Quantitative EEG (Brain Mapping)

Number: 0221


  1. Aetna considers the use of quantitative EEG (brain mapping), also known as BEAM (Brain Electrical Activity Mapping), medically necessary only as an adjunct to traditional EEG for any of the following:

    1. For ambulatory recording of EEG to facilitate subsequent expert visual EEG interpretation; or
    2. For continuous EEG monitoring by frequency-trending to detect early, acute intracranial complications in the operating room or intensive care unit (ICU); or
    3. For evaluation of certain members with symptoms of cerebrovascular disease whose neuroimaging and routine EEG studies are not conclusive; or
    4. For evaluation of dementia and encephalopathy when the diagnosis remains unresolved after initial clinical evaluation; or
    5. For screening for possible epileptic seizures in high-risk ICU members; or
    6. For screening for possible epileptic spikes or seizures in long-term EEG monitoring; or
    7. For topographic voltage and dipole analysis in pre-surgical evaluations for intractable epilepsy.
  2. In accordance with the American Academy of Neurology/American Clinical Neurophysiology Society's assessment and available evidence, Aetna considers the use of quantitative EEG experimental and investigational for all other indications, including any of the following diagnoses because there is inadequate scientific evidence to prove its clinical usefulness for these indications:

    • Alcoholism
    • Asperger syndrome and other autism spectrum disorders
    • Attention disorders
    • Bipolar disorder
    • Chronic pain (diagnosis and guide to strategies for pain control)
    • Depression
    • Drug abuse
    • Fibromyalgia
    • Hypoxic ischemic encephalopathy
    • Insomnia
    • Learning disability
    • Mild or moderate head injury
    • Minimally conscious state/persistent vegetative state
    • Panic disorder
    • Parkinson's disease
    • Post-concussion syndrome
    • Predicting response to psychotropic medication
    • Prion diseases
    • Schizophrenia
    • Sepsis-associated encephalopathy prognosis
    • Sports concussion (diagnosis and assessment of recovery)
    • Tinnitus.

See also CPB 0480 - Tourette's Syndrome.


Quantitative EEG (qEEG) is a method of analyzing the electrical activity of the brain to derive quantitative patterns that may correspond to diagnostic information and/or cognitive deficits.

Quantitative EEG, a technique for topographic display and analysis of brain electrophysiological data, has been proposed for use in the diagnosis of various psychiatric disorders.  Clinical studies have demonstrated distinctive forms of brain electrical activity in psychiatric conditions including attention deficit disorder, schizophrenia, major depression, and obsessive-compulsive disorder.  However, the clinical significance of these distinctive patterns of brain wave activity is unknown.  Thus the role of quantitative EEG in diagnosis, evaluation of disease progression, and treatment of these conditions has yet to be elucidated.  A report from the American Academy of Neurology and the American Clinical Neurophysiology Society concluded that quantitative EEG remains investigational for clinical use in post-concussion syndrome, mild-to-moderate head injury, learning disability, attention disorders, schizophrenia, depression, alcoholism, and drug abuse.

Clinical studies have demonstrated distinctive forms of brain electrical activity in neurologic and psychiatric conditions including learning disabilities, autism, traumatic brain injury, coma, schizophrenia, major depression, and obsessive-compulsive disorder.  However, the clinical significance of these distinctive patterns of brain wave activity is unknown. Thus the role of quantitative EEG in diagnosis, evaluation of disease progression, and treatment of these conditions has yet to be elucidated. 

Quantitative EEG has been proposed for use in a broad array of potential applications. This evidence has focused on the diagnostic accuracy of QEEG. There is, however, a paucity of evidence regarding its clinical utility.

There are no current guidelines from leading medical professional organizations recommending the use of quantitative EEG as a screening test for neurological and psychiatric conditions. In addition, there are no peer-reviewed published prospective studies of the use of quantitative EEG screening for these conditions showing that management is altered such that clinical outcomes are improved.
There are no published clinical studies demonstrating that use of quantitiative EEG reduces the number of imaging studies or other follow-up tests. In addition, there are no current guidelines from leading medical professional organizations recommending the use of quantitive EEG either as a prerequisite to, or as a replacement for, imaging studies. 
While there is some evidence that electroencephalograph activity differs between normal control subjects and subjects suffering from tinnitus, additional evidence is needed to evaluate the value of including quantitative EEG in a battery of electrophysiological tests for the clinical identification of a predominantly central type of tinnitus.  In addition, there is little evidence to support the use of quantitative EEG to determine the need for change of medications in the treatment of tinnitus.

Some investigators have proposed use of quantitative EEG in psychiatric cases to facilitate selection of medications. However, there is a lack of reliable evidence from prospective studies demonstrating that clinical outcomes are improved by basing selection of psychotropic medications on quantitative EEG results compared to empiric selection. The FDA approved prescribing information for psychotropic medications includes no recommendation for use of quantitative EEG in selection or dosing, and there are no current guidelines from leading medical professional organizations recommending such use of quantitative EEG.

Crumbley and associates (2005) examined the use of quantitative EEG in predicting response to psychotropic medication.  The clinical outcomes of 2 groups of patients were compared:
  1. those with prescribed medication regimens that were concordant with the quantitative EEG predictors, and
  2. those whose medication regimens were discordant with the quantitative EEG predictors. 

Participants included 70 adolescent inpatients who were administered quantitative EEG upon admission.  The results indicated no significant difference in clinical outcome between the two groups.  The failure of this study to find significant differences in patient outcomes questions this particular use of the quantitative EEG (Crumbley et al, 2005).

