March 11, 2016
Correlating Oxidative Stress and qEEG
Richard Soutar, Ph.D., BCN,
James Hopson, OMD
Robert Longo, MRC, BCN
New Mind Center, Roswell, GA

Fifty Eight subjects were assessed using measures of qEEG and Oxidative Stress. Subjects were identified as either fast oxidizers or slow oxidizers using hair analysis and then assigned to categories of either fast wave dominant maps or slow wave dominant maps based on a reviewer shared rating system. Fast wave maps were identified as qEEGs dominated by statistically significant levels of beta frequencies and slow wave maps were identified as qEEGs dominated by delta and or theta frequencies. Chi square analysis indicated that fast wave maps tended to be associated with fast oxidizers and slow wave maps tended to be identified with slow oxidizers.

Keywords: qEEG, Oxidative Stress, Neurofeedback

The American Academy of Pediatrics has recognized Toxic Stress resulting in oxidative stress and chronic inflammation as a leading cause of adult health problems (Shonkoff & Garner, 2012). Neuroinflammatory processes and excitotoxicity leading to neuronal dysregulation and necrosis are often either directly or indirectly a consequence of toxic stress (Sapolsky, 1999). These processes typically have an enduring impact on electrophysiology that can be identified in the EEG and qEEG (Niedermeyer & Lopes da Silva, 2005; Knyazev, 2012) in the form of significantly elevated delta frequencies in addition to other standard measures of neural functioning (Dietzel et al, 2012). Inflammation from other sources such as TBI (Scicutella, 2007) as well as pathogens and environmental toxins (Filley, 2001), have increasingly become recognized as a major health problem also responsible for neuroinflammatory cycles.

Inflammatory processes can arise from subconcussive events resulting in temporary breeches in the brain blood barrier that allow for the entrance of opportunistic spyroketes, bacteria and industrial toxins (Wang & Michaelis, 2010). They can also result from excitotoxicity arising from extreme or chronic emotional trauma associated with dysfunctional family systems and maladaptive behavior patterns arising from inadequate socialization in those family systems (Shonkoff & Garner, 2011). The consequences of emotional trauma and associated anxiety appear in the form of elevated fast wave activity and beta asymmetries (Heller, 1997; Engels 2007). Since these patterns of oxidative stress and resulting inflammatory activity appear to have correlating EEG signatures, it is reasonable to speculate that these signatures can be identified more clearly and related to qEEG patterns.

Oxidative Stress is a process in which the allosatatic balance or pro-oxidant and antioxidants becomes disturbed by an overabundance of free radicals (Mancinelli et al, 2011). Under pathological conditions the overproduction of peroxides and free radicals can begin to surpass the body’s detoxification capacity. What follows is damage to lipids, proteins, and DNA as well as cellular signaling. In the extreme this can result in neuronal necrosis and apoptosis.

A wide variety of phenomena can trigger this response including radiation, ozone, nitrous oxide, heavy metals, pesticides, alcohol, a wide variety of food additives and many chemical compounds contaminating food and water (Mancinelli et al, 2011). Although these are primary external environmental contaminants, there are psycho-social factors that can precipitate an oxidative stress process through the medium of excitotoxicity (Harvey, 2006). Toxic Stress also precipitates excitotoxicity (Savic, 2013). Excitotoxicity is a common feature of extreme states of stress as found in severe emotional abuse and PTSD (Sherin & Nemeroff, 2013).

Neurons have unusually high energy requirement, especially when producing high frequency brainwaves (Alkhadi, 2013). These high frequencies are especially overabundant in individuals with anxiety for prolonged periods of time (Heller et al, 1997). When cortical neuronal columns are overactive they produce excessive amounts of cellular glutamate as a byproduct of cell metabolism. Microglia that typically convert this to glutamine for recycling within the presynaptic neuron become overburdened resulting in a buildup of toxic extracellular glutamate in the interstitial tissue (Wang & Michaelis, 2010). This glutamate binds to NMDA receptors and allows the entry of Ca2 into the postsynaptic neuron that in turn results in necrotic cell death or apoptosis. It also binds to non-NMDA receptors allowing entry of excessive Na into postsynaptic neurons resulting in cytotoxic edema.

