Developing Treatment Protocols
The development of training protocols in neurofeedback has been the result of an evolution of training practices, some growing out of early observational results related to research studies and some developing from attempts to elicit responses in the human or animal EEG that were either atypical or were an attempt to enhance already existing characteristics. In this section we will explore the history of this evolution and some of the significant milestones that have guided clinical applications of neurofeedback during its 60+ year history.
The term protocol, when used in relation to training means “the process of bringing a person, etc., to an agreed standard of proficiency, etc. by practice and instruction” (ReversoDictionary – an online resource). This definition works quite well for neurofeedback training and is an excellent description of the process. Neurofeedback training is, indeed, a training process that utilizes reinforcement and instruction to enable an individual to develop improved proficiency or skill in a particular cognitive, mental or central nervous system activity related task.
Another definition of protocol, from the National Institutes of Health (NIH) is as follows: “A detailed plan of a scientific or medical experiment, treatment, or procedure. In clinical trials, it states what the study will do, how it will be done, and why it is being done.” This is obviously a more medically or research-oriented definition and suggests a more rigorously organized plan of training where the clinician or researcher has identified an issue or condition and has a specific approach to apply to its resolution.
Generally accepted definitions of neurofeedback suggest that it is a process of operant conditioning leading to self-regulation of brain activity. Marzbani and colleagues (2016) state that “Neurofeedback is a kind of biofeedback, which teaches self-control of brain functions to subjects by measuring brain waves and providing a feedback signal”.
However, Ute Strehl, (Strehl, 2014) writes that the learning process associated with neurofeedback requires more than operant conditioning and simple feedback and that we need to also understand the influence of classical conditioning, skill learning and motivational aspects. She goes on to note that all types of learning, including neurofeedback are the result of trial and error, conscious and unconscious responses to events including feedback and reinforcement and an awareness of the results of such efforts. To facilitate the ability to incorporate these new skills into everyday life, known as generalization, we need to incorporate a behavioral therapy approach to the process of neurofeedback training. Therefore, our analysis of the development of training protocols will include an evaluation of which protocols may more effectively meet the needs of the client or participant in light of the learning requirements noted above.
In the field of neurofeedback, the term protocol has come to mean a set of training guidelines that can be applied based upon certain assessment criteria or in some cases a simple diagnostic category, such as attention deficit hyperactivity disorder (ADHD). This development of preset training approaches has been helpful by allowing new practitioners to begin training clients without necessarily understanding all of the science behind the choices made in a particular training approach. However, the same ease-of-use has sometimes led practitioners with minimal experience to apply training approaches without proper assessment and without appropriate monitoring to determine the efficacy of a training approach. Additionally, such practitioners may not recognize negative consequences of inappropriate training and so the development of standardized, diagnosis-based training protocols has in some cases been a mixed blessing. Therefore training protocols, if utilized in neurofeedback training, should be done with an understanding of why such approaches could be appropriate for an individual client, and this decision must be based upon adequate assessment tools and a level of licensure, certification, training and experience that qualifies the practitioner to work with each client.
History of Neurofeedback Training Protocols
insert Berger picture and caption Two early researchers in in brain activity were Joe Kamiya and Barry Sterman. Kamiya began by exploring the alpha rhythm, which was the first EEG pattern identified by Hans Berger in 1929 and is generally identified as 8-11 or 8-12 Hz.
Kamiya (1968) worked with subjects to determine whether they could identify alpha activity and whether they could develop volitional control to create an increase in voltage in the alpha activity being measured. His earliest experiments began in 1956 and are described in the book Neurofeedback The First Fifty Years (Evans, JR, Dellinger, MB & Russell, HL, eds., Academic Press, 2020 Chap. 1). These experiments at the University of Chicago, involved presenting a direct feedback signal to the subject to indicate a specific EEG behavior, in this case an increase in alpha amplitude above a set threshold. This was likely the first instance of EEG training in history.
