These results are from 300 seconds of linked ear EEG data, note the dominant slower alpha peak frontally…. And the raw coherence values of that linked ear data. The raw EEG file sample is also included, so you can see the waveforms these values are being drawn from.

Linked Ears

Raw Coherence Values of Linked Ear Data

Raw EEG

The same exact 300 seconds of EEG data, reprocessed now with the weighted average montage.  Note the difference in spectra, and waveform!!!  The temporal slower alpha is now seen as the source of that slower alpha content.

The alpha hypercoherence in the EEG is easily seen in this data, but not in the linked ears.

This shows that you need to find the EEG montage that shows the actual EEG data for your case first, and THEN calculate coherence.

Weighted Average Spectra

Weighted Average Waveform

Weighted Average Raw

The images below show the Spectral plot, coherence plot and raw EEGs. Just like the other montages did. The Cz coherences are so inflated with field effects they are at 0.8 across the full spectrum at some sites, obviously artifactually high.

Spectral Plot

Coherence Plot

Raw EEG

We are honored to welcome several high-profile speakers, including:

• Personalized Medicine in the Age of Technology - Vilayanur S. Ramachandran, MD, PhD; Director of the Center for Brain and Cognition and Professor with the Psychology Department and Neurosciences Program at the University of California, San Diego, and Adjunct Professor of Biology at the Salk Institute

• Regeneration and Stress at Work: Strategies for Improved Employee Health - Tores Theorell, MD, PhD; Professor Emeritus at the University of Stockholm, Sweden

• An Overview of Mind Body Healing - C. Norman Shealy, MD, PhD; founder of the American Holistic Medical Association, and past president of the International Society for the Study of Subtle Energies and Energy Medicine

• Neurotherapy in the Treatment of Traumatic Brain Injury: A Physiological Hypothesis – Paul Rapp, PhD; Professor in the Department of Military and Emergency Medicine at the Uniformed Services University of the Health Sciences

General comments:

There is a generally reciprocal effect between alpha and beta, as brain stem stimulation desynchronizes the alpha generators, beta is seen.  During states of under-arousal, this relationship is not seen, as when the subject is alerted, when both alpha and beta increase.

The point is that the arousal level changes the EEG responses expected, as when a stimulant is given to an under-aroused subject, increasing alpha. In a normally aroused subject, stimulants decrease alpha, and in an anxious (low voltage fast EEG variant) subject alpha will not be seen as changed by a stimulant.

Though there is a response stereotype for each medication, there are also individual responses, which vary. Mixtures of medications become too complex to evaluate each individual medication’s contribution, not to speak of synergistic effects not seen with any single medication, which may be seen in polytherapy.

The following pages represent a summary of many articles, papers, reviews and books on medications and the CNS function, and finally nearly 30 years of experience in clinical and research EEG. The difficulty in this area is the definitions of bands varies, the methods of analysis range from visual inspection of the raw EEG to quantitative measures, not all of which are clearly defined… and thus the need for a brief summary which puts this into a concise form for reference.

I will use the following definitions for the EEG bands. Delta is .5-3.5 Hz.; theta is 3.5-7 Hz, with slowing describing activity starting in the delta band, fading out in amplitude through the theta band. Alpha is 7-13 Hz, with “high alpha” being 11-15 or 16 Hz. Beta is from 13 Hz to the high frequency response of the system.

Due to the difficulty in visually detecting many of the changes reported, even small but significant changes can be missed. Don’t expect to “see” every change noted in each patient, or when using only visual inspection.

Marijuana/ Hashish/ THC:

There is increased frontal alpha, with increased frontal interhemispheric hypercoherence and phase synchrony.  These findings are reported in chronic exposures.

Effects on the evoked potentials have been noted as well.

Lysergic acid diethylamide (LSD-25):

The baseline EEG seems to determine the effect, with decreased alpha and increased beta from a normal background.  With slower EEGs, there is an increase in alpha and fast activity. The low voltage fast EEG shows little change in spectral profile with exposure.

The increase in conditioned inhibition seen with lower doses corresponds to the decrease in paroxysmal activity. The stimulant effects of this powerful drug may cause convulsions at higher doses, such as the early government studies. In these studies, milligram doses were supplanted for the microgram recommendations from Switzerland, where the LDS was produced.

PCP, Phencyclidine, or angel dust:

There is a marked increase in slow activity, with paroxysmal activity and extreme voltages noted with increased dosage. Convulsions have been reported.

Barbiturates:

Rhythmic 18 to 26 Hz activity is noted, initially frontally, spreading with time to the entire cortex. With increased dose there is an increase in slowing, with further increases the faster activity is decreased and the slowing predominates, progressing to a decreased voltage and even a recoverable iso-electric pattern, in barbiturate coma.

Morphine/Opiates/Heroin:

Shortly following administration, there is increased alpha, with slowing of alpha during the euphoric high, with increased dose there is increased slowing, and like barbiturates the EEG may go iso-electric. There is an increase in REM sleep noted with opioids.

Alcohol:

Ethanol at higher levels causes slowing to occur, with the depressant effect seen behaviorally.  In the low voltage fast type EEG (seen in anxious, nervous and in many chronic alcoholics and their family members), the initial alcohol exposure causes the sudden occurrence of alpha. With severe chronic alcoholism, there can be an abnormal pattern of periodic lateralized epileptiform discharges (PLEDS) seen with obtundation. This is not true underlying epilepsy, but rather disappears with the treatment of the alcoholism.

Neuroleptics:

“Tranquilizers” such as chlorpromazine, or it’s equivalent, increase the coherence of the EEG and decrease beta, however they increase temporal and frontal sharp morphologic theta transients. There is a reduced alpha blocking with sensory stimulation, likely corresponding to the memory disturbance reported with these medications.

In cases of dopamine receptor hypersensitivity (tardive dyskinesia) there are prolonged bursts of mixed fast/sharp transients and slowing. There is a potentiation of latent epileptiform activity, even with lower doses.

Thioridazine also increases faster activity, accounting for its commonly reported antidepressant effects.

Clozapine, or Clozaril, shows the typical neuroleptic pattern, though with an increase in epileptiform discharges and increasing possibility with duration of medication usage, reaching as high as 30% of patients with epileptogenic EEGs after 3 years of use.

Anxiolytics:

Meprobamate was the first anxiolytic, or anti-anxiety, medication. It decreases alpha and increases beta over 20 Hz, also slightly increasing theta, while not increasing epileptiform activity or paroxysms. The benzodiazapines, like Valium or Ativan also decrease alpha and increase the 20-30 Hz band, with a sinusoidal hyper-rhythmic spindling waveform. Paroxysmal and epileptiform discharges are reduced with these medications. The effect of decreasing neural has been used for its anti-epileptic qualities, especially in cases of status epilepticus, where Intravenous Valium has the apparently “comatose” patient sitting up wondering what has been happening.