The excessive Ca2 in particular overwhelms mitrochondria that attempt to absorb the excess which in turn degrades mitrochondrial DNA. The loss of ATP output from compromised mitochondria leads to reduced efficiency in sodium pumps required for production of high frequency electrical activity associated with EEG (Belanger, 2011; Alkhadi, 2013). It leads to leakage or disruption of axonal membranes and the nodes of Ranvier and thereby reducing conduction velocities (Moritini et al, 2005). The excitotoxic model describes a process of high intrinsic oxidative stress leading to mitochondrial dysfunction and low ATP production resulting in a chronic inflammatory response and deficient DNA repair. The outcome is neuronal degradation at all levels and often neuronal death (Wang & Michaelis, 2010).

In addition, astrocytes play a critical role in gating capillary process and determining what molecules are allow to penetrate the blood-brain barrier (Enciu et al, 2013). When these astrocytes are compromised by inflammation secondary to oxidative stress and excitoxic activity, this barrier becomes degraded allowing the infiltration of toxins and pathogens normally excluded. This further compounds the inflammatory process and further degrades neuronal functions.

Thus environmental factors, TBI, pathogens, and toxic stress can all result either directly or indirectly in oxidative stress leading to chronic inflammatory processes that degrade neuronal function.
The body also has stereotypical responses to these same challenges. This has been documented through decades of research in a variety of fields (Shonkoff & Garner, 2011). It is well known that stress increases adrenal activity, the output of epinephrine and elevates cortisol levels. This in turn also upregulates the hypothalamic-pituitary axis (HPA) altering endocrine functions. Elevated cortisol acts upon the hippocampus to reduce ST memory function and suppress immune function activity. Body PH shifts in a more acidic direction as the enteric microbiome alters its bacterial species distribution. Chronic stress can then result in intestinal inflammation and weaken T joints in the intestinal lining leading to the migration of foreign proteins into the interstitial matrix and blood stream. This in turn precipitates an overresponse of cytokines resulting inflammatory activity frequently ending in an autoimmune response. Liver function typically becomes burdened as toxin levels rise and hypothyroid conditions develop. This in turn reduces thyroid T4-T3 conversion and deficits of thyroid T3 transport to neurons further degrades processing speed and sodium pump activity resulting in diffuse elevated slowed alpha (Dietzel, 2012). This shift in EEG alpha frequencies is then another EEG marker of the stages of stress.

Hans Selye (1982) published a model of stress, The General Adaptation Syndrome, that has been well supported in the research and still endures to the present. This model consisted of an Alarm stage, a Resistance Stage and an Exhaustion Stage. Based on the foregoing research and observations it would be reasonable to expect the following physiological pattern to attend escalating stress on the organism. The alarm stage would result in excitotoxic activity in the brain. Prolonged excitotoxic activity would be accompanied by growing inflammation. Chronic inflammation would increase over time and slowly degrade physiological function. As physiological function declines, energy production capacity decreases and high frequency loss occurs with increasing slowing in the EEG.

Since oxidative stress is the underlying physiological process of this model, it stands to reason that progressive stages of one would be reflected in the other. Since electrical activity (Freeman, 2009) and blood perfusion (Rosa et al, 2009) are reflected in EEG activity, qEEG should be a proxy measure of physiological changes in the brain and CNS activity. Based on the above research, it should also reflect bodily dysregulation in many metabolic function areas the related degradation of the associated physiological processes.

Since anxiety has a clear EEG signature, correlations were conducted between psychological anxiety and reports of adrenal symptoms to assess the related variance between these factors. A very high correlation of .719 was noted, confirming the strong association between psychological and physiological factors. This is consistent with past biofeedback research (Shwartz, 1995). Consequently the physiological component of anxiety should be reflected in the brain and the EEG as well. Past research supports this observation (Hammond, 2011).