Kamiya’s work would eventually lead to work at the Menninger Foundation with Elmer and Alyce Green and Dale Walters and subsequently, following Eugene Peniston’s experience at Menninger, Peniston’s alpha-theta training protocol (Peniston, E. G., Kulkosky, P. J., 1989, 1990) and other alpha related EEG training such as Fehmi’s work with alpha synchronization (Fehmi L.G. and Robbins J., 2007), Budzynski’s work with what he termed ‘twilight learning’ (Sittenfeld, P, Budzynski, T., & Stoyva, J., 1976, Budzynski, T. H., 1977, 1996), Hardt’s work with alpha training (Hardt J.V. and Kamiya J, 1978, Hardt J.V., 2007), and Scott’s work with addiction disorder treatment programs, as well as many other practitioners and researchers exploring clinical and research applications.
Out of this history of alpha and alpha related training, the protocol most often used in current clinical practice is Peniston’s alpha-theta protocol, including various individual adaptations and modifications of his original approach. This approach will be covered in detail in the section on training protocols.
Barry Sterman’s EEG work initially began with cats. He credits his earliest efforts in the study of the EEG to the Russian physiologist Ian Pavlov, who developed the theory of classical conditioning (Pavlov, 1927). Sterman’s initial interest was related to the concept of internal inhibition (Sterman, 1996) in its relationship to sleep onset and he decided to utilize EEG recordings to evaluate the behavior of cats. He attempted to use operant conditioning techniques instead of the classical conditioning used by Pavlov (Ross, Sterman, and Clemente, 1967; Sterman and Wyrwicka, 1967; Wyrwicka and Sterman, 1968).
Sterman and his colleagues worked with cats who were first trained to press a bar to receive a food reward. They then introduced a tone that was associated with the withholding of the food reward. If the cat pressed the bar while the tone was on, the tone would be prolonged but if the cat waited until the tone stopped and pressed the bar, the food was delivered immediately. In his 1996 paper describing the history of his work with EEG, he reports two behaviorally specific EEG patterns emerging from the study. One pattern was related to the learned suppression of the bar press response during the presentation of the tone and the other was related to the experience of the reward, following the completion of a correct response. The pattern associated with the suppression of bar pressing behavior was in the 12-20 Hz frequency band, seen within the sensorimotor cortex. Because of the localization to the sensorimotor cortex, Sterman reports labeling this rhythmic activity the sensory motor rhythm, or SMR. He noted a similarity between this pattern and the EEG pattern seen during sleep, known as sleep spindles, that typically show a rhythmic burst of activity in the 12-15 Hz range.
The pattern he noted in association with the food reward was a 4-12 Hz rhythm in the posterior dorsal cortex, along the midline at approximately the 10-20 system coordinate Pz. He labeled this EEG behavior post reinforcement synchronization or PRS.
Noting that these were the 2 most clearly defined patterns seen in the trained cats, the decision was made to attempt to operantly condition activity associated with these findings. The initial training focused on the SMR pattern. A signal detection filter centered at 13 Hz was set to activate the feeder when a defined amplitude and duration of 12-14 Hz activity occurred to trigger a relay (Sterman, Wyrwicka and Roth, 1969; Wyrwicka and Sterman, 1968). The cats were found to be amenable to this training and they learned to produce this rhythm voluntarily. He reports that between 150 and 200 conditioned responses could be elicited prior to the cats becoming full and no longer interested in further food rewards.
The behavior associated with this conditioned response was a suppression of motor activity; not simply physical stillness, but a progressive process beginning with a reduction in muscle tone, followed by sustained immobility and then subsequently by the increased production of the 12-15 Hz rhythm.
Further studies identified the origin of the activity to occur in the somatosensory relay nuclei of the thalamus known as the ventral basal nuclei (Howe and Sterman, 1972). Further studies indicated that during the production of increased SMR activity, the behavior of these ventral basal relay cells changed from what was described as random bursting, which more recent knowledge of neurophysiology shows to be associated with the transmission of incoming sensory input, to a more rhythmic pattern that suppressed somatosensory information conduction (Howe and Sterman, 1973).
