. Anxiety is defined as subjective sense of worry, apprehension, fear and distress. When severe, anxiety disorder can affect a child's thinking, decision-making ability, learning and concentration. The aim of this study was to analyze the power spectra and spectrum weighted frequency (brain rate) as an indicator of general mental arousal in anxious patients and to compare the results with healthy preadolescents on the same age and gender. Methodology: The diagnosis was made according to two statistical manuals (DMSIV-R and ICD-10), medical history, neuropsychological assessment and QEEG. Results from spectra power for four conditions (eyes closed, eyes open, VCPT and ACPT) were exported to brain rate software. Results and discussion: Calculating factorial ANOVA we found that there was a strong statistical significance, between results of power spectra for all four bands and brain rate for sagittal and lateral topography between control group of healthy subjects vs. the observed anxious group. Conclusions: The results indicated the presence of decreased theta, alpha and beta activity, especially in central and midline regions.The identification of these characteristics in comparison with the HBI database is very simple and easy and has important implications for mean of QEEG in the assessment of children with anxiety. However, until more research is done, these abnormal QEEG patterns, can not be considered as pathognomonic of anxiety disorder.

Alpha asymmetry in QEEG recordings in young patients with anxiety
Demerdzieva, A., and Pop-Jordanova, N. (2011). Alpha asymmetry in QEEG recordings in young patients with anxiety. Prilozi, 32, 229-244.

Anxiety is defined as a subjective sense of worry, apprehension, fear and distress. When severe, it can affect a child's thinking, decision-making ability, perceptions of the environment, learning and concentration. Basal instability in cortical arousal, as reflected in measures of quantitative electroencephalography (qEEG), is common in most of the anxiety disorders.

SUBJECTS AND METHODS: The sample was composed of 26 children and teenagers aged 11.73 +- 4.03 years, F: M = 1 : 2. The group was diagnosed as having Generalized Anxiety Disorder (GAD). EEG was recorded with Quantitative EEG equipment - Mitsar-amplifier[with 19 electrodes with 250 Hz sampling rate in the 0.3-70 Hz frequency range in the following conditions: eyes opened and eyes closed, at least 5 minutes each.

RESULTS AND CONCLUSIONS: A statistically significant difference of spectra power in alpha band between left and right hemisphere was obtained. The right frontal asym-metry is specific to the right-handed. In eyes-open condition the percentage of children manifesting right asymmetry is up to 92.31% and in the eyes-closed condition it is 88.46%. Left frontal asymmetry was typical of left-handed children. We did not confirm the posterior right asymmetry suggested by other authors. The correlations between asymmetry and hand preference of children was very strong (r = 0.68-0.85) and statistically significant (p < 0.05) only for frontal regions of the brain. For parietal regions this was weak and statistically not significant.

Transcend the DSM using phenotypes.
Gunkelman, J. (2006). Transcend the DSM using phenotypes. Biofeedback, 34, 95-98. Identifying subtypes of specific disorders is an attractive exercise, as it expands our understanding of the individual's response to therapy, but it remains attached to the approach based on the Diagnostic and Statistical Manual of Mental Disorders (DSM), which is rooted in behavior and frequently does not predict therapeutic response by any individual within the DSM grouping. Phenotypes are an intermediate step between genetics and behavior. These proposed electroencephalography (EEG) phenotypes are semistable states of neurophysiological function. The author proposes a framework allowing one to describe much of the observed EEG variance with a small number of phenotypical categories. These groupings cut across the DSM categories, and unlike the DSM, the phenotypes predict the individual's response to therapy, for neurofeedback as well as for medication.

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Clinical database development: characterization of EEG phenotypes.
Johnstone, J., Gunkelman, J., and Lunt, J. (2005). Clinical database development: characterization of EEG phenotypes. Clinical EEG and Neuroscience, 36(2), 99-107. We propose development of evidence-based methods to guide clinical intervention in neurobehavioral syndromes based on categorization of individuals using both behavioral measures and quantification of the EEG (qEEG). Review of a large number of clinical EEG and qEEG studies suggests that it is plausible to identify a limited set of individual profiles that characterize the majority of the population. Statistical analysis has already been used to document "clusters" of qEEG features seen in populations of psychiatric patients. These clusters are considered here as intermediate phenotypes, based on genetics, and are reliable indices of brain function, not isomorphic with DSM categories, and carry implications for therapeutic intervention. We call for statistical analysis methods to be applied to a broad clinical database of individuals diagnosed with neurobehavioral disorders in order to empirically define clusters of individuals who may be responsive to specific neurophysiologically based treatment interventions, namely administration of psychoactive medication and/or EEG neurofeedback. A tentative set of qEEG profiles is proposed based on clinical observation and experience. Implication for intervention with medication and neurofeedback for individuals with these neurophysiological profiles and specific qEEG patterns is presented.