Correlations were also conducted between depression and reports of thyroid symptoms. A lesser but nevertheless significant correlation was detected suggesting strong connections between depression and thyroid conditions. This association has been noted previously in other research (Neidermeyer & da Silva, 2005). The lower level of correlation indicates the variance of the dependent variable is shared by other unrepresented but known factors such as mood and serotonin production. Since depression has an established EEG signature in asymmetry (Davidson, 1992), there should be a significant association between EEG and hypothyroid.

The Model
The first signs of stress in the EEG is excessive desynchronization resulting in chronic reductions in alpha due to chronic desynchronization activity related to anxiety and excessive production of beta frequencies (Hardt & Kamiya,1978; Avram et al, 2010). This a period of increased adrenal activity, elevated cortisol levels, immune suppression, and disrupted melotonin cycles.

Over time this results in a chronic beta asymmetry that can be present even when the rest of the EEG appears fairly normal (Heller, 1997; Engles et al, 2007).

As anxiety becomes more chronic and sleep losses increase, beta progressively increases with chronically low alpha (Avram, 2010). Common physical symptoms include gastrointestinal problems and headaches. Typically aldosterone levels decrease with symptoms of tachycardia and heart palpitations in addition to visual vestibular disturbances.

High levels of anxiety, rumination and panic begin to manifest with more severe physiological problems as the body becomes progressively taxed. Diffuse elevated beta becomes common with high beta reflecting excessive global bracing of the muscle system.

Compromised mitochondria reduce ATP output and sodium pumps fatigue resulting in diffuse elevations of alpha due to large dipole layers idling as a consequence of energy deficiencies.

With time, after prolonged excitotoxic activity, the neuronal pump efficiency degrades and high frequency activity begins to diminish as diffuse alpha activity increases (Scicutella, 2007). At this point the beta again begins to appear normal although it is due to exhaustion.

As body physiology becomes chronically mobilized and enteric integrity degrades inflammations begins to impact thyroid function. This reduces thyroid T3 levels and slows down the alpha frequency. High frequency activity continues to decrease from loss of mitochondrial energy and beta become significantly low.

As inflammation grows from chronic oxidative stress glucose transport diminishes with growing diffuse theta and temporal delta emerging (Bridwell et al, 2013; Bellanger et al, 2011;Rosa et al, 2010).


Finally the levels of oxidative stress, inflammation and perfusion overwhelm the brains capacity to produce fast wave activity and it becomes dominated by slow wave activity in the form of diffuse elevated theta and delta (Knyazev, 2012).

The entire cycle is represented below as a sequence.

This model provides a comprehensive sequence of changes beginning with anxiety and emotional trauma, moving through depression and diffuse elevated slowed alpha and ending in diffuse elevated delta corresponding to neuroinflammatory processes, but an individual can be accelerated into any phase of this process by highly traumatizing events such as assault, TBI, stroke, or exposure to highly toxic substances or pathogens.

At the outset the individual is engaged in rapid oxidative processes but eventually becomes more deficient in resources resulting in a slow oxidative process in many areas. One mode of measurement for this process was developed by Dr. George Watson who proposed a fast and slow oxidizer profile that can be measured through hair analysis.

George Watson (1972) distinguished between individuals with acid blood pH versus slow oxidizers with more alkaline pH as well as Co2 differences between the groups. Paul Eck, M.D. develop techniques that brought hair mineral analysis to a new sophisticated level to assess oxidative types and related them to Selye’s work on stress. Fast oxidizers were defined as those with a calcium/potassium ratio of less than 4 and a sodium/magnesium ratio of 4.17 or higher. They theoretically tend to have higher sympathetic tone and intake more oxygen with faster breath and heart rates. Slow oxidizers are defined as having a calcium/potassium ratio greater than 4 and a sodium magnesium ratio less than 4.17. They have slower metabolic rates and tend to suffer from chronic parasympathetic activity due to exhaustion and globally deficient mitrochondria suffering from electron transport problems. Wilson (2014) observes that they are in a chronic stage of resistance to stress or in the exhaustion stage completely. He characterizes them as suffering from sympathetic dominance with adrenal and thyroid insufficiency. Some factors which can reportedly skew analysis results are excessive toxic metals, nutritional deficiencies, infections, and illnesses.
Hair analysis is considered a method of sampling tissue mineral levels as opposed to blood or serum mineral levels. It has been controversial with regard to its accuracy in the past however the technology has advanced and it is presently being used by the courts and medical facilities to accurately assess drug use and a new method has emerged for tracking cortisol changes in the body over time that has proven more accurate than any other method presently being used (Manenschijn et al, 2013).

