1925 - Hans Berger, a neuro-psychiatrist, recorded the first human EEG on his son Klaus. An EEG records brain electrical activity over time. Berger named the 10 cycles/second rhythms “alpha” waves.¹

The first neurologists and neuro-psychiatrists to use EEG saw that the rate, amplitude and constancy of their alpha waves were different.²

Early research also found that some EEGs show mostly beta waves.

(Other regular rhythms are omitted for simplicity).

1930's - Neurologists discovered transient, irregular activity in the EEG of patients with seizures or epilepsy. Like the short-lived nature of an observable seizure, irregular EEG activity was rapid in onset and offset, easy to see and looked like a glitch in an electrical circuit. Irregular EEG activity was objective evidence of brain dysfunction that a doctor could not know from a medical exam.

Differences in alpha waves meant human neurophysiology is different, but the meaning and benefits of this observation would only emerge half a century later.

Leaving the medical significance of brainwave variations for others to pursue, neurologists applied their EEG work to diagnosing and treating patients with seizures or epilepsy.¹

1950 - Neurologists and neurosurgeons at major medical centers were using EEG to identify irregular activity and to monitor the effects of medical treatment on individual brain function. EEG also evolved as a tool to detect brainwave patterns indicating coma or brain death, to study sleep and monitor sensory and motoric pathways.

1930-1940's - A few neuro-psychiatrists carefully studied the regular brainwaves in healthy adults and observed they remained distinct and stable over time. The conclusion: an EEG is characteristic of a person.³

Three neuro-psychiatrists noted subtle differences in their usual brainwave pattern. Then, each submitted to brief physical challenges over five years, including inadequate oxygen, stomach distension, decompression sickness, low blood sugar, excess alcohol, low carbon dioxide, a high dose of anti-malaria medication and others. An EEG was done during and after each distressing event.

Each doctor’s brainwaves were temporarily altered by the stressors, but two of them suffered visual defects and migraine headaches, during which their regular brainwaves distorted into irregular, slow waves before returning to their usual pattern.

EEG had served as a neurological gauge, tracking - in real time - brain physiology in health, through medical illness and back to health.

After the study, each doctor’s regular EEG brainwaves looked the same as five years earlier.⁴

1950 - Brainwave pattern recognition showed:

• Brainwaves are individually distinct and stable over time.
• Brainwave variations among persons mean neurological differences.
• Neurological differences mean different brain physiology.
• EEG tracks brain physiology during health and physical distress.

1950 forward - Advances in psychology increasingly influenced neuro-psychiatry and psychiatry by offering psychological theories and terms for a lack of neurophysiologic knowledge. An example is that medical EEG researchers repeatedly tried to link brainwave variations [physiology] with personality [a bio-psycho-social concept]. They were not successful.

A behavioral sorting system called the Diagnostic & Statistical Manual of Mental Disorders [DSM] began in the 1950s to organize the symptoms and signs of mental disorders.

The DSM was not intended to predict responses to types of medication. In the absence of a neurological gauge to show the effects of medicines on brain activity, DSM symptoms and signs were used by doctors for this purpose.

1970's – The advent of high speed computers provided digitized EEG processing with efficient recording and storage of data. Additional regular brainwave features, not detectable by visible inspection, were also available for the first time.

Pioneering neuroscientists constructed quantitative EEG databases in asymptomatic persons from childhood to old age.^5-8

For the first time, neuro-psychiatrists and psychiatrists could compare a symptomatic patient’s EEG & QEEG values with those of asymptomatic persons of the same age.

1986 - Hamlin Emory, M.D. and Stephen Suffin*, M.D.,dissatisfied with traditional psychiatric treatment, decided that persistent mental disorders were symptoms and signs of medical illnesses. They thought that EEG and quantitative QEEG data might show how psychiatric medications affect individual brain function.

Dr. Emory adopted a comprehensive medical approach:

• Physiology is primary.
• Collect, sort, integrate medical/neuro-psychiatric findings, EEG/QEEG & other lab data.
• EEG/QEEG data is the primary organizing variable for selecting medical treatment.

2010 - An EEG technology that transforms a person’s brainwaves into a medication predictor was shown more effective than the traditional psychiatric approach in guiding medication selection for major depressive disorder [MDD]. Psychiatrists at 5 prestigious U.S. medical centers achieved a 68% success rate for MDD when they used an automated version of Dr. Emory’s EEG pattern recognition to select a medical regimen for each patient. In contrast, when doctors chose medication from each patient’s symptoms and behaviors, there was only a 39% success rate.

The superiority of Dr. Emory’s EEG medication prediction method is described in the January 2011 issue of The Journal of Psychiatric Research.⁹

Dr. Emory’s medication selection for each patient in the EEG database was based on:

• an inclusive medical & neuro-psychiatric assessment
• medication to improve or correct abnormal medical & neuro-cognitive findings
• medication to improve the baseline EEG/QEEG data toward age average values
• each patient in the EEG database had long term medical follow up

This landmark investigation suggests that a medical approach and EEG/QEEG medication correlation yield superior neuro-psychiatric outcomes for persons with underlying medical conditions.

Such results demand a change from the traditional method of treatment.

On learning the results, Dr. Emory stated, “After more than two decades of neuro-psychiatric EEG research to improve the underlying medical component of mental disorders, I am relieved that an automated version of my EEG medication selection was validated by psychiatrists at five U.S. medical centers. Objective EEG measures were robustly superior to the STAR*D algorithm in helping doctors select effective medication for patients in the study.“

• Neidermeyer E, Lopes da Silva, F. Electroencephalography. 5th ed. Philadelphia, PA: Lippincott Williams & Wilkins; 2005.
• Adrian ED, Yamagiwa K. The origin of the Berger rhythm. Brain. 1935;58:323-351.
• Lemere F. The significance of individual differences in the Berger rhythm. Brain. 1936;59:366-375.
• Engel GL, Romano J, Ferris EB. Variations in the normal electroencephalogram during a five year period. Science. 1947;105(2736):600-601.
• John ER, Karmel BZ, Corning WC, et al. Neurometrics. Science. 1977;196(4297):1393-1410.
• John ER, Prichep LS, Almas M. Toward a quantitative electrophysiological classification system in psychiatry. In: Racagni G, Brunello N., Fukuda T, eds. Biological Psychiatry, Amsterdam: Elsevier. 1991;2:401-406.
• Prichep LS, John ER, Essig-Peppard T, Alper KR. Neurometric subtyping of depressive disorders. In: Cassullo CL, Invernizzi G, Sacchetti E, Vita A, eds. Plasticity and Morphology of the Central Nervous System. London: MTP Press. 1990.
• Prichep LS, Mas F, Hollander E, et al. Quantitative electroencephalographic subtyping of obsessive compulsive disorder. Psychiat Res: Neuroimaging. 1993;50(1):25-32.
• DeBattista C, Gustavo K, Hoffman D, et al. The use of referenced-EEG (rEEG) in assisting medication selection for the treatment of depression. J Psychiatr Res. 2011;45(1):64-75.