The thalamus to cortex relay system, or TCR is a complex system that regulates the transmission of afferent information pathways, through the thalamus and specific thalamic nuclei, to the cortex. A variety of neurochemical relationships either facilitate the transmission of ascending sensory information or serve to block this information by supplanting it with rhythmic patterns of activity that are transmitted to specific cortical regions such as the somatosensory cortex and the parietal and occipital areas associated with visual processing. This rhythmic activity, when seen in the visual processing areas is generally known as the alpha rhythm, also known as the posterior dominant rhythm (PDR). Depending on the state of the cortical neurons receiving these bursting patterns, they may respond by firing synchronously with this incoming input. This primarily depends upon what other tasks these cortical neurons may be engaged in and other characteristics that determine level of excitation.
The association between these findings and the experimental results with the cats led Sterman and his colleagues to conduct multiple studies with cats and primates to determine the EEG correlates of a variety of lesions at various points in the somatosensory pathway (Sterman 1996). The same mechanism that facilitates the oscillatory discharges that include the SMR rhythms also, when propagated for longer periods of time and when associated with fatigue and drowsiness can lead to increased rhythmicity, synchronization and slower EEG frequencies in the range that is typically identified as theta, generally in the 4-8 Hz range.
Much of the information that Sterman and his colleagues developed from these studies presented entirely new information at the time but which has now become accepted science in the understanding of degrees of arousal and inhibition and their association with EEG patterns seen on the scalp surface, as well as the behavioral and attentional characteristics associated with them.
Through a serendipitous series of events (Sterman, M. B., et al., 2010), some of the cats that had been trained in previous studies were subsequently included in a study of the toxic effects of a component of rocket fuel known as monomethyl hydrazine (MMH). The cats trained to produce increased and sustained sensorimotor rhythm activity showed a significant reduction in reactivity to the chemical compound, including increased latency prior to the onset of seizure response compared to untrained cats.
Initially, the results of this research were not available to the public, as it was conducted under the auspices of the US Air Force and therefore it was not published in typical journals. However, it has now become available and serves to underpin the subsequent studies regarding the apparent protective effect of SMR training for individuals with seizure disorders.
The interesting finding from this early research into the effect of MMH was the significant delay or even absence of convulsions in the cats that were trained to produce increased SMR voltage and duration compared to those cats not trained in this activity.
Sterman notes that these cats had not received SMR training for over 3 months prior to the drug test and therefore any effect was that which was retained by the cats in the absence of recent training. His conclusions (historical archives included in Sterman, 2010) were that SMR training can provide an effective protection against the most devastating consequences of this compound, i.e. seizures.
Subsequently, Sterman published a paper in 1972 (Sterman, MB, Fryer, L, 1972) that noted that a localized rhythm similar to the cat SMR can be recorded from the sensorimotor cortex of humans with surface electrodes and that this rhythm can be trained using operant conditioning techniques through EEG biofeedback. The results of this training showed an increase in the amplitude and persistence of this rhythm and a greater differentiation from other cortical EEG rhythms. Because of their success with EEG operant conditioning in the cat and its positive effect on reducing seizure activity caused by chemical exposure, they decided to try SMR biofeedback training with a subject with a 7-year history of convulsive disorder. Previous examination of this individual showed no localized lesions and reported generalized slowing but no proximal discharges during a typical EEG recording. Immediately prior to the beginning of training, the client was experiencing seizures approximately 2 times per month and had tried several anticonvulsant medications.
After the 3rd session of neurofeedback training, the client demonstrated the beginning of control of the SMR response and following this 3rd session there were no seizures reported for 3 months. Training of course continued and the client began showing changes in other areas of her life as well, becoming more outgoing, showing increased personal confidence and an enhanced interest in her appearance. She also reported improved sleep and an easier time of waking up in the morning. She had one small seizure after 3 months and then had no additional seizures for the rest of the training.