To test his hypothesis we compared individuals assessed as either fast or slow oxidizers based on this model of hair analysis and associated metabolic profile with qEEG brainmaps. Based on the foregoing arguments and data, we hypothesize that fast oxidizers should have qEEGs dominated by fast wave activity and slow oxidizers should have qEEGs dominated by slow wave activity.

Fifty-eight clients (ages 18-72) from three clinics who had received a qEEG, were resistant to NFB training and demonstrated high metabolic checklist scores were selected for hair analysis. The New Mind metabolic checklist is a normed checklist (N=3000) with a sample derived from over 500 clinics nationwide. Clients are asked to rate their symptoms based on frequency and severity of symptoms related to major metabolic categories. The normed checklist construction and validation can be found on the New Mind website in the help section.

Clinical NFB therapists were provided a grading system to rate qEEG maps. Fast wave maps were identified as qEEGs dominated by statistically significant levels of beta frequencies and slow wave maps were identified as qEEGs dominated by delta and or theta frequencies. Each client received a hair analysis which was used by the lab to determine if they were a fast oxidizer or a slow oxidizer. Clients were then assigned to categories of Fast Oxidizer with fast wave map, Fast Oxidizer with slow wave map, Slow Oxidizer with fast wave map and Slow Oxidizer with slow wave map. A chi square test of independence was then performed on these ordinal levels of variable categories.

Results are listed below showing a significant chi square test statistic of 10.56, with a critical value of 3.8 and P=.001. Fast oxidizers had an average metabolic checklist score of 55 and slow oxidizers had an average metabolic score of 94. In addition 86 percent of the entire group had significant elevated aluminum levels.

The findings show a clear trend in the data in support of the above theory. The maps do tend to reflect patterns of fast and slow oxidizers. Fast oxidizers show more initial high frequency activity as the organism constantly mobilizes itself to deal with chronic environmental challenges and social distress. Slow oxidizers tend to show greater slow wave activity as the organism progressively becomes depleted in resources through the resistance phase and begins the exhaustion phase. Of particular interest is the fact that slow oxidizers generally scored much higher on the metabolic score indicating they consistently had more physical symptoms associated with health problems. This also supports the notion that their system is compromised and depleted. There were exceptions to this pattern and that should be expected considering the complexity of the phenomena being studied. The descent into slow oxidizer status may be erratic and inconsistent in its expression. Some may cling partially to their high oxidizer status as they decline, resulting in dual occupation of the perhaps rigid ordinal categories being defined and measured. Others may make more rapid decent with health problems accumulating at a more rapid pattern. The brain and the body have barriers and boundaries that define them partially independent but closely related systems. This is consistent with the modern model of neuroimaging (Freeman, 2005).

The next phase of investigation will involve collecting larger numbers to confirm the statistical findings here. Since the metabolic score reflects health status of the client and the clearly associated oxidizer status it is likely we can use the metabolic scores as proxy measure of hair analysis tests. In fact a normed metabolic measure that reflects oxidizer status is well within reach of the resources of our present database and will likely be employed.

The results clearly show our hypothesis supported. In addition, the checklist score indicate the individuals with fast wave dominant maps had fewer problem than those with the slow wave maps suggesting that there was a chronological sequence to the acquisition of their problems that began with anxiety. This is consistent with theories that anxiety precedes depression, that depression follows the physiological depletion resulting from anxiety and that depression is a form of end stage anxiety. This is further supported by research showing the relationship between depression and neuroinflammation as well as depression and the enteric microbiome problems.