QEEG guided neurofeedback therapy in personality disorders: 13 case studies.
Surmeli, T., and Ertem, A. (2009). QEEG guided neurofeedback therapy in personality disorders: 13 case studies. Clinical EEG and Neuroscience, 40, 5-10. According to DSM-IV, personality disorder constitutes a class only when personality traits are inflexible and maladaptive and cause either significant functional impairment or subjective distress. Classical treatment of choice for personality disorders has been psychotherapy and/or psychopharmacotherapy. Our study is to determine if subjects with antisocial personality disorders will benefit from quantitative EEG (qEEG) guided neurofeedback treatment. Thirteen subjects (9 male, 4 female) ranged in age from 19 to 48 years. All the subjects were free of medications and illicit drugs. We excluded subjects with other mental disorders by clinical assessment. Psychotherapy or psychopharmacotherapy or any other treatment model was not introduced to any of the subjects during or after neurofeedback treatment. For the subject who did not respond to neurofeedback, training was applied with 38 sessions of LORETA neurofeedback training without success. Evaluation measures included qEEG analysis with Nx Link data base, MMPI, T.O.V.A tests and SA-45 questionaries at baseline, and at the end of neurofeedback treatment. Lexicor qEEG signals were sampled at 128 Hz with 30 minutes-neurofeedback sessions completed between 80-120 sessions depending on the case, by Biolex neurofeedback system. At baseline and after every 20 sessions, patients were recorded with webcam during the interview. Twelve out of 13 subjects who received 80-120 sessions of neurofeedback training showed significant improvement based on SA-45 questionaries, MMPI, T.O.V.A. and qEEG/Nx Link data base (Neurometric analysis) results, and interviewing by parent/family members. Neurofeedback can change the view of psychiatrists and psychologists in the future regarding the treatment of personality disorders. This study provides the first evidence for positive effects of neurofeedback treatment in antisocial personality disorders. Further study with controls is warranted

Using QEEG-guided neurofeedback for epilepsy versus standardized protocols: enhanced effectiveness?
Walker, J. E. (2010). Using QEEG-guided neurofeedback for epilepsy versus standardized protocols: enhaned effectiveness? Applied Psychophysiology and Biofeedback, 35, 29-30. This article briefly reviews some of the past EEG treatments of epilepsy and discusses how QEEG may potentially enhance effectiveness of this approach. Several cases are presented in support of this approach.

QEEG-Guided neurofeedback for anger/anger control disorder.
Walker, J. E. (2013). QEEG-Guided neurofeedback for anger/anger control disorder. Journal of Neurotherapy, 17, 88-92. Previous observations suggested that chronic anger may be associated with persistent excessive high-frequency beta activity in one or more cortical areas and that poor anger control may be associated with excessive slowing of the EEG. We hypothesized that downtraining of elevated high-frequency beta activity would reduce anger and that downtraining of excessive cortical slow wave activity would improve anger control. Forty-six individuals underwent neurofeedback training to downtrain excess beta and slow wave activity. This protocol resulted in significantly improved anger control and a reduction in the frequency of outbursts

Spectrum-weighted EEG Frequency ("Brain-Rate") as a Quantitative of Mental Arousal
A concept of brain-rate is introduced, defining it as the weighted mean frequency of the EEG spectrum. In analogue to the blood pressure, heart-rate and temperature, used as standard preliminary indicators of corresponding general bodily activations, it is proposed to use the brain-rate as a preliminary indicator of general mental activation (mental arousal) level. In addition, along with the more specific fewband biofeedback parameters (theta-beta ratio, relative beta ratio, etc.), the brain-rate could be effectively used as a general multiband biofeedback parameter.

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Clinical Advantages of Quantitative Electroencephalogram (QEEG)-Electrical Neuroimaging Application in General Neurology Practice
QEEG-electrical neuroimaging has been underutilized in general neurology practice for uncertain reasons. Recent advances in computer technology have made this electrophysiological testing relatively inexpensive. Therefore, this study was conducted to evaluate the clinical usefulness of QEEG/electrical neuroimaging in neurological practice. Over the period of approximately 6 months, 100 consecutive QEEG recordings were analyzed for potential clinical benefits. The patients who completed QEEG were divided into 5 groups based on their initial clinical presentation. The main groups included patients with seizures, headaches, post-concussion syndrome, cognitive problems, and behavioral dysfunctions. Subsequently, cases were reviewed and a decision was made as to whether QEEG analysis contributed to the diagnosis and/or furthered patient's treatment. Selected and representative cases from each group are presented in more detail, including electrical neuroimaging with additional low-resolution electromagnetic tomography analysis or using computerized cognitive testing. Statistical analysis showed that QEEG analysis contributed to 95% of neurological cases, which indicates great potential for wider application of this modality in general neurology. Many patients also began neurotherapy, depending on the patient's desire to be involved in this treatment modality.

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Assessment of Digital EEG, quantitative EEG, and EEG Brain Mapping
Clinical Database Development: Characterization of EEG Phenotypes