John and Prichep (2006) noted that as quantitative EEG and pharmaco-EEG have evolved, a vast body of facts has been accumulated, describing changes in the EEG or event-related potentials observed in a variety of brain disorders or after administration of a variety of medications.  With some notable exceptions, these studies have tended to be phenomenological rather than analytical.  There has not been a systematic attempt to integrate these phenomena to provide better understanding of how the abnormal behaviors of a particular psychiatric patient might be related to the specific pattern of the deviant electrical activity, nor just how pharmacological reduction of that deviant activity may have resulted in more normal behavior.

There is insufficient evidence to support the use of quantitative EEG in the diagnosis and/or classification of attention-deficit hyperactivity disorder (ADHD) (Krull, 2009).  Several studies have demonstrated differences in qEEG between groups of children with ADHD and normal children.  However, these studies are limited by non-random assignment, lack of blinding, failure to consider comorbidities, and/or failure to control for pharmacologic therapy.  In addition, the specificity of the findings for ADHD has not been demonstrated.

Snyder and Hall (2006) performed a meta-analysis on the use of quantitative EEG in evaluating patients with ADHD.  The 9 eligible studies (n = 1,498) observed quantitative EEG traits of a theta power increase and a beta power decrease, summarized in the theta/beta ratio with a pooled effect size of 3.08 (95 % confidence interval: 2.90 to 3.26) for ADHD versus controls (normal children, adolescents, and adults).   These investigators concluded that this meta-analysis supports that a theta/beta ratio increase is a commonly observed trait in patients with ADHD relative to normal controls.  Moreover, they noted that since it is known that the theta/beta ratio trait may arise with other conditions, a prospective study covering differential diagnosis would be needed to determine generalizability to clinical applications.  Furthermore, standardization of the quantitative EEG technique is also needed, specifically with control of mental state, drowsiness, and medication.

Although QEEG may prove to be helpful in the diagnosis and/or classification of ADHD in the future, at present, there is insufficient evidence to support its use in clinical populations.

Much of the literature submitted focuses on the use of QEEG in the early detection of dementia. Although several markers of early dementia have been reported in the literature, there is a lack of evidence that early detection of dementia alters clinical management such that outcomes are improved, especially given the lack of robust treatments available.

An assessment by the Swedish Office of Health Technology Assessment (SBU, 2008) found insufficient evidence to support the use of quantitative EEG in dementia.  The SBU assessment stated: "[t]here is limited evidence that either visually rated EEG or qEEG helps the diagnostic workup differentiate AD (Alzheimer’s Disease) patients from controls or AD from other dementia disorders."

Klassen et al (2011) evaluated qEEG measures as predictive biomarkers for the development of dementia in Parkinson disease (PD).  Preliminary work shows that qEEG measures correlate with current PD cognitive state.  A reliable predictive qEEG biomarker for PD dementia (PD-D) incidence would be valuable for studying PD-D, including treatment trials aimed at preventing cognitive decline in PD.  A cohort of subjects with PD in the authors' brain donation program utilizes annual pre-mortem longitudinal movement and cognitive evaluation.  These subjects also undergo biennial EEG recording.  EEG from subjects with PD without dementia with follow-up cognitive evaluation was analyzed for qEEG measures of background rhythm frequency and relative power in δ, α, and β bands.  The relationship between the time to onset of dementia and qEEG and other possible predictors was assessed by using Cox regression.  The hazard of developing dementia was 13 times higher for those with low background rhythm frequency (lower than the grand median of 8.5 Hz) than for those with high background rhythm frequency (p < 0.001).  Hazard ratios (HRs) were also significant for greater than median bandpower (HR = 3.0; p = 0.004) compared to below, and for certain neuropsychological measures.  The HRs for δ, α, and β bandpower as well as baseline demographic and clinical characteristics were not significant.  The authors concluded that qEEG measures of background rhythm frequency and relative power in the band are potential predictive biomarkers for dementia incidence in PD.  These QEEG biomarkers may be useful in complementing neuropsychological testing for studying PD-D incidence.

Marzano and colleagues (2008) stated that in the last 2 decades quantitative EEG analysis has been used to examine the neurophysiological characteristics of insomnia.  These studies provided evidence in support of the hypothesis that primary insomnia is associated with hyper-arousal of central nervous system and altered sleep homeostasis.  However, these researchers have here underlined that these results have intrinsic methodological problems, mainly related to constraints of standard assessment in clinical research.  They have proposed that future studies should be performed on larger samples of drug-free patients, using within-subjects designs and longitudinally recording patients adapted to sleep laboratory.  All these methodological improvements will allow to partial out the contribution of individual differences, pharmacological influences and first-night effects on EEG frequencies.  Moreover, they have discussed the potential relevance of recent findings from basic research concerning local changes during physiological sleep, which could be extended to the study of insomnia.