Alkadhi, K. (2013). Brain physiology and pathophysiology in mental stress. ISRN Physiology, 1-23.
Alper, K., Prichep, S., Kowalik, S., Rosenthal, M., John, E. (1988). Persistent QEEG abnormality in crack cocaine users at 6 months of drug abstinence: Thesis of neural sensitization in cocaine dependence. Neuropsychopharmacology 1998; 19: 1–9.
Avram J., Baltes F.R., Miclea, M., Miu A. C. (2010). Frontal EEG activation asymmetry reflects cognitive biases in anxiety: Evidence from an emotional face stroop task. Appl Psychophysiol Biofeedback, 35: 285–292. doi: 10.1007/s10484-010-9138-6
Baker, M.,Akrofi, K., Schiffer, R., O'Boyle, M.,W.(2008). EEG Patterns in mild cognitive impairment (MCI) patients. The Open Neuroimaging Journal, 2, 52-55.
Belanger, M, Allaman, I., Magistretti, P. (2011). Brain energy metabolism:Focus on astrocyte-neuron metabolic cooperation. Cell Metabolism, 14, 724-738.
David A. Bridwell, D., Wu, L, Tom Eichele, Calhoun, Vince (2013). The spatiospectral characterization of brain networks: Fusing concurrent EEG spectra and fMRI maps. NeuroImage, 69, 101–11.1
Davidson, R.J. (1992). Anterior asymmetry, affective style, and depression. Psychophysiology, 29(4A, suppl 4).
Dietzel, I.D., Mohanasundaram, S., Niederkinkhaus, V., Hoffmann, G., Meyer, J.W., Reiners, C., Bohr, C.B.K. (2012). Thyroid hormone effects on sensory perception, mental speed, neuronal excitability and ion channel regulation. In Neeraj Kumar Agrawal (Ed.), Thyroid hormone, 85-122. ISBN 978-953-51-0678-4
Enciu, A., Gherghiceanus, M., Popescu, B. (2013). Triggers and effectors of oxidative stress at blood-brain barrier level: Relevance for brain ageing and neurodegeneration. Oxidative Medicine and Cellular Longevity, 2013, 1-12.
Engels, A. S., Heller, W., Mohanty, A., Herrington J. D., Banich, M. T., Webb, A. G., Miller, G. A. (2007). Specificity of regional brain activity in anxiety types during emotion processing. Psychophysiology, 44, 352-363
Filley, C., Kleinschmidt-DeMasters, B.K. (2001) Toxic leukoencephalopathy. N Engl J Med, Vol. 345, No. 6, 425-432.
Freeman, Walter J.(2005). A field-theoretic approach to understanding scale-free neocortical dynamics. Biological Cybernetics, 92,6: 350-359.
Freeman, W. J., Ahlfors, S. P., Menon, V. (2009). Combining fMRI with EEG and MEG in order to relate patterns of brain activity to cognition. International Journal of Psychophysiology.
Hammond, D Corydon (2011). What is neurofeedback: An update. Journal of Neurotherapy: Investigations in Neuromodulation, Neurofeedback and Applied Neuroscience, 15:4, 305-336
Haneef, Z., Levin, H.S.,Frost, J.D., Mizrahi, E.M.(2013). Electroencephalography and quantitative electroencephalography in mild traumatic brain injury. Journal of Neurotrauma., 30:653–656.
Hardt, J. V., & Kamiya, J. (1978). Anxiety changes through electroencephalographic alpha seen only in high alpha subjects. Science 201, 79-81.
Harvey, B.H. (2006). Adaptive plasticity during stress and depression and the role of glutamate-nitric oxide pathways. S Afr Psychiatry, 9,132-139.
Heller, W., Nitschke, J. B., Etienne, M. A., & Miller, G. A. (1997). Patterns or regional brain activity differentiate types of anxiety. Journal of Abnormal Psychology, 106(3), 376-385.