Hormones:

Vasopressin, usually in the form of DDAVP (desomopressin acetate), increases the high alpha band.  Cyproterone acetate is an anti-androgen with clinical effects on premenstrual complaints, though the qEEG effects predicted its strong anti-anxiety and mood elevating side effects. The decrease in frontal alpha and increased beta are noted.

Antidepressants:

Imipramine:

This drug produces an increase in slow activity, a decrease in alpha and high alpha, with an increase in the faster beta frequencies in the mid to upper 20 Hz range and up.

Amitriptyline: This drug produces more slowing than imipramine, though the other effects are similar. This corresponds to an increased initial sedative effect and its use as a sleeping medication for sleep onset as well as the usual wakefulness effects of antidepressants. In epileptics, there are increases in paroxysmal discharges, which can be controlled normally with adjustments to the anti-epileptic medications.

Ipronazid: This drug produces a slight increase in slower activity, though it produces a marked increase in faster activity. Paradoxically, this antidepressant does not produce an increase epileptiform profile or promote convulsions, even with this beta increase.

MAO Inhibitors: These medications have a wider variation of response than the other antidepressants. Isocarboxazide increases 30-20 Hz and decreases slower and higher frequencies, similar to a stimulant profile. Nialamide and Tranylcypromine produce a more typical profile, though with more variability.

SSRIs: These more modern antidepressants, such as Prozac, Paxil and Zoloft have fewer changes in the slow activity (associated with less viscero/autonomic side-effect), with a mild fronto-central beta increase in the range of 18-25 Hz and a decrease in alpha anteriorly.

Stimulants:

Stimulants increase the activity in the RAS, with the Raphe nucleus releasing norepinephrine, decreasing the polarization in the reticular nucleus of the thalamus and thus increasing the “clocking” or peak frequency of the rhythmic alpha activity and increasing faster activity.

Amphetamines: Both dextro and methamphetamines like Dextrostat or Adderal are similar in effect, with decreased slower activity and increased beta from 12-26 Hz. There is a paradoxical increase in alpha noted in the CEEG work of Itil (Itil et al., 1980). This is likely from the increased activation effect mentioned in the opening section.

Methylphenidate: Ritalin produces a decrease in delta and theta, with a more pronounced posterior alpha increase and an increase in low beta, with effects delayed up to 6 hours, compared to the rapid effects of the amphetamines.

Caffeine: This moderate stimulant has a moderate length of effect, but has surprisingly little research on its EEG effect. A fairly current study of its withdrawal effects (Clinical EEG, Vol. 26 No.3, July 1995) shows an alpha increase frontally, with suppression following resumption. The study also shows theta increases with withdrawal, maximal the second day, resolving with resumption. The degree of change in both frequencies corresponds well to the subjective withdrawal severity.

Nicotine: This drug has similar effects to caffeine, including the withdrawal study (Itil et al., 1971).

Cocaine: The effects of cocaine differ from the amphetamines in that cocaine decreases synaptic reuptake, and amphetamines increase the release of the neurotransmitters in the dopamine/norepinephrine systems in the brain. With lower to moderate doses, there is increased alpha and beta. With increased doses there is a desynchronization of the EEG and faster activity predominates.

The alpha increase frontally is seen during the euphoric phase of the subjective report. Cocaine is a well-known epileptic potentiator. Chronic abuse causes a “burned out” dopamine system, with delta decreases and slower alpha noted with little improvement even one year later

Antimanics:

Lithium carbonate is used extensively to treat bipolar depression, reducing the manic behavior and being prophylactic to depressive recurrences and further mania. The EEG shows an increase in theta, mild decrease in alpha as well as increased faster activity, with a strong potentiation of latent epileptiform activity. This mimics the tricyclic anti-depressant profile, though with slower slows and more fast activity.

Overdoses produce a marked slowing of the EEG, with triphasic discharges reported, likely associated with the liver toxicity and the associated metabolic disturbances, similar to the findings in hepatic encephalopathies. These slower findings may be noted many weeks following discharge from the hospital. Slowing of alpha (rhythmic background that responds to eye opening) down to 4 and 5 Hz two weeks after discharge from hospitalization, with normal 9 Hz alpha in the child returning only after many months is reported in a case study (NeuroNet Neuroscience Centers, 1999).

Tuburculostatics:

INH, Isonicotinic acid hydrazide, is an irritant to the CNS. Large doses can hypersensitize the CNS. The EEG shows bursts of paroxysmal activity with photic stimulation.

Methanol:

The EEG shows marked slowing, which correlates with the extent of acidosis more than the blood levels of methanol. This has been shown to be quite neuro-toxic, with optic nerve blindness noted commonly in chronic abuse/exposure.

Solvents:

The EEG show slowing, though the etiology remains uncertain, it is not without possibilities. Polyneuropathy, dendritic degeneration and demyelination have been seen in industrial exposures, any and/or all of which can cause slowing.

Mercury:

With initial exposure to this neurotoxin (and many other heavy metals) there is an increase of faster activity, though with increased concentrations there is an increase in fast and slow activity, with eventual paroxysmal activity of an epileptiform nature.

Organo-phosphates:

The insecticides are known to form peripheral neuropathies, though also have central actions. The EEG shows slowing and paroxysmal bursts, though in coma there is a paradoxical spindling fast activity.

Chlorinated hydrocarbons:

Also insecticidal, these chemical compounds are fat soluble, stored and accumulating to a toxic level they are known to cause convulsions. Neurologically, there are bi-temporal sharp discharges and anterior slowing, rarely are spikes noted, with or without convulsions.

Lead, organic:

Cerebrotoxic effects are strong, with IQ points dropped significantly even with trace measurable exposure. Dementia progresses with increased exposure, with eventual convulsions. The EEG shows diffuse slowing in sub-acute exposure, with increased exposure leading to paroxysmal discharges. Inorganic lead has weak cerebrotoxicity.

Aluminum:

Commonly seen in dialysis encephalopathies, with myoclonic activity seen behaviorally. Though not well documented, the EEG shows slowing with excessive fast activity, in my experience.  At autopsy, the aluminum is found concentrated anteriorly.

Provided courtesy of Jay Gunkelman, QEEG-Diplomate, Q-Pro Worldwide

Epilepsia

Volume 43 Issue 5 Page 482 – May 2002

To cite this article: Martin C Salinsky, Lawrence M Binder, Barry S Oken, Daniel Storzbach, Carey R Aron, Carl B Dodrill (2002)
Effects of Gabapentin and Carbamazepine on the EEG and Cognition in Healthy Volunteers
Epilepsia 43 (5), 482–490.
doi:10.1046/j.1528-1157.2002.22501.x

The results demonstrate that prolonged treatment with either CBZ or GBP can have significant effects on quantitative measures derived from EEG background rhythms. Both AEDs slowed the posteriorly dominant (alpha) EEG rhythm and median EEG frequency, and increased the percentage of theta and delta power. Overall, CBZ produced significantly greater slowing than did GBP. The observed test–retest changes are not simply shifts in group mean over time, but include many individuals (10 of 11 CBZ subjects, and six of 12 GBP subjects) whose test–retest change exceeded the 95% CI based on untreated healthy controls. Long-term AED treatment also affected several objective and most subjective measures of cognition and mood as compared with test–retest normative data obtained from untreated controls.