Hargrove and colleagues (2010) stated that there is increasing acceptance that pain in fibromyalgia (FM) is a result of dysfunctional sensory processing in the spinal cord and brain, and a number of recent imaging studies have demonstrated abnormal central mechanisms.  These researchers compared quantitative electroencephalogram (qEEG) measures in 85 FM patients with age- and gender-matched controls in a normative database.  A statistically significant sample (minimum 60 seconds from each subject) of artifact-free EEG data exhibiting a minimum split-half reliability ratio of 0.95 and test-retest reliability ratio of 0.90 was used as the threshold for acceptable data inclusion.  Electroencephalograms of FM subject were compared to EEGs of age- and gender-matched healthy subjects in the Lifespan Normative Database and analyzed using NeuroGuide 2.0 software.  Analyses were based on spectral absolute power, relative power and coherence.  Clinical evaluations included the Fibromyalgia Impact Questionnaire (FIQ), Beck Depression Inventory and Fischer dolorimetry for pain pressure thresholds.  Based on Z-statistic findings, the EEGs from FM subjects differed from matched controls in the normative database in 3 features:
  1. reduced EEG spectral absolute power in the frontal International 10-20 EEG measurement sites, particularly in the low- to mid-frequency EEG spectral segments;
  2. elevated spectral relative power of high frequency components in frontal/central EEG measurement sites; and
  3. widespread hypo-coherence, particularly in low- to mid-frequency EEG spectral segments, in the frontal EEG measurement sites. 

A consistent and significant negative correlation was found between pain severity and the magnitude of the EEG abnormalities.  No relationship between EEG findings and medicine use was found.  The authors concluded that qEEG analysis reveals significant differences between FM patients compared to age- and gender-matched healthy controls in a normative database, and has the potential to be a clinically useful tool for assessing brain function in FM patients.

Hathi et al (2010) assessed an EEG-based index, the Cerebral Health Index in babies (CHI/b), for identification of neonates with high Sarnat scores and abnormal EEG as markers of hypoxic ischemic encephalopathy (HIE) after perinatal asphyxia.  This was a retrospective study using 30-min EEG data collected from 20 term neonates with HIE and 20 neurologically normal neonates.  The HIE diagnosis was made on clinical grounds based on history and examination findings.  The maximum-modified clinical Sarnat score was used to grade HIE severity within 72 hrs of life.  All neonates underwent 2-channel bedside EEG monitoring.  A trained electroencephalographer blinded to clinical data visually classified each EEG as normal, mild or severely abnormal.  The CHI/b was trained using data from Channel 1 and tested on Channel 2.  The CHI/b distinguished among HIE and controls (p < 0.02) and among the 3 visually interpreted EEG categories (p < 0.0002).  It showed a sensitivity of 82.4 % and specificity of 100 % in detecting high grades of neonatal encephalopathy (Sarnat 2 and 3), with an area under the receiver operator characteristic (ROC) curve of 0.912.  CHI/b also identified differences between normal versus mildly abnormal (p < 0.005), mild versus severely abnormal (p < 0.01) and normal versus severe (p < 0.002) EEG groups.  An ROC curve analysis showed that the optimal ability of CHI/b to discriminate poor outcome was 89.7 % (sensitivity: 87.5 %; specificity: 82.4 %).  The authors concluded that the CHI/b identified neonates with high Sarnat scores and abnormal EEG.  These results support its potential as an objective indicator of neurological injury in infants with HIE.

Lopes et al (2010) examined and compared the brain cortical activity, as indexed by qEEG power, coherence and asymmetry measures, in panic disorder patients during an induced panic attack with a 35 % CO(2) challenge test and also in a resting condition.  A total of 15 subjects with panic disorder were randomly assigned to both 35 % CO(2) mixture and atmospheric compressed air, in a double-blind study design, with EEG being recorded for a 20-min period.  During induced panic attacks, a reduced right-sided frontal orbital asymmetry in the beta band, a decreased occipital frontal intra-hemispheric coherence in the delta band at both right and left sides, a left-sided occipital delta inter-hemispheric asymmetry and an increased relative power in the beta wave at T4 were observed.  These data showed a disturbed frontal cortical processing, pointing to an imbalance of the frontal and occipital sites, common to both hemispheres, and an increased right posterior activity related to the high arousing panic attack condition.  These findings corroborated the neuroanatomical hypothesis of panic disorder.

Velasques et al (2013) examined the relationship between cortical gamma coherence within patients with bipolar disorder and a control group during a pro-saccadic attention task.  These investigators hypothesized that gamma coherence oscillations act as a main neural mechanism underlying information processing which changes in bipolar patients.  A total of 32 subjects (12 healthy controls and 20 bipolar patients) were enrolled in this study.  Participants performed a pro-saccadic attention task while their brain activity pattern was recorded using qEEG (20 channels).  These researchers observed that the maniac group presented lower saccade latency when compared to depression and control groups.  The main finding was a greater gamma coherence for control group in the right hemisphere of both frontal and motor cortices caused by the execution of a pro-saccadic attention task.  The authors concluded that these findings suggested a disrupted connection of the brain's entire functioning of maniac patients and represented a deregulation in cortical inhibitory mechanism.  Thus, these results reinforce the hypothesis that greater gamma coherence in the right and left frontal cortices for the maniac group produces a "noise" during information processing and highlights that gamma coherence might be a biomarker for cognitive dysfunction during the manic state.  The authors stated that these findings need to be confirmed in larger samples and in bipolar patients before start the pharmacological treatment.

An UpToDate review on “Attention deficit hyperactivity disorder in children and adolescents: Clinical features and evaluation” (Krull, 2013) states that “We do not suggest qEEG for the evaluation of children with ADHD.  Although the US Food and Drug Administration has licensed the first EEG test for assessment of children (6 to 17 years) for ADHD, and several studies have demonstrated differences in qEEG between children with ADHD and normal children, the studies were limited by non-random assignment, lack of blinding, failure to consider comorbidities, and/or failure to control for pharmacologic therapy.  In addition, the EEG patterns differ in boys and girls.  A 2013 meta-analysis of nine studies (including 1253 children with ADHD and 517 without ADHD) found significant heterogeneity and concluded that EEG profiles (specifically an increased theta to beta ratio) cannot be used to reliably diagnose ADHD (although they may be helpful for prognosis).  Current evidence is insufficient to support the use of qEEG over clinical evaluation of symptoms and functional impairment for the diagnosis of ADHD”.