Subsequent studies by Sterman and others has resulted in over 25 years of peer-reviewed, published research demonstrating the effectiveness of neurofeedback training for seizure reduction. However, a review article published in 2002 (Monderer, RS, et al., 2002) concludes that “In the absence of any rigorously controlled studies, the relationship between neurofeedback and seizure frequency cannot be firmly established. Despite these limitations, the promising role of neurofeedback as a treatment for epilepsy is illustrated”.
Defining an effective training protocol Difficulties with establishing the efficacy of a particular behavioral intervention have been covered in the Research Evidence Basis for Neurofeedback section. However it is useful to note in regard to this review article (Monderer, RS, et al. 2002) that they cite multiple research studies that utilized treatments other than the sensorimotor rhythm training, including slow cortical potential training, training to inhibit epileptiform activity in the EEG and several other techniques. They also did not evaluate specific training approaches and whether interventions were applied appropriately. In light of Strehl (2014), it appears clear that simply applying operant conditioning techniques without appropriate additional components noted in her paper may result in reduced effectiveness for an otherwise useful treatment.
In an attempt to elucidate the factors associated with effective neurofeedback training, Rogala and colleagues (Rogala, J, et. al., 2016, Frontiers in Human Neuroscience) identify some of the “do’s and don’ts” of effective neurofeedback training. Included in the list of approaches with positive effects is the use of more specific electrode locations that are chosen to be associated with known sources of the particular EEG frequency activity being trained, for example, frontal midline theta or posterior alpha activity.
The use of multiple electrodes in the general area identified as a source of this activity is also recommended, to represent the source areas more completely. They suggest that neurofeedback clinicians and researchers should identify optimal training electrode locations based on anatomical and functional studies and to develop protocols that may use a weighted average approach for the input from a variety of electrode locations.
Suggestions from the same paper of what not to do, include avoiding training multiple EEG frequencies at the same time to avoid confusion and cross frequency interactions that may result in effects other than those desired for the training protocol. This suggestion did not have much basis in research and was merely an observation that studies involving multiple frequencies appeared to have less robust effects.
Ultimately Rogala and colleagues found that lack of specificity in training approach seemed to elicit less clear and easily measurable results. However they did not conclude that improved behavioral results were attributable to this narrower and easily measured focus, thus making their attempts to define the best approach somewhat inconclusive.
So, how do new practitioners learn the prevailing neurofeedback approaches? There are many ‘schools of thought’ in neurofeedback with sometimes widely varied methods, types of equipment, clinical populations, etc. In clinical practice, individuals providing neurofeedback training generally choose their training protocols based upon instructions from trainers and mentors and therefore their approaches reflect these influences. In light of what may be considered a type of apprenticeship approach to the study of neurofeedback, that is a common entry point for new practitioners, it is worth discussing the various schools of neurofeedback training and how they have evolved and contributed to the development of the field of neurofeedback.
Primary schools of thought in neurofeedback training
A variety of clinical approaches have been developed by various individuals and in some cases by research groups and the following discussion will try to identify the main proponents of each approach. This is not an exhaustive treatment but will attempt to hit the high points.
One of the earliest practitioners of neurofeedback training was Margaret Ayers, who developed her initial interest in EEG as a student of Barry Sterman, while working with him at the Veteran’s Administration Center in Sepulveda CA. She went on to developed EEG training protocols mainly by evaluating subtle patterns in the EEG of each client, that she was able to see with an enhanced neurofeedback system developed out of software created for missile guidance systems. She claimed a sample rate of more than 25,000 samples per second, which allowed for the identification of patterns that she then correlated with known medical diagnoses (Ayres, M & Montgomery, P, 2007). She based her approaches on her knowledge of neurophysiological characteristics of the brain in correlation with client symptoms (Montgomery, P, 2019). One of her most important contributions was with individuals with traumatic brain injuries, including those in persistent coma states (Ayers, M 1977, Byers, AP, 1998). Her training approaches were mainly focused on inhibition of unwanted activity, primarily in the theta frequency band. Her partner, Penny Montgomery continues her work to this day, continuing the training primarily focused on inhibiting unwanted frequencies in the client’s EEG.