I suggested at the time that all the emotion was merely an example of someone yelling “the sky is falling”, like Chicken Little. There was no real problem, just lots of crying out and hand wringing.

I requested in an open international forum for anyone to send me a sample of the problem, and none could be produced. I suspected there was no real problem, as the sample issue was concerning a 500 sample/second device having a time skew… though this was in comparison to a database collected on a 100 sample per second device, with the waveforms interpolated from these samples.

It was highly suspect from my technical perspective when this issue was raised, and it was even more suspect when nobody could produce actual data showing the coherence or phase issue.

Testing now has shown that the old style Mitsar, with the non-simultaneous sampling is identical in performance to the new style amplifier that has simultaneous sampling, and thus no skewing error is possible in the newest amp.  There is also an intermediate style amplifier tested, which is one of the smaller amps, but with a more current sampling design.

The data clearly show that there is no difference in coherence between these devices.

The Mitsar amplifier also has been tested with the new BranMaster  Discovery amplifier, and it also was shown to have indenticle coherence findings with the Mitsar amplifier.

Clearly there is no real issue.

Data rules…. the experimental details are below.

Jay

We performed the following experiment.

We took three different Mitsar amplifiers:

1. Mitsar-EEG-201 – old model of amplifiers with relatively large time shift between channels (1.75 ms maximum) 2. Mitsar-EEG-201M – new model of amplifiers with relatively small time shift between channels (470 microsecond maximum) 3. Mitsar-EEG-202 – 32-channels amplifiers with zero time shift between channels.

Than we take Electro-Cap and put it on the head of one subject. We used linked ears referent for EEG recording.

We sequentially connect Electro-Cap and referents to three different amplifiers and perform independent recording of EEG in eyes closed condition. The duration of recording was longer than 300 seconds.

When we reconnect Electro-Cap and referents from one amplifier to another we do not touch to electrodes on the head and ears.

The total time of out experiment was approximately 20 minutes. This means the functional state of subject remain relatively stable.

Than we remontage the EEG to average referent (very important for time  shift influence measurements), compute the coherence for all three EEG recordings using the same processing parameters and compare them. The duration of time interval for processing was the same for these EEG recording and was equal to 300 seconds.

We do not find any dramatic differences in coherence corresponding to different amplifiers. The small fluctuations can be explaned by amplifiers noise and non-stationarity of EEG.

Mitsar Calibration

The Mitsar system is calibrated at the manufacturer. There is no need to recalibrate the amplifier unless there is a serious problem from damage. The calibration button is so the user can run a test calibration signal to demonstrate that the channels are in fact correct and equal. If these were ever not correct (or equal) then the manufacturer would recalibrate the hardware device. It is blocked so that a user cannot accidentally mess the calibrations up. So in summary the calibration button in the software is pressed and then the button to observe EEG is pressed. Then the test calibration signal is generated and can be recorded. This is a good idea if the case is a medical or legal evaluation so that when the data is presented as evidence there is validation that a microvolt equals a microvolt.

It’ll be the first Neuroscience Multidisciplinary Meeting hosted by the Brainmech Foundation in Spain after the last conference held in Holland in 2007. This is a unique oppurtunity for professionals to learn today what investigators and scientists on neuroscience are preparing for the future.

It’ll be the meeting point for Psychiatrists, Psychologists, Neurologists and Pediatricians that will have the chance to learn from the authors about the last investigations and researches on the human brain, new methods of diagnosis, new diagnosis criteria on mental disorders proposed for the DSM-V, neurobiologist database of the ADHD, bipolar disorder, as well as the new treatments and therapy for neurological illness and psychiatric malfunctions.

Neurotherapy using slow cortical potentials also shows promise in the treatment of epilepsy (Kotchoubey et al., 2001; Birbaumer et al., 1981; Sterman, 2000). Neurotherapy has also been used for ADD/ADHD (Monastra, Monastra, & George, 2002) depression (Rosenfeld, 1997), anxiety (Vanathy, Sharma, & Kumar, 1998), fibromyalgia (Donaldson, 2002), and for cognitive enhancement (Budzynski, 2000; Klimesch, et al.). Commonly reported success rates of 60 to 90% are reported  (Wright & Gunkelman, 1998).

Neurofeedback is an emerging neurosciencebased clinical application based on the general principles of biofeedback or cybernetics. The Neurofeedback process involves training and learning self regulation of brain activity. Understanding the underlying principles of this process allows the therapist to provide referrals or treatment to their clients with some added understanding, and provides clients with  a framework for understanding the neurofeedback process. The following short paper will provide a quick review of the brain’s function, and the underlying process involved in neurofeedback, a technique  that will allow the client to better regulate and operate their brain.

The brain controls its own blood supply through the dilation and constriction of the blood vessels, and the blood flow is directed to areas that are more active through this self-regulation. The blood supply’s flow, along with the utilization of the oxygen and glucose the blood carries is measured as “perfusion,” a measure that is clearly seen in some of the modern imaging techniques, such as Positron Emission  Tomography (PET) and SPECT technology. Though these techniques are invasive, requiring the injection of small amounts of very short half-life radioactive materials, they do give good resolution of the perfusion due to the emission of the positrons, which are emitted from where the brain utilizes the oxygen and burns the glucose carried by the blood flow.

A research project performed at UCLA’s Neuropsychiatric Institute (Cook, O’Hara, Uijtdehaage, Mandelkern, & Leuchter, 1998) showed that the brain’s electrical activity, or electroencephalogram (EEG), had specific correlates of the brain’s perfusion. This is useful in that the EEG is capable of showing when the perfusion is low, such as  seen frontally in ADD/ADHD. In these situations, the EEG shows a resting or idling rhythm of alpha (8–13 Hz) and/or theta (4–7 Hz), frequency patterns in the EEG that have rhythmic waveforms.
(For a general review of electroencephalography see Niedermeyer & Lopes Da Silva, 1999).

In ADD/ADHD a study of over 400 participants using a neurometric approach (see Prichep & John, 1992; Prichep et al., 1993) showed that there were generally findings of excess alpha and/or theta in the frontal lobes (Chabot & Serfontein, 1996), which corresponded to the frontal hypoperfusion seen in ADD/ADHD with the PET or SPECT perfusion studies. The frontal lobes are executive areas in the  brain, which control attention, emotions (affect) and impulsivity, as well as regulate (inhibit) the motor areas of the brain. The Chabot study (1996) also showed that the EEG could be used to differentiate those with ADD/ADHD from normal clients, as well as differentiating ADD/ADHD from those participants with a learning disability (LD). The LD population was shown to have a slower pattern, with excess activity in the delta frequencies (1–3.5Hz) over the central and parietal lobes (posteriorly at the crown of the head). These areas are responsible for integrating raw sensory stimuli into perceptually interpretable activity.