Kutcher et al (2013) summarized the evidence for the following technologies/strategies related to diagnosing or managing sports-related concussion: quantitative EEG, functional neuroimaging, head impact sensors, telemedicine and mobile devices.  Databases used were MEDLINE, PubMed, Cochrane Controlled Trials Registers, SportDiscus, EMBASE, Web of Science and ProQuest databases.  Primary search keywords were concussion, sports concussion and mild traumatic brain injury.  The keywords used for secondary, topic specific searches were quantitative electroencephalography, qEEG, functional MRI, magnetoencephalography, near-infrared spectroscopy, positron emission tomography, single photon emission CT, accelerometer, impact sensor, telemetry, remote monitoring, robotic medicine, telemedicine, mobile device, mobile phone, smart phone and tablet computer.  The primary search produced 8,567 publications.  The secondary searches produced 9 publications that presented original data, included a comparison group in the study design and involved sports-related concussion: 4 studies spoke to the potential of qEEG as a diagnostic or management tool, while 5 studies addressed the potential of fMRI to be used in the same capacity.  The authors concluded that emerging technologies and novel approaches that aid in sports concussion diagnosis and management are being introduced at a rapid rate.  Moreover, they stated that while some technologies show promise, their clinical utility remains to be established.

Furthermore, the American Medical Society for Sports Medicine’s position statement on “Concussion in sport” (Harmon et al, 2013) did not mention the use of quantitative EEG/brain mapping as a management tool.

Hosokawa et al (2014) noted that several studies have reported the presence of EEG abnormalities or altered evoked potentials (EPs) during sepsis.  However, the role of these tests in the diagnosis and prognostic assessment of sepsis-associated encephalopathy remains unclear.  These researchers performed a systematic search for studies evaluating EEG and/or EPs in adult patients with sepsis-associated encephalopathy.  The following outcomes were extracted:
  1. incidence of EEG/EP abnormalities;
  2. diagnosis of sepsis-associated delirium or encephalopathy with EEG/EP; and
  3. outcome. 

Among 1,976 citations, 17 articles met the inclusion criteria.  The incidence of EEG abnormalities during sepsis ranged from 12 % to 100 % for background abnormality and 6 % to 12 % for presence of tri-phasic waves.  Two studies found that epileptiform discharges and electrographic seizures were more common in critically ill patients with than without sepsis.  In 1 study, EEG background abnormalities were related to the presence and the severity of encephalopathy.  Background slowing or suppression and the presence of tri-phasic waves were also associated with higher mortality.  A few studies demonstrated that quantitative EEG analysis and EP could show significant differences in patients with sepsis compared to controls; but their association with encephalopathy and outcome was not evaluated.  The authors concluded that abnormalities in EEG and EPs are present in the majority of septic patients.  They stated that there is some evidence to support EEG use in the detection and prognostication of sepsis-associated encephalopathy, but further clinical investigation is needed to confirm this suggestion.

Minimally Conscious State/Persistent Vegetative State

In a systematic review and meta-analysis, Bender et al (2015) examined the sensitivity and specificity of new diagnostic methods for the minimally conscious state (MCS).  These researchers identified and evaluated 20 clinical studies involving a total of 906 patients with either persistent vegetative state (PVS) or MCS.  The reported sensitivities and specificities of the various techniques used to diagnose MCS vary widely.  The sensitivity and specificity of functional MRI-based techniques were 44 % and 67 %, respectively (with corresponding 95 % confidence intervals [CI]: 19 % to 72 % and 55 % to 77 %); those of quantitative EEG were 90 % and 80 %, respectively (95 % CI: 69 % to 97% and 66 % to 90 %);  EEG, event-related potentials, and imaging studies could also aid in prognostication.  Contrary to prior assumptions, 10 % to 24 % of patients in PVS could regain consciousness, sometimes years after the event, but only with marked functional impairment.  The authors concluded that the basic diagnostic evaluation for differentiating PVS from MCS consists of a standardized clinical examination.  They stated that in the future, modern diagnostic techniques may help identify patients who are in a subclinical MCS.

Furthermore, an UpToDate review on “Hypoxic-ischemic brain injury: Evaluation and prognosis” (Weinhouse and Young, 2016) states that “The clinical value of the electroencephalogram (EEG) is unclear in the assessment of prognosis of anoxic brain injury because investigators have used different classification systems and variable intervals of recordings after resuscitation.  Furthermore, the EEG is susceptible to subjective interpretation, the effects of sedative drugs, metabolic disturbances, and sepsis, which can invalidate the results”.