Knyazev, Gennady G. (2012). EEG delta oscillations as a correlate of basic homeostatic and motivational processes. Neuroscience and Biobehavioral Reviews, 36: 677–695.
Ivanka Savic, Ivanka (2013). Structural Changes of the Brain in Relation to Occupational Stress. Cerebral Cortex, pg1-11.doi:10.1093/cercor/bht348
Lin, M., & Beal, M. (2006). Mitochondrial dysfunction and oxidative stress in neurodegenerative diseases. Nature, 443(7113), 787-795.
Manenschijn, L, Schaap, L.,van Schoor, N., van der Pas, S.,Peeters, G., Lips, P.,Koper, J., van Rossum, E (2013). High Long-Term Cortisol Levels, Measured in Scalp Hair, are Associated with a History of Cardiovascular Disease. J Clin Endocrinol Metab, 98,5, 2078–2083.
McBride,J., Zhao,X., Munroc,N.,Smith,C., Jicha,J.,Hively,L., Broster,L., Schmittd,F.A.,Kryscioe, R.J., and Jiang,Y. (2014). Spectral and complexity analysis of scalp EEG characteristics for mild cognitive impairment and early alzheimer’s disease. Computer Methods Programs. Biomed, 114(2), 153-163.
Niedermeyer, E., Lopes da Silva, F.H. (2005). Electroencephalography: Basic principles, clinical applications, and related fields. New York: Lippincott Williams & Wilkins.
Ortiz, M., Jacas, C., Cordoba, J., (2005). Minimal hepatic encephalopathy: diagnosis, clinical significance and recommendations. Journal of Hepatology, 42, S45-s53.
Rosa, M.J., Kilner, J., Blankenburg, F., Josphs, O., Penny, W. (2010). Estimating the transfer function from neuronal activity to BOLD using simultaneous. EEG-fMRI. Neuroimage, 49, 1496-1509.
Savic, I. (2013). Structural changes of the brain in relation to occupational stress. Cerebral Cortex, 1-11.doi:10.1093/cercor/bht348.
Sapolsky, Robert M (1999). Glucocorticoids, stress, and their adverse neurological effects: relevance to aging. Experimental Gerontology, 34(6) ,721–732
Selye, H. (1982). History and present status of the concept of stress. In L. Goldberger and S. Breznitz, eds.,Handbook of Stress: Theoreticaland Clinical Aspects, 7-17. New York: Free Press.
Scicutella, Angela (2007). Neuropsychiatry and traumatic brain injury. In Jean Elbaum & Deborah M. Benson. (Eds.) Acquired brain injury: An integrative neuro-rehabilitation approach (pp81-121). New York: Springer.
Shonkoff, J.P., Garner, A. S. (2012). The lifelong effects of early childhood adversity and toxic stress. Pediatrics,129 (1), e232-e246. DOI: 10.1542/peds.2011-2663
Mark S. Schwartz, Mark s.(1995). Biofeedback: A Practitioner’s Guide. 2nd ed., New York: The Guilford Press.
Tops, M., van Peer, J.M., Wester, A.E., Wijers, A.A., Korf, J. (2006). State-dependent regulation of cortical activity by cortisol: An EEG study. Neuroscience Letters, 404, 39–43.
Wang, X., Michaelis, E.K. (2010). Selective neuronal vulnerability to oxidative stress in the brain. Frontiers in Aging Neuroscience, 12, 1-13.
Yuen,E.Y., Wei, J., Liu,W.,Zhong, P.,Xiangning,L.,Yan, Z.(2012). Repeated stress causes cognitive impairment by suppressing glutamate receptor expression and function in prefrontalcortex. Neuron, 73(5), 962-977.
Watson, George (1979). Nutrition and Your Mind. The Psychochemical Response. Harpercollins, N.Y
Wilson, Lawrence (2014). Nutritional balancing and hair mineral analysis. L.D. Wilson Consultants, Inc., Phoenix.