More recently, a comparison of children and adults seen in a single neurofeedback practice specializing in ADD/ADHD was performed (Gurnee, 2000). This study showed that unlike the children’s study, which showed theta to be the dominant pattern, the adults had an alpha dominance, likely due to maturational changes that have increased the frequencies. In the children the excess theta group is over 50% of the cases, with the adult group showing excess theta to only comprise about 25% of the incidence.

The qEEG data may also be used to select specific medications if a pharmacotherapeutic approach is preferred. The qEEG pattern of frontal theta responds better to stimulants such as methylphenidate (Ritalin), whereas the frontal alpha type responds better to antidepressants. If a specific statistical measure called ‘coherence’ is deviant (too high or too low), the participant may require an anticonvulsant (Suffin & Emory, 1995). These patterns also may coexist such that an individual may require two or more types of medication.

Physicians generally use behavioral indicators in choosing psychoactive medication. However, it was clear from the work of Suffin and Emory that neurophysiological profiles can be used to guide prescription, even in populations of patients with similar behavioral disturbance (e.g., attentional or affective disorders as defined by DSM). Most physicians generally find the proper medication the old fashioned way . . . by trial and error. The “best guess” medication selection method requires more doctor office visits, medication trials, and has the possibility of significant side effects. All this is generally avoided with the more objective qEEG based method, which is based on the person’s physiology, not the behavior. It is not difficult to see why this is the case. The medication treats the physiology, hoping to affect the behavior. The measurement of the physiological indicators should logically be more related to the proper medication choice, since this is what is actually being treated.

The stimulant medications typically decrease appetite, with weight loss commonly noted, as are sleep problems. Long-term use of stimulants have been known to cause teeth grinding (Bruxism), cardiac rhythm changes, blood pressure increases, weight loss, changes in sleep patterns, anxiety/nervousness and even “psychotic” symptoms (such as hearing voices or other sensory hallucinations). There are also those with medical contraindications for stimulant use, such as heart problems, gastro-intestinal and blood pressure problems and other more rare complications that preclude prescribing them. In these individuals, as in those with complications from taking the medications, the presence of an alternative treatment is essential for proper behavioral adjustment and scholastic achievement. For those individuals uncomfortable with using potent medications, or those with adverse side effects, it is fortunate that a non-medication intervention is available.

The choice of medicating the client requires continued treatment, as it is merely a temporary change, due to the drug’s effects. Neurotherapy is a treatment, which changes the way the brain works, and once the skill is learned, (unlike medication) it appears to be persistent. Follow-up studies show long-term change in the brain’s function following neurotherapy (Monastra, 2003). Both of these methods (medication and neurotherapy) improve the client’s attentional and behavioral states. The choice of which method to use is merely a personal choice. Medications, when used long term, may end up being more expensive than Neurotherapy. Neurotherapy has less likelihood of having side effects than does the medication, but it takes a number of training sessions before the effect is noted and becomes more persistent.

It is no surprise that the brain can learn, but what may surprise some is that the brain changes structurally when it learns. This morphologic change is microscopic, the forming and reinforcing of small connections between a part of a neuron, called “dendrite,” but it is a structural change, nevertheless. This highly changeable connective nature is referred to as “neural plasticity,” based on the original definition of plastic, not as a substance, but as a descriptor of the malleability or change—ability of materials or structures.

The brain has a method of developing and expanding the pathways that are used, and “pruning” the connections that aren’t utilized. This process is most dramatic early in life, but continues throughout life. We are born with about twice as many neurons as are present when we become young adults. The pathways that are more consistently utilized are protected from the pruning process through a mechanism still unknown to science, though the fact of the change is irrefutable.

Another time when this process of plasticity is evident is following damage, such as head injury, or disuse of an area, due to “deafferentation,” such as when hearing is lost. In these situations the surrounding functions may take over an area not utilized, occasionally causing some subjective changes, which may be uncomfortable. One example of this is tinnitus, or ringing in the ears, following loss of hearing; another is “phantom pain” when a limb is severed and is no longer present, but sensations seeming to come from the missing limb are felt. The functions adjacent to these areas in the brain merely intrude into the area and the person misinterprets these new
signals as the older inputs.

These examples are dramatic, but “growththrough-utilization” is the underlying process we want to focus on. This process is how we build additional capacity for the nervous system to do its work. Analogous to exercise building muscle mass, the utilization of the brain builds the mass of the brain’s dendritic connections.

Certain intense negative experiences may change the body’s chemistry, increasing the stress hormone released from the adrenal cortex of the kidneys. This chemical, cortisol, is a healthy response to stress, though with chronic or overly intense stressors, the cortisol has deleterious effects on the brain, specifically attacking a temporal lobe structure, the hippocampus, which has immune receptors. This structure has important non-immune system functions as a memory comparator, required for both comprehension and recall. The implication for this latter process where stress deteriorates the brain’s ability to comprehend and recall has large implications for education. A person with a stressful existence may never reach his or her true potential due to the damaging effects of the stress hormones. If the stress was experienced during pregnancy from the mother’s hormonal balance, the newborn will have a disproportionately intense reaction to stress, causing inordinately large amounts of strain (and thus more cortisol) and ultimately more extensive deterioration of the brain’s capacity.

The ability to teach a new response to the situational stressors can change the life course for these individuals, creating a much more favorable outcome. The operant conditioning technique, Neurotherapy, mentioned earlier is one such method of intervention. Similarly to the stress response, there is a relaxation response, which can be trained to counteract these deleterious effects.

The brain’s electrical patterns are a form of behavior, which is subject to behavior modification through “operant conditioning,” a fact discovered in research done in the late 1960s by Dr Barry Sterman, now a professor emeritus at UCLA. The operant conditioning of the EEG was first demonstrated in cats, where placebo effects are assumed to be absent. Sterman’s original work with animals was replicated in humans starting in the 1970s (Sterman, 2000).

Recording and analysis of EEG has been shown to yield reliable results (Fein, Galin, Yingling, Johnstone, & Nelson, 1984). Further, those studies done with control groups have shown the neurotherapy technique to be a robust and valid intervention. Many more studies are of a case series variety, without control groups. Though this latter category is not held in high regard, perhaps this is changing. A recent issue of the New England Journal of Medicine reviews of research design have cast doubt on the need for placebo-controlled designs. Their review has shown that when there is a preponderance of case series reports, the concordance between those results and those of the “gold standard” (double blind placebo-controlled studies) was very high. Many in the field are now arguing against doing a double blind study due to the lack of proper humane treatment of those in the control group (receiving no treatment), an approach which is also now considered unethical by the World Health Organization when known treatments exist. Interestingly, recent work with placebo effect has elucidated brain mechanisms underlying placebo response that were different than those mediating medication response (Leuchter, Cook, Morgan, Witte, & Abrams, 2002).