Attention Deficit Hyperactivity Disorder (ADHD)

The American Academy of Neurology (AAN)‘s practice advisory on “The utility of EEG theta/beta power ratio in ADHD diagnosis” (Gloss et al, 2016) evaluated the evidence for EEG theta/beta power ratio for diagnosing, or helping to diagnose ADHD.  The authors identified relevant studies and classified them using AAN criteria.  Two Class I studies assessing the ability of EEG theta/beta power ratio and EEG frontal beta power to identify patients with ADHD correctly identified 166 of 185 participants.  Both studies evaluated theta/beta power ratio and frontal beta power in suspected ADHD or in syndromes typically included in an ADHD differential diagnosis.  A bivariate model combining the diagnostic studies showed that the combination of EEG frontal beta power and theta/beta power ratio has relatively high sensitivity and specificity but is insufficiently accurate.  The authors concluded that it is unknown whether a combination of standard clinical examination and EEG theta/beta power ratio increases diagnostic certainty of ADHD compared with clinical examination alone.  The AAN provided the following recommendations:

Clinicians should inform patients with suspected ADHD and their families that the combination of EEG theta/beta power ratio and frontal beta power should not replace a standard clinical evaluation.  There is a risk for significant harm to patients from ADHD misdiagnosis because of the unacceptably high false-positive diagnostic rate of EEG theta/beta power ratio and frontal beta power.  Level B (Probably effective, ineffective or harmful (or probably useful/predictive or not useful/predictive) for the given condition in the specified population)

Clinicians should inform patients with suspected ADHD and their families that the EEG theta/beta power ratio should not be used to confirm an ADHD diagnosis or to support further testing after a clinical evaluation, unless such diagnostic assessments occur in a research setting.  Level R (Level R recommendations are ones that “the guideline authors assert should be applied only in research settings)

Chronic Pain

Pinheiro and colleagues (2016) reviewed recent findings on EEG patterns in individuals with chronic pain.  These researchers also discussed recent advances in the use of qEEG for the assessment of pathophysiology and biopsychosocial factors involved in its maintenance over time.  Data collection took place from February 2014 to July 2015 in PubMed, SciELO and PEDro databases.  Data from cross-sectional studies and longitudinal studies, as well as clinical trials involving chronic pain participants were incorporated into the final analysis.  Primary findings related to chronic pain were an increase of theta and alpha EEG power at rest, and a decrease in the amplitude of evoked potentials after sensory stimulation and cognitive tasks.  The authors concluded that increased alpha and theta power at spontaneous EEG and low amplitudes of ERP during various stimuli appeared to be clinical characteristics of individuals with chronic pain; qEEG can be a simple and objective tool for studying the mechanisms involved in chronic pain, identifying specific characteristics of chronic pain conditions and providing insights about appropriate therapeutic approaches.  Nevertheless, more studies are needed before drawing any conclusion on the utility of qEEG on chronic pain.  Further clinical studies should be conducted to establish the clinical applicability of this instrument as an effective marker for diagnosis and guide to strategies for pain control.  Systematic reviews with samples of individuals who have similar characteristics and type of pain can help determine a specific EEG pattern for each type of chronic pain.

The drawbacks of this study were:
  1. data from the included studies were very heterogeneous, which prevented a meta-analysis.  The conclusions were based on a qualitative analysis of the studies.  Future studies should try to include similar variables, whenever possible, to allow for greater comparability of findings, and
  2. the exclusion of EEG sleep studies. 

These researchers attempted to homogenize the sample, understanding that the awake standard EEG can be quite different from the sleep EEG.  However, these findings may, in the future, be compared to findings of studies with sleeping participants in order to acquire a more comprehensive understanding of the chronic pain phenomenon.  The authors noted that since they did not aim to analyze or discuss the clinical significance of EEG as a tool to detect changes after interventions, their findings and conclusions came from observational studies.  Clinical trials are considered the gold standard to provide the highest level of clinical evidence.  However, these researchers’ questions are better addressed by the observational design.  To control for quality of the evidence presented here, the articles included were assessed by criteria defined by an adapted version of the Newcastle-Ottawa scale.  In general, data acquisition, processing and analysis were clearly stated in these studies, which allow reproducibility of their methods.

Prion Diseases

Franko and colleagues (2016) stated that prion diseases are universally fatal and often rapidly progressive neurodegenerative diseases; and EEG has long been used in the diagnosis of sporadic Creutzfeldt-Jakob disease (sCJD).  However, the characteristic waveforms do not occur in all types of prion diseases.  These researchers re-evaluated the utility of EEG by focusing on the development of biomarkers.  They examined if abnormal qEEG parameters can be used to measure disease progression in prion diseases or predict disease onset in healthy individuals at risk of disease.  In the National Prion Monitoring Cohort study, these investigators performed qEEG on 301 occasions in 29 healthy controls and 67 patients with prion disease.  Patients had either inherited prion disease or sCJD.  These researchers computed the main background frequency, the α and θ power and the α/θ power ratio, then averaged these within 5 electrode groups.  These measurements were then compared among participant groups and correlated with functional and cognitive scores cross-sectionally and longitudinally.  The authors found lower main background frequency, α power and α/θ power ratio and higher θ power in patients compared to control participants.  The main background frequency, the power in the α band and the α/θ power ratio also differed in a consistent way among the patient groups.  Moreover, the main background frequency and the α/θ power ratio correlated significantly with functional and cognitive scores.  Longitudinally, change in these parameters also showed significant correlation with the change in clinical and cognitive scores.  The authors concluded that these findings supported the use of qEEG to follow the progression of prion disease, with potential to help evaluate the treatment effects in future clinical-trials.  Priorities for future work should include the use of these technologies in a clinical trial setting as an exploratory biomarker, the continued study of healthy at-risk individuals and consideration of related technologies such as magnetoencephalography.

This study had 2 major drawbacks:
  1. studies of a rare disease were limited by sample size in addition to relatively small number of sCJD patients, and
  2. no differences were observed between qEEG parameters in asymptomatic gene mutation carriers compared with healthy controls. 