Following in the footsteps of Barry Sterman, Joel Lubar at the University of Tennessee proceeded to replicate and validate Sterman’s work with seizure disorders. In 1976, he published a study of 8 individuals with epilepsy who were trained to increase SMR activity (12-14 Hz) and reduce or inhibit 4-7 Hz. Patients showed improvement generally, with two of the participants with the most severe epilepsy, becoming seizure free for periods of up to one month (Lubar, JF, Bahler, WW, 1976). Seizure intensity and duration also decreased in other participants. There was a correlation between participants showing increases in the amplitude of SMR during the training period with decreases in seizure activity.
In 1981, Lubar published a study (Lubar, et. al., 1981) using a controlled, double-blind crossover design that showed similar results in experimental participants compared to controls.
During the years of studying the effects of SMR training, Lubar and his colleagues noted improvements in behavior and cognitive function similar to those noted by Sterman and decided to study the effect of such training with individuals diagnosed with what was then called “minimal brain dysfunction syndrome”. This condition later was labeled hyper kinetic disorder and, more recently, attention deficit hyperactivity disorder (ADHD).
Again, using an ABA study design, Lubar and colleagues noted that behaviors improved with increases in SMR and decreases in 4-7 Hz activity and that problematic behaviors returned when the training was reversed, and then again improving following a subsequent return to the correct training (Lubar, JF, Shouse, MN, 1976; Shouse, MN, Lubar, JF, 1979).
This finding led to the widespread use of neurofeedback training for ADHD and a number of other behavioral and cognitive function issues. Rewarding increases in 12-15 Hz and rewarding decreases in 4-7 or 4-8 Hz became common practice. Multiple studies have been published, demonstrating positive effects of such training in a wide variety of clinical, research and even school environments (Anderson, 1994-1998, unpublished project report manuscripts).
Vincent Monastra followed Lubar’s work with an assessment process for ADHD that utilizes the ratio between the amount (voltage, amplitude and/or power) of 4-7 Hz theta and 13-21 Hz beta, measured from various locations, most typically along the midline and generally in the anterior midline near the 10-20 system location Fz. This is known as the theta/beta ratio or T/B ratio and has been the subject of several large, multi-site studies to determine both its sensitivity (the ability of an assessment measure to identify individuals known to have the condition or diagnosis for which the measure was developed) and specificity (the ability of the measure to accurately differentiate those with the condition from those who do not have the condition). The best of these studies in terms of research design, multi-site participation and degree of control of variables (Snyder, et. al., 2006) showed a sensitivity of 87% and a specificity of 94%, for a combined overall accuracy of 89%. The next best assessment tool commonly used for ADHD screening, the Connors Rating Scale for teachers, showed a combined score of 58% and other methods were even less accurate.
Monastra’s work led to a technique for training clients with ADHD that simply trained for desirable levels of the T/B ratio rather than training the individual components of that ratio with narrower frequency band choices such as 12-15 Hz or 15-18 Hz that others were using to target the activity or behavior of interest more specifically. Many practitioners have used the T/B ratio training approach and reported success with their clients. However, others suggest that it lacks the specific focus required for the best effect.
Two prominent individuals, who have contributed substantially to the field and have a large following are Susan and Siegfried Othmer. They initially studied with Margaret Ayers when she worked with their son, Brian, who experienced temporal lobe epilepsy. After several transitions, they developed a training institute and proceeded to work with clients and train new practitioners. In the early years of the 1990s, the training they offered clinicians generally stayed with the standard operant conditioning concepts taught by Sterman, Lubar and others, with a fairly basic Cz midline central electrode location (personal experience, John Anderson, 1992) and training protocols consisting of rewarding increases in either 12-15 Hz or 15-18 Hz and inhibiting increases in 4-8 Hz and faster beta frequencies in the 20-30 Hz range. This evolved into an approach that utilized an attempt to balance training between left and right hemispheres by generally training to increase 15-18 Hz at C3, the left side sensory motor area and 12-15 Hz at C4, the right side sensory motor area. The amount of time spent in an individual session at one or the other of these sites was ‘titrated’ specifically for each client.