With the neurotherapy approach, the brain frequencies that are in excess are reduced, and those with a deficit are increased. The technique uses the EEG, amplified from the minute voltages and hooked up with special instruments to control a computer game. The person’s EEG is the “joystick” they use to operate the game. Over a series of sessions the person learns to use the EEG to control the game. The clinician slowly adjusts criteria for reward presented to the individual, and thereby “shapes” the behavior of the participant’s brain into a more normal pattern.

The neurotherapy technique requires time to learn, and varies depending on the initial condition the individual starts with. In general, the more severe the starting condition, the more learning has to occur to correct the state. Simple relaxation may take as few as 10 sessions to learn; although with more severe cases, a longer training course may be needed, such as with generalized anxiety disorder, or panic attacks. The important point is that the learning is internalized so that the benefit of the training persists, and does not require on-going training. This is unlike the use of medications where symptoms typically reappear after discontinuing medication.

The learning curve for EEG has been described in research done at Langley Porter Neuropsychiatric Institute, at the University of California, San Francisco. The research showed that the curve is a fifth-order curve, which contains an initial increase, followed by a dip, a second increase, followed by the exponential increase at the end of the training (Hardt, 1975). This corresponds to the subjective states reported in some individuals. They are initially presented in a slightly anxious state, which gets better when they habituate to the training situation, corresponding to the initial increase in the curve. The individuals then report that they “try hard to relax,” a counterproductive attempt, which corresponds to the dip. They give up trying hard (active volitional attempts), corresponding to the second increase, which is then followed by the learning of the passive volitional state, which is the final exponential increase.

In some cases, the individuals may need more peripheral forms of biofeedback-based intervention, such as muscle relaxation or training of temperature or the electrodermal responses. These will depend upon whether their individual response profile shows their stress in these areas as well. The peripheral training often requires less training time, though the source of the difficulty is universally central, as these modalities are all under the control of the central nervous system. Training peripherally may be all that is required in more mild cases, but in many individuals, the central training is the only method that will have a persistent result. In our clinical experience with these more severe cases there may be symptom substitution without the central intervention. In cases with mild stress, the peripheral intervention is often a complete intervention, though when additional complaints such as attentional problems, depression, or hyperactivity are noted, the central intervention is usually the first choice, to minimize the total number of sessions, by getting directly to the common source
of the problem.

Prior to starting work with an individual, and in order to design an appropriate operant conditioning intervention, their existent brain function must be known. Optimally, this would entail a full recording of their EEG, with quantitative analysis and comparison of the individual’s brain activity to an age matched database. These databases are commercially available from a number of sources, and are described in a recent Journal of Neurotherapy special edition (Vol. 7, No. 3 and 4, 2003). Following such a comparison, areas that deviate from normal may be identified, as well as the direction of the deviation from normal. This shows whether an excess or a deficit of any frequency pattern exists, as well as the location of the deviation. Specific individualized patterns of results are used to guide intervention with neurotherapy.

Following this evaluation, an appropriately customized operant training may be designed which optimizes the training time by focusing on the areas of deviation. The training will take time, with 30 to 40 sessions being quite common before a permanent result is established, and even more are required for more severe or complex cases such as Asperger’s/Autism (Thompson & Thompson, 2003). There are commonly reported behavioral changes long before this end point of training is reached, with the early signs of change showing themselves at 5–10 sessions for most individuals, though some have strong changes even after their initial session. It is also common for an individual to not notice the change, as they are occasionally not self-aware, though the changes are easily seen in objective testing and reported by those observing the individual’s behavior.

Commonly reported success rates of 60 to 80% are seen in the scientific literature, with up to 90% reported in qEEG based intervention (Wright and Gunkelman, 1998). Using strict criteria (total remission of complaint) the percentage range from 50 to 60%, with those reporting positive results, though with less stringent measurements of success, such as “feeling like you got a positive benefit,” ranging in the 80 to 90% rate.

Many therapists are not aware of neurofeedback as an application of operant conditioning, being familiar with more easily observable behavioral operant training than the operant training of “internal states.” The neurofeedback literature is most well accepted in the area of operant training of EEG in epilepsy. Well-controlled studies show that the technique can assist in cases where medication alone was shown to be inadequate at controlling the electrical discharges associated with the epilepsy. A review of this application is published in a special edition of Clinical EEG, in the January 2000 issue (Sterman, 2000). Neurotherapy using slow cortical potentials also shows promise in the treatment of epilepsy (see, Kotchoubey et al., 2001; Birbaumer et al.,1981).

Since the EEG in epileptics can be taught to stop the abnormal discharges, leading to the elimination of the behavioral manifestations of the epilepsy, the neurotherapy technique has also been applied in less severe neurological disorders such as ADD/ADHD (Monastra et al., 2002) depression (Rosenfeld, 1997), anxiety (Vanathy et al., 1998), and fibromyalgia (Donaldson, 2002). Budzynski (2000) used neurofeedback to reverse cognitive decline in an elderly population (see also the work of Klimesch et al. for studies of EEG related to memory performance). For recent reviews of neurobehavioral disorders noted to respond to this emerging technologically based operant training technique see Yucha and Gilbert (2004), and Nelson, (2003).

There are two international professional organizations dedicated to the study of this technology and these applications: the Association of Applied Psychophysiology and Biofeedback (AAPB), and the International Society for Neuronal Regulation (ISNR). Both of these societies have annual conferences and often sponsor additional regional workshops. There are also many state and regional organizations, often affiliated with one of these international organizations. ISNR also has international chapters in other countries. Both organizations have web sites (www.aapb.org and www.isnr.org), and both sponsor a professionally published journal with material focused on Neurofeedback: The Journal of Neurotherapy (ISNR), and Applied Psychophysiology and Biofeedback (AAPB).

REFERENCES
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1Q-Metrx, Inc., Burbank, CA.
2Department of Psychology, University of California, Los
Angeles, CA.
3To whom correspondence should be addressed at Q-Metrx, Inc.,Burbank, CA

The thalamic involvement in the generation of the alpha rhythm is being under-valued when looking at the LORETA images of alpha current source generators. The alpha power may come from the sources that LORETA identifies, but the thalamus is intimately involved in alpha rhythm generation, and this is not part of the LORETA image of the sources.

The polarization within the thalamus sets the base frequency of the alpha, but the cortical rhythm requires a complex multi-layer feedback loop from the thalamus to the cortex, and back to the thalamus. Without the cortex, there is a total disruption of the normal spatio-temporal distribution of the alpha wave’s spike trains within the thalamus, and cortical damage often disturbs coherence due to this mechanism.

The thalamus distributes the alpha posteriorly via specific sensory relays, which have a simple return circuit. Like the white matter relay from the lateral geniculate of the thalamus to the occipital lobe’s primary visual areas, and directly back. This thalamo-cortical-thalamic loop is relatively faster than the loop seen frontally. The frontal return circuitry is not simple, but the descending routes are complex and somewhat circuitous, taking more time, and thus it is common for the frontal lobe’s alpha to be at the slower end of the individual’s alpha frequency range. The frontal lobe has a return path through the striatum.