Two interpretations were plausible

  1. the EEG became abnormal several years before clinical onset, reflecting incipient neurodegeneration, but there were too few patients close to actual clinical onset in the asymptomatic inherited prion disease (aIPD) group to detect this, and
  2. the EEG only became abnormal in IPD at clinical onset. 

The authors stated that continued follow-up of aIPD patients and retrospective analysis of converting clinical cases may be helpful.

Also, an UpToDate review on “Diseases of the central nervous system caused by prions” (Brown and Lee, 2016) does not mention qEEG as a diagnostic tool.


Wang and colleagues (2017) examined the aberrant EEG oscillation in major depressive subjects with basal ganglia stroke with lesions in different hemispheres.  Resting EEG of 16 electrodes in 58 stroke subjects, 26 of whom had post-stroke depression (13 with left-hemisphere lesion and 13 with right) and 32 of whom did not (18 with left lesion and 14 with right), was recorded to obtain spectral power analysis for several frequency bands.  Multiple analysis of variance and receiver operating characteristic (ROC) curves were used to identify differences between post-stroke depression (PSD) and post-stroke non-depression (PSND), treating the different lesion hemispheres separately.  Moreover, Pearson linear correlation analysis was conducted to test the severity of depressive symptoms and EEG indices.  PSD with left-hemisphere lesion showed increased beta2 power in frontal and central areas, but PSD with right-hemisphere lesion showed increased theta and alpha power mainly in occipital and temporal regions.  Additionally, for left-hemisphere lesions, beta2 power in central and right parietal regions provided high discrimination between PSD and PSND, and for right-hemisphere lesions, theta power was similarly discriminative in most regions, especially temporal regions.  These researchers also explored the association between symptoms of depression and the power of abnormal bands, but found no such relationship.  The authors concluded that the aberrant EEG oscillation in subjects with PSD differed between subjects with lesions of the left and right hemispheres, suggesting a complex association between depression and lesion location in stroke patients.  The main drawbacks of this study were its relatively small sample size (n = 58) and the inclusion of participants with different lesions of the basal ganglia.

Table: CPT Codes / HCPCS Codes / ICD-10 Codes
Code Code Description

Information in the [brackets] below has been added for clarification purposes.   Codes requiring a 7th character are represented by "+":

CPT codes covered if selection criteria are met:

95961 Functional cortical and subcortical mapping by stimulation and/or recording of electrodes on brain surface, or of depth electrodes, to provoke seizures or identify vital brain structures; initial hour of attendance by a physician or other qualified health care professional.
+ 95962     each additional hour of attendance by a physician or other qualified health care professional (List separately in addition to code for primary procedure)

Other CPT codes related to the CPB:

95812 - 95830 Electroencephalography

HCPCS code covered if selection criteria are met:

S8040 Topographic brain mapping

ICD-10 codes covered if selection criteria are met (not all-inclusive):

F02.80 Dementia in other diseases classified elsewhere, without behavioral disturbance
F02.81 Dementia in other diseases classified elsewhere, with behavioral disturbance
F03.90 - F03.91 Unspecified dementia
F06.1, F06.8 Psychotic disorders with hallucinations and other specified mental disorders due to known physiological conditions
G40.00 - G40.919 Epilepsy and recurrent seizures
G92 Toxic encephalopathy
G93.1 Anoxic brain damage, not elsewhere classified
G93.40 - G93.49 Encephalopathy, not elsewhere classified [not covered for assessing prognosis of sepsis-associated encephalopathy]
G97.31 - G97.32 Intraoperative hemorrhage and hematoma of a nervous system organ or structure complicating a procedure
I65.01 - I69.998 Occlusion and stenosis of precerebral arteries, occlusion of cerebral arteries, transient cerebral ischemia, acute, but ill-defined cerebrovascular disease, other and ill-defined cerebrovascular disease, and late effects of cerebrovascular disease
I97.810 - I97.821 Other intraoperative and postprocedural cerebrovascular infarction during surgery
R40.3 Persistent vegetative state
R56.1 Post traumatic seizures
R56.9 Unspecified convulsions
T56.0x1+ Toxic effect of lead and its compounds, accidental (unintentional)

ICD-10 codes not covered for indications listed in the CPB:

A81.00 - A81.9 Atypical virus infections of central nervous system [Prion diseases]
F07.81 Postconcussional syndrome
F10.121 Alcohol abuse with intoxication delirium
F10.14 Alcohol abuse with alcohol-induced mood disorder
F10.150 - F10.159 Alcohol abuse with alcohol-induced psychotic disorder
F10.180 - F10.19 Alcohol abuse with other alcohol-induced disorders
F10.221 Alcohol dependence with intoxication delirium
F10.230 - F10.24 Alcohol dependence with withdrawal and alcohol-induced mood disorder
F10.250 - F10.29 Alcohol dependence with alcohol-induced psychotic, persisting amnestic, persisting dementia and other alcohol-induced disorders
F10.920 - F10.99 Alcohol use, unspecified, with intoxication, alcohol-induced mood, psychotic, persisting amnestic, persisting dementia and other and unspecified alcohol-induced disorders
F11.10 - F19.999 Drug induced mental disorders
F20.0 - F20.9 Schizophrenia
F25.0 - F25.9 Schizoaffective disorders
F30.10 - F39 Mood [affective] disorders
F34.1 Dysthymic disorder
F41.0 Panic disorder [episodic paroxysmal anxiety]
F51.01 Primary insomnia
F51.02 Adjustment insomnia
F51.03 Paradoxical insomnia
F51.09 Other insomnia not due to a substance or known physiological condition
F80.0 - F89 Pervasive and specific developmental disorders
F90.0 - F90.9 Attention-deficit hyperactivity disorders
G20 - G21.9 Parkinson's disease
G47.00 Insomnia, unspecified
G89.21 - G89.29 Chronic pain not elsewhere classified
H93.11 - H93.9 Tinnitus
H93.A1 - H93.A9 Pulsatile tinnitus
M79.7 Fibromyalgia
P91.60 Hypoxic-ischemic encephalopathy (HIE), unspecified
S06.0x0+ - S06.9x9+ Intracranial injury [excluding those with skull fracture]
S09.10x+ - S09.11x+
S09.19x+, S09.8xx+ - S09.90x+
Head injury, unspecified
R48.0 Dyslexia and alexia
W21.00+ - W21.9 Striking against or struck by sports equipment
Z73.810 - Z73.819 Behavioral insomnia of childhood