After some experience with this approach, training began to include the use of a ‘bi-polar’ or sequential montage, rather than the referential or ‘mono-polar’ montage that used an ear reference (negative electrode) with the positive (active) electrode on the scalp position. The bi-polar or sequential montage involved placing the positive electrode on one scalp location and the negative electrode on another scalp location. Locations were initially C3-C4 or T3-T4 but continued to evolve from practice and experimentation to include combinations of either right side or left side locations, anterior to posterior combinations and other combinations and frequency bands.
In light of this expanding complexity, Susan Othmer developed a ‘protocol guide’ in a flow-chart design with a ‘start here and progress’ to another combination of sensor locations and frequency bands and so on until the most effective combination for that client was found. They began to call this optimal response frequency (ORF) training (Othmer, S, in Evans, 2020).
One component of this training was an effort to find the optimal reward frequency within an individual training session by shifting the reward band to higher or lower values. Often these values would end up in very low frequencies, in the theta and delta ranges and this caused a great deal of condemnation from others in the neurofeedback community. Charges were made that the Othmers were rewarding frequencies that would trigger seizures and other concerns were expressed (personal experience, 1996-2001).
Some of these concerns were due to ignorance of the nature of common mode rejection (CMR), which is covered in detail in the instrumentation and electronics section of this program. The aspect of CMR that is important when using a bi-polar montage is that one is not actually training for an increase or decrease in voltage or synchronous activity. The effect of CMR in this context results in training for increased difference between the rewarded activity between the two locations.
This is because, with CMR, the amplifier amplifies that which is different between the signals at each active sensor location and rejects anything that is the same. Because of this, the apparent increase in 12-15 Hz for example, represents a change in the relative amount of this frequency at one or both sensor locations. This could result from a variety of factors but CMR is primarily sensitive to phase relationships between the EEG waveforms at each pair of sensors (whether the waves are in phase, waving synchronously, e.g., up and down at the same time, or out of phase by 180 degrees or somewhere in between) and somewhat less sensitive to the relative voltage at each location. So if voltage increases or decreases in both sensors by the same amount and phase remains the same, there will appear to be no change in voltage and if voltage stays the same but phase changes, then the apparent voltage will change in the displayed signal. This is demonstrated by the simple table below. The actual mechanism of CMR is more complex but the basic concepts are valid.
Therefore, the training the Othmers were recommending and also using in their clinical practice was to promote greater difference between sensor locations. The theory put forth to explain the effectiveness of this approach was that, to accomplish this task, the client’s brain must communicate internally and exercise greater control and coordination between these training locations. This would then result in better self-regulation in the central nervous system generally. The Othmers and their students applied these approaches and reported excellent results, often supported by objective testing, mostly based on continuous performance tests, which are discussed in the assessment portion of this program. There were several published studies of results using this or similar training approaches at this phase of their work (Putman, J.S., et. al., 2005; Kaiser, D.A., & Othmer, S., 2000; Othmer, S., Othmer, S.F., & Kaiser, D.A., 1999; Kaiser, D.A., 1998; Linden, M., et. al., 1996; Lubar, J.F., 1995)
Ultimately, the evolution of the Othmer method led to training in a ‘frequency’ below 0.1 Hz, known as infra-low frequencies. This caused even more controversy in the field because these portions of the EEG frequency spectrum are poorly understood and the mechanisms of control of this area of electrophysiology are even less clear. This is also within the frequency spectrum occupied by such artifacts as eye blink, eye movement, cable sway, electro-dermal responses (GSR), electrode drift and other factors. Siegfried Othmer has a PhD in physics and has explained