The five divisions of the frontal-striatal pathways are the motor circuit, the oculomotor circuit (from the frontal eye fields), the dorsolateral prefrontal circuit (cortical gating), lateral orbito-frontal circuit (emotive), and the anterior cingulate circuit (emotional and cognitive flexibility). The striatal-thalamic pathways are divided into two descending pathways which both start from the cortex to the head of the caudate and then the putamen, and then this pathway divides between the globus pallidus and substantia nigra, and then these both go to the thalamus. The thalamo-cortical completion of the circuit projects to both the premotor and motor cortex directly.

Not all circuits are simple thalamus-to-cortex-to-thalamus “echoic” returns to the original source…

A cortico-thalamo-cortical projection system exists which originates from the primary visual cortex, relayed by the lateral posterior nucleus of the thalamus, projecting to the suprasylvian visual area (which is involved in highest levels of visual integration and comprehension). This finding suggests that the thalamus modulates transmission of cortical signals from one cortical area to another… the coherence or “connectivity” of the cortex is not cortical-cortical, but cortical-thalamo-cortical.

With maturation, the cortex provides a stimulatory effect on the alpha frequency, raising it to a slightly faster frequency tuning through feedback to the thalamus, but the basic frequencies of alpha are generated by the reticular nucleus of the thalamus providing acetylcholine to the thalamic nuclei, and by the underlying polarization within the thalamus, which is effected by the NE levels from the brainstem, and by fluctuating DC field strength levels in the brain. The other effects are the thalamo cortical transmission times, and an effect of the cortical-thalamic processing time for any given pathway…. Longer time needed for frontal than posterior circuits.

Crudely stated: The frequencies of alpha are set in the thalamus, and the spatial and temporal distribution of alpha are controlled by the cortex, with rhythmic “initiation” (phase reset) done by the DC system’s “modulatory” influence on the AC rhythms of the EEG.

The thalamus can provide rhythms in the range from 3 to 16, with the common range of 8-12 representing an adult group’s “average”. Hyperpolarization of the thalamus slows the alpha, and hypopolarization speeds it up until it desynchronizes at about 16 Hz, and becomes a low voltage fast EEG.

The addition of some GABA (an inhibitory neurotransmitter) easily acquired with the addition of some alcohol will slow the alpha back into a rhythmic pattern. This basic mechanism is the reason alpha-theta training works so well on the low voltage fast EEGs seen so commonly in alcohol addiction.

LORETA may show a generator in the precuneus/cuneus area for the occipital alpha component, and the posterior cingulate for the parietal component (when alpha modulators are identified with ICA analysis and then source localized)… but these localizations miss the full beauty of the real mechanism’s complexity and especially the primary importance of the thalamus.

The thalamus gates our perceptions into “perceptual packets”, with the “thalamic gate” being open during the negative half-wave (up-side of the waveform), and less open during the positive half wave (the downward going half). Two stimuli presented within 75 to 100 milliseconds of each other will be “perceptually synchronized”, or though of as being instantaneously simultaneous.

The alpha frequency is the perceptual sampling rate… how many perceptual packets are evaluated per unit time, with a better semantic or declarative memory function seen with faster alpha frequencies. This is from the work on IAF (individual alpha frequency) from Professor Dr. Wolfgang Klimesch’s lab in Salzburg Austria, with significant contributions from Drs Michael Doppelmayr and Simon Hanselmayr.

The databases have difficulty characterizing alpha frequency tuning issues, with many identifying too much power at a slower frequency (like 7 Hz)… although the power values would be healthy and normal if alpha were only faster (like 9 Hz)… the databases seldom tell you it is merely 2 Hz slow. The normal alpha coherence values, if the alpha is slowed, are seen as hypercoherence, although they are perfectly normal for alpha. Databases that rely on predetermined band’s peak frequency may miss a shift if it exceeds their defined band, and this will miss the mean frequency if the peak is good but the band width has less faster content than slower content.

Faster alpha may cause similar issues (too much 12-15 Hz power or 12-15 Hz hypercoherence) when it is not “too much” of either that is really wrong, just alpha being too fast.

Thus when there are tuning issues, databases often have difficulty characterizing the core issue of tuning. When a tuning issue is noted, the coherence and power values may be “off” according to the database, when the real values are not really abnormal, just that they are too slow or too fast.

Theoretically, these issues may prove to be an area where Z-score training may have difficulty, flagging red herrings of power and coherence… though this is an empirical question that will be answered with time and experience

The Boy with the Incredible Brain

This is the breathtaking story of Daniel Tammet. A twenty-something with extraordinary mental abilities, Daniel is one of the world’s few savants. He can do calculations to 100 decimal places in his head, and learn a language in a week. This documentary follows Daniel as he travels to America to meet the scientists who are convinced he may hold the key to unlocking similar abilities in everyone.


Secrets of the Mind

Click here to view the embedded video.

Phantoms in the Brain: V. S. Ramchandran from T.E.D.

This is a 25 minute video of Ramchandran’s talk presented at TED.

Phantoms in the Brain Part 1 Full Documentary


Phantoms in the Brain Part 2

From Taboo. this clip is about a man who wants to get rid of his own leg. Eventually trying to do it at home.

I’d like to just throw out there a few other findings that were discovered in a few exploratory investigations while working on some studies with our colleague Alicia Townsend, at the time at Univ. of North Texas. Lexicor funded these projects and now the arrangements are such that I can’t disclose more than was published in the abstracts from our talks at ISNR and AAPB.  I did at least want to point to these very preliminary findings because theoretically they are in concert with your explanations.

First, we explored 10 participants between the ages of 65 and 85 were recruited at the University of North Texas Health Science Center.  Each was diagnosed by the Alzheimer’s Disease Assessment Scale and a medical interview.  The aim of the study was to identify current source density markers in AD.  EEG recording of the eyes closed condition of an AD group was compared to an age-sex matched control group using within-subject multiple t-test procedures. sLORETA difference maps in nine frequency bands were investigated. Interestingly the results showed that there was a significant increase in current source density in the delta and theta bands in the Brodmann Area (BA) 39 of the right temporal lobe and BA 31, the cingulate gyrus respectively.  Additionally there were decreases in alpha in the BA 21 of the right temporal lobe and right inferior parietal lobule (Sherlin, Townsend & Hall, 2006).

This was corroborative previous findings of increased delta and theta and decreased alpha from a single case study of AD I analyzed with Tom Budzynski  (Budzyski, Budzynski, & Sherlin, 2002).  Results varied from previous studies that showed diffuse differences although the temporal lobe slowing is replicated.  We recognized that the proximity of the significant locations to the precuneus and fusiform gyrus which are both important in facial recognition and processing social information.  The precuneus is also involved in episodic memory retrieval and imagery of motor functions. A correlation study found similar patterns with sLORETA.

I believe that future investigation for patterns in different types of dementia (vascular vs. alzheimer’s vs. frontal lobe vs. mild cognitive impairment) may increase our ability to differentially diagnose.