The above policy is based on the following references:

  1. American Academy of Neurology.  Assessment: EEG brain mapping.  Report of the American Academy of Neurology, Therapeutics and Technology Assessment Subcommittee.  Neurology. 1989;39(8):1100-1101.  
  2. American Psychiatric Association.  Quantitative electroencephalography: A report on the present state of computerized EEG techniques.  American Psychiatric Association Task Force on Quantitative Electrophysiological Assessment.  Am J Psychiatry. 1991;148(7):961-964.  
  3. Kahn EM. Imaging of brain electrophysiologic activity: Applications in psychiatry. Gen Hosp Psychiatry. 1992;14(2):99-106. 
  4. Stam CJ, Jelles B, Achtereekte HA, et al. Diagnostic usefulness of linear and nonlinear quantitative EEG analysis in Alzheimer's disease. Clin Electroencephalography. 1996;27(2):69-77.  
  5. Kuperman S, Johnson B, Arndt S, et al. Quantitative EEG differences in a nonclinical sample of children with ADHD and undifferentiated ADD. J Am Acad Child Adolesc Psychiatry. 1996;35(8):1009-1017.  
  6. Nuwer M. Assessment of digital EEG, quantitative EEG, and EEG brain mapping: Report of the American Academy of Neurology and the American Clinical Neurophysiology Society. Neurology. 1997;49(1):277-292.  
  7. Hoffman DA, Lubar JF, Thatcher RW, et al. Limitations of the American Academy of Neurology and American Clinical Neurophysiology Society paper on QEEG. J Neuropsychiatry Clin Neurosci. 1999;11(3):401-407.  
  8. Small JG, Milstein V, Malloy FW, et al. Clinical and quantitative EEG studies of mania. J Affect Disord. 1999;53(3):217-224.  
  9. Hughes JR, John ER. Conventional and quantitative electroencephalography in psychiatry. J Neuropsychiatry Clin Neurosci. 1999;11(2):190-208.  
  10. Claus JJ, Kwa VI, Teunisse S, et al. Slowing on quantitative spectral EEG is a marker for rate of subsequent cognitive and functional decline in early Alzheimer disease. Alzheimer Dis Assoc Disord. 1998;12(3):167-174.  
  11. Mai R, Facchetti D, Micheli A, et al. Quantitative electroencephalography in amyotrophic lateral sclerosis. Electroencephalogr Clin Neurophysiol. 1998;106(4):383-386.  
  12. Claus JJ, Ongerboer de Visser BW, Walstra GJ, et al. Quantitative spectral electroencephalography in predicting survival in patients with early Alzheimer disease. Arch Neurol. 1998;55(8):1105-1111.  
  13. Drake ME, Padamadan H, Newell SA. Interictal quantitative EEG in epilepsy. Seizure. 1998;7(1):39-42.  
  14. Ebersole JS. New applications of EEG/MEG in epilepsy evaluation. Epilepsy Res Suppl. 1996;11:227-237.  
  15. Jacobs MP, Fischbach GD, Davis MR, et al. Future directions for epilepsy research. Neurology. 2001;57(9):1536-1542.  
  16. Procaccio F, Polo A, Lanteri P, et al. Electrophysiologic monitoring in neurointensive care. Curr Opin Crit Care. 2001;7(2):74-80.  
  17. Wallace BE, Wagner AK, Wagner EP, et al. A history and review of quantitative electroencephalography in traumatic brain injury. J Head Trauma Rehabil. 2001;16(2):165-190. 
  18. Barry RJ, Clarke AR, Johnstone SJ.  A review of electrophysiology in attention-deficit/hyperactivity disorder: I. Qualitative and quantitative electroencephalography. Clin Neurophysiol. 2003;114(2):171-183.
  19. Weiler EW, Brill K, Tachiki KH, Wiegand R. Electroencephalography correlates in tinnitus. Int Tinnitus J. 2000;6(1):21-24.
  20. Shulman A, Goldstein B. Quantitative electroencephalography: Preliminary report--tinnitus. Int Tinnitus J. 2002;8(2):77-86.
  21. Chabot RJ, di Michele F, Prichep L. The role of quantitative electroencephalography in child and adolescent psychiatric disorders. Child Adolesc Psychiatr Clin N Am. 2005;14(1):21-53, v-vi.
  22. Nuwer MR, Hovda DA, Schrader LM, Vespa PM. Routine and quantitative EEG in mild traumatic brain injury. Clin Neurophysiol. 2005;116(9):2001-2025.
  23. Crumbley JA, DeFilippis NA, Dsurney J, Sacco A. The neurometric-quantitative electroencephalogram as a predictor for psychopharmacological treatment: An investigation of clinical utility. J Clin Exp Neuropsychol. 2005;27(6):769-778.
  24. John ER, Prichep LS. The relevance of QEEG to the evaluation of behavioral disorders and pharmacological interventions. Clin EEG Neurosci. 2006;37(2):135-143.