The second study we completed was to examine the relationship between memory loss and brain electrical activity that was not AD diagnosable. Eighty-four participants between the ages of 50 and 85 were recruited for the original study. Participants were administered the Alzheimer’s Disease Assessment Scale – Cognitive (ADAS-Cog), a QEEG, and a clinical interview. The cross spectra was averaged and LORETA correlation maps.  Correlations were computed for each individual’s ADAS-Cog score compared to each voxel (7×7x7 mm) of their baseline sLORETA.

What we found were significant positive correlations between ADAS-Cog scores and frontal and parietal delta activity, and theta activity in the precuneus. Significant negative correlations were found between ADAS-Cog scores and temporal alpha. This corroborated prior findings and further alluded that as our memory continues to become impaired we expect frontal and parietal delta as well as anterior midline theta to increase. And that alpha will decrease as impairment grows (Townsend, Sherlin & Hall, 2006). This is exactly as you reported as expectations in the EEG.

Budzinski, T., Budzinski, H., & Sherlin, L. (2002).  Short and Long Term effects of Audio Visual Stimulation (AVS) on an Alzheimer’s Patient as documented by Quantitative Electroencephalography (QEEG) and Low Resolution Electromagnetic brain Tomography (LORETA) [Abstract].  Journal of Neurotherapy. Vol 6:1.

Sherlin, L. ,Townsend, A., & Hall, J. (2006). LORETA Analysis of Alzheimer’s Disease. [Abstract].  Journal of Neurotherapy. Vol 9:4.

Townsend, A., Sherlin, L., & Hall, J.  (2006).  LORETA and QEEG Correlations with the Alzheimer’s Disease Assessment Scale. [Abstract].  Journal of Neurotherapy. Vol 9:4.

There recently has been some discussion regarding the use of low resolution brain electromagnetic tomography or LORETA, sLORETA and eLORETA. I felt compelled to make a few comments regarding this since there may be some confusion of how LORETA works and the usage of LORETA as an inverse solution specifically the licensing agreements of the KEY Institute for Brain-Mind Research at the University Hospital of Psychiatry, Zurich.

My intention is to very briefly explain the license agreement so that the end user can be informed. I’ll do so in an informal way by telling the story of the implementation of these methods from my perspective. For a more formal description of the use of LORETA families and some examples you can see a recently written chapter 4 by myself (Sherlin, 2009) in the latest edition of the book Introduction to Quantitative EEG and Neurofeedback edited Budzynski, Budzynski, Evans & Arbarbanel.

In 2000 I had the great privilege to visit with Roberto Pascual-Marqui PhD, the developer of the LORETA family, with my colleague and fellow student Marco Congedo. At this time the LORETA-Key software (Pascual-Marqui, 1994, 1999), had not been widely distributed and utilized in the United States. Marco had significant interest in using LORETA for visualizing brain activity and for exploring newer methods for neurofeedback and had many questions for Roberto. So upon the invitation of Roberto, Marco found funding to travel to Zurich and learn the details from the creator and I happen to be standing in the right spot at the right time. Roberto Pascual-Marqui trained us extensively on how to use his software, named LORETA-Key, which had been already released as free academic software. The LORETA-Key software is a collection of independent modules that the user must run in sequence in order to get from raw EEG to LORETA images.

Upon our return after learning the mechanics of performing an analysis I wanted us to develop a mini-program that would perform the extensive steps necessary in the LORETA-Key software all at once taking care of all possible options in a clear and understandable manner. The analysis was very labor intensive with many batch steps running many different modules. The room for error was great and the user had to be tedious and detailed and still the process would take significant time. With some already existing programming skills Marco wrote a very clean program that would run the LORETA-Key modules after the user had input the necessary data details. This program we called the Workstation (Congedo, 2000). Upon the recommendation of our primary professor and supervisor Joel Lubar, PhD, we formed a company to distribute this program to the larger clinical field of neurofeedback and QEEG providers. The company was named Nova Tech EEG representing our mission and goal of providing “New Technology for EEG”. This very straightforward program, Workstation (released in 2001), was a hit because it allowed clinically oriented users the ability to perform LORETA analysis with a greater ease and utilize this incredible tool in performing and understanding the current source density localization in their subjects and clients, preventing possible errors operating the research-oriented LORETA-Key program. Later we created several other software tools and interestingly revolutionized the development of applications in the neurofeedback industry. Prior to the Workstation (Congedo, 2000) and later the EEG Editor (Congedo, 2001) and finally EureKa! (Congedo, 2005), many of the analysis software were still compiled in the DOS operating system despite many newer developer environments integrating Windows 32 gadgets being released. This made our applications more convenient, professional and provided higher visual acuity in displaying the EEG and the spectral output. It should be noted that since 2005, Nova Tech EEG has also released all of the in house software as freeware, stating unequivocally that our goal was to make inverse solution tools as accessible as possible in the neurofeedback community.

For some time these software along with the original LORETA-Key package were the only ones available for computing LORETA output “all-at-once”. Meanwhile, myself and Marco developed the first adult normative database for LORETA current density in the frequency domain using non-parametric statistics (Congedo and Lubar, 2003). Other third party commercial entities developed programs that would perform LORETA analysis seamlessly calling up the LORETA-Key modules, although there were considerable differences in the applications and the output options. It is worth noting that the directions for seamless integration of the LORETA-Key modules into third-party software have always been clearly explained in the help files of the LORETA-Key software. At some point through the development of third party software the KEY Institute and Pascual-Marqui felt it necessary to restrict the licensing of the software due to apparent misuse. In the June 2007 release of the LORETA-Key software there was a new screen following the previous End User License Agreement (EULA), screens which required the user to agree not to misuse the LORETA software. This screen reads:
This software computes LORETA from scalp electric potential differences (time domain EEG/ERP) or from EEG cross-spectra (frequency domain). One particular very incorrect usage is to cheat LORETA with the input.

Examples of misuse:
1. Inputting scalp electric potential spectral powers will not output LORETA (current density) spectral powers.
2. Inputting scalp electric potential square roots of spectral powers will not output LORETA (current density) square roots of spectral powers.
3. Inputting scalp z-transformed-maps will not output LORETA (current density) z-transformed-values. The three previous invalid inputs to LORETA violate the mathematics and the physics underlying all computations.

Furthermore, they violate any correct usage of statistical analysis. Some more technical details can be found in:
1. For time domain computations: Pascual-Marqui RD: Review of methods for solving the EEG inverse problem. International Journal of Bioelectromagnetism 1: 75-86, 1999.
2. For frequency domain computations: Frei E, Gamma A, Pascual-Marqui R, Lehmann D, Hell D, Vollenweider FX: Localization of MDMA-induced brain activity in healthy volunteers using low resolution brain electromagnetic tomography (LORETA). Human Brain Mapping 14: 152-165, 2001. See text and equations on pages 154-155 therein (Pascual Marqui, 2009) .

It wasn’t long after a wider spread distribution of the LORETA software and use in the community of QEEG and neurofeedback that the natural question arose of if the current source density of interior cortical areas could be operant conditioned in the same manner as the scalp neurofeedback was being conducted currently. This was actually our first concern as announced during a workshop at ISNR by Joel Lubar,  Marco Congedo, David Joffe and Leslie Sherlin (Lubar, Congedo, Joffe, & Sherlin, 2001). The workshop was the starting point for a 3 year project which would be Marco’s dissertation where he demonstrated and verified that in fact the deeper structures could be trained using “LORETA feedback” (Congedo, 2003). This work was published the following year in IEEE Trans. in Rehabilitation Engineering and Neuronal systems (Congedo, Lubar and Joffe, 2004) and is still today a pioneering study in multi-channel neurofeedback. Dr. Lubar’s lab continued to pursue these techniques with additional validation studies (Cannon et al, 2007, 2009). Currently the method is used in several other universities and is becoming available in several neurofeedback systems.

Newer methods were developed by Pascual-Maqui in 2002 and it was named standardized LORETA or sLORETA (Pascual Marqui, 2002). This new implementation had to its advantage the ability to localize test point sources with zero localization error in the absence of noise, which had not previously been accomplished. Since my goal here is not to distinguish the difference in the methods I will skip over these technical issues. It will suffice to say that despite the name, from a mathematical point of view sLORETA is very different from the old LORETA method, and much more accurate. The sLORETA-KEY software was released by Dr. Pascual-Marqui once again as free academic software, but now there were new licensing agreements. As compare to the previous package there was an additional clause that, “This free academic software package is intended for use in research… If you install and use this software, you have then accepted the “license agreement”, and from then on, by law, clinical use and commercial use are strictly forbidden (Pascual Marqui, n.d. b)” This means that any use of the sLORETA package as published and distributed by the KEY Institute cannot be utilized outside the scope of research only. The use of this package cannot be used for creating clinical reports. Not less important, the sLORETA-Key software modules computing sLORETA images cannot be called upon anymore by third party software. The days of utilizing third party software for automatic processing of LORETA data were over to protect the software against misuse and profiteering.

It wasn’t long after the original publications of Pascual Marqui (Pascual Marqui, 2002) on the new sLORETA method that others began to independently replicate the sLORETA work (Congedo, 2006; Congedo et al. 2006; Wagner et al, 2006). This, or any other, independent replication does not fall under the same licensing restrictions because the independently replicated sLORETA algorithms are generated independently, that is, they do not use the implementation in the sLORETA software package distributed as freeware from the KEY Institute. One implementation of this sLORETA transformation was in the EureKa! Software. It made use of the same head model of the old LORETA-Key software, making the sLORETA computation as straightforward as LORETA computations!  Due to the fact that the head model implemented in the new sLORETA-Key package is not open, the older LORETA-Key viewer had to be utilized rather than the newer sLORETA viewer but the computations using sLORETA in this way are completely legal and ethical. The end user must comply with the EULA of the EureKa! Software but this does not violate the KEY Institute’s EULA. This is only one example but I am aware of at least one other legal and ethical such use by a third party and that being the Mitsar Company of St. Petersburg, Russia. In fact the Mitsar Company has recently implemented their independent replication of the sLORETA algorithm in their neurofeedback software BrainTuner. In a recent study that is still under data examination, the clinical reports of using this modality with the sLORETA implementation was overwhelmingly positive (Ozier, Whelton, Mueller, Lampman, & Sherlin, unplublished dissertation in progress).

Recently, we have even successfully replicated and implemented independently the newer eLORETA method, which represents a further improvement over sLORETA in the EureKa! software.

So in summary, the end user must be aware and of course operate within the EULA of the software they are implementing whether for analysis or feedback. I might add that all of the implementations that I am aware of require that the end user understands and agrees that he/she is ultimately responsible for the use of the software; and that the distributor is not responsible and cannot be held responsible for any acts outside the intended use.

References

Cannon R., Congedo M., Lubar J.F., Hutchens T. (2009). Differentiating a network of
executive attention: LORETA neurofeedback in anterior cingulate and dorsolateral prefrontal cortices. International Journal of Neuroscience, 119, 404-441.

Cannon R., Lubar J.F., Congedo M., Thornton K., Towler K., Hutchens T. (2007), The
Effect of Neurofeedback Training in the Cognitive Division of the Anterior Cingulate Gyrus, International Journal of Neuroscience, 117(3), 337-57.

Congedo, M. (2000). Workstation (Version 1.0). Knoxville, TN: Nova Tech EEG, Inc.

Congedo, M. (2001). EEG Editor (Version 1.0). Knoxville, TN: Nova Tech EEG, Inc.

Congedo, M. (2003). Tomographic Neurofeedback; a new Technique for the
Self-Regulation of Brain Electrical Activity. University of Tennessee, Knoxville.

Congedo M., Lubar J.F. (2003), Parametric and Non-Parametric Normative Database
Comparisons in Electroencephalography: A Simulation Study on Accuracy, Journal of Neurotherapy, 7(3/4), 1-29.

Congedo, M. (2004). sLORETA zero-localization error as seen in a point spread functions: an animation Retrieved April 29, 2009, from http://www.lis.inpg.fr/pages_perso/congedo/sLORETA.htm

Congedo M., Lubar J.F., Joffe D. (2004), Low-Resolution Electromagnetic
Tomography neurofeedback, IEEE Trans. on Neuronal Systems & Rehabilitation Engineering, 12(4), 387-397.

Congedo, M. (2005). EureKa! (Version 3.0). Mesa, AZ: Nova Tech EEG, Inc.

Congedo M., Lotte F, Lécuyer A. (2006), Classification of Movement Intention by
Spatially Filtered Electromagnetic Inverse Solutions, Physics in Medicine and Biology, 51, 1971-1989.

Congedo M. (2006), Subspace Projection Filters for Real-Time Brain
Electromagnetic Imaging, IEEE Transactions on Biomedical Engineering, 53(8), 1624-34.

Congedo M., Joffe D. (2007), Multi-Channel Spatial Filters for Neurofeedback. In
“Neurofeedback: Dynamics and Clinical Applications “, (Ed) Evans J., Haworth Press, New York,

Lubar, J. F., Congedo, M., Joffe, D., & Sherlin, L. (2001). LORETA 3-D Neurofeedback, Normative Database and New Findings. Paper presented at the Society for Neuronal Regulation.

Ozier, D., Whelton, W., Mueller, H., Lampman, D., & Sherlin, L. (unpublished). Comparing the efficacy of thermal biofeedback and sLORETA neurotherapy as interventions for chronic pain., University of Alberta, Edmonton.

Pascual-Marqui RD, Michel CM, Lehmann D. (1994). Low resolution electromagnetic
tomography: a new method for localizing electrical activity in the brain. International Journal of Psychophysiology, 18:49-65.

Pascual-Marqui RD. (1999). Review of Methods for Solving the EEG Inverse
Problem. International Journal of Bioelectromagnetism, 1:75-86.

Pascual Marqui, R. D. (2002). Standardized low-resolution brain electromagnetic
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