  25. Snyder SM, Hall JR. A meta-analysis of quantitative EEG power associated with attention-deficit hyperactivity disorder. J Clin Neurophysiol. 2006;23(5):440-455.
  26. Bares M, Brunovsky M, Kopecek M, et al. Changes in QEEG prefrontal cordance as a predictor of response to antidepressants in patients with treatment resistant depressive disorder: A pilot study. J Psychiatr Res. 2007;41(3-4):319-325.
  27. Purins A, Hiller J. Quantitative EEG for predicting patient response to antidepressants. Australia and New Zealand Horizon Scanning Network. Prioritising Summary. Volume 17. Canberra, ACT: Australian Government; August 2007.
  28. Pichon Riviere A, Augustovski F, Cernadas C, et al. Cerebral mapping [summary]. IRR No. 100. Buenos Aires, Argentina: Institute for Clinical Effectiveness and Health Policy (IECS); 2007.
  29. Marzano C, Ferrara M, Sforza E, De Gennaro L. Quantitative electroencephalogram (EEG) in insomnia: A new window on pathophysiological mechanisms. Curr Pharm Des. 2008;14(32):3446-3455.
  30. Galderisi S, Mucci A, Volpe U, Boutros N. Evidence-based medicine and electrophysiology in schizophrenia. Clin EEG Neurosci. 2009;40(2):62-77.
  31. Hargrove JB, Bennett RM, Simons DG, et al. Quantitative electroencephalographic abnormalities in fibromyalgia patients. Clin EEG Neurosci. 2010;41(3):132-139.
  32. Hathi M, Sherman DL, Inder T, et al. Quantitative EEG in babies at risk for hypoxic ischemic encephalopathy after perinatal asphyxia. J Perinatol. 2010;30(2):122-126.
  33. Lopes FL, Oliveira MM, Freire RC, et al. Carbon dioxide-induced panic attacks and quantitative electroencephalogram in panic disorder patients. World J Biol Psychiatry. 2010;11(2 Pt 2):357-363.
  34. Klassen BT, Hentz JG, Shill HA, et al. Quantitative EEG as a predictive biomarker for Parkinson disease dementia. Neurology. 2011;77(2):118-124.
  35. Tye C, McLoughlin G, Kuntsi J, Asherson P. Electrophysiological markers of genetic risk for attention deficit hyperactivity disorder. Expert Rev Mol Med. 2011;13:e9.
  36. Velasques B, Bittencourt J, Diniz C, et al. Changes in saccadic eye movement (SEM) and quantitative EEG parameter in bipolar patients. J Affect Disord. 2013;145(3):378-385.
  37. Krull KR. Attention deficit hyperactivity disorder in children and adolescents: Clinical features and evaluation. Last reviewed December 2013. UpToDate Inc., Waltham, MA.
  38. Harmon KG, Drezner JA, Gammons M, et al. American Medical Society for Sports Medicine position statement: Concussion in sport. Br J Sports Med. 2013;47(1):15-26.
  39. Kutcher JS, McCrory P, Davis G, et al. What evidence exists for new strategies or technologies in the diagnosis of sports concussion and assessment of recovery? Br J Sports Med. 2013;47(5):299-303.
  40. Hosokawa K, Gaspard N, Su F, et al. Clinical neurophysiological assessment of sepsis-associated brain dysfunction: A systematic review. Crit Care. 2014;18(6):674.
  41. Bender A, Jox RJ, Grill E, et al. Persistent vegetative state and minimally conscious state: A systematic review and meta-analysis of diagnostic procedures. Dtsch Arztebl Int. 2015;112(14):235-242.
  42. Weinhouse GB, Young GL. Hypoxic-ischemic brain injury: Evaluation and prognosis. UpToDate [online serial]. Waltham, MA: UpToDate; reviewed January 2016.
  43. Gloss D, Varma JK, Pringsheim T, Nuwer MR. Practice advisory: The utility of EEG theta/beta power ratio in ADHD diagnosis: Report of the Guideline Development, Dissemination, and Implementation Subcommittee of the American Academy of Neurology. Neurology. 2016;87(22):2375-2379.
  44. Pinheiro ES, de Queirós FC, Montoya P, et al. Electroencephalographic patterns in chronic pain: A systematic review of the literature. PLoS One. 2016;11(2):e0149085.
  45. Franko E, Wehner T, Joly O, et al. Quantitative EEG parameters correlate with the progression of human prion diseases. J Neurol Neurosurg Psychiatry. 2016;87(10):1061-1067.
  46. Brown HG, Lee JM. Diseases of the central nervous system caused by prions. UpToDate [online serial]. Waltham, MA: UpToDate; reviewed December 2016.
  47. Wang C, Chen Y, Zhang Y, et al. Quantitative EEG abnormalities in major depressive disorder with basal ganglia stroke with lesions in different hemispheres. J Affect Disord. 2017;215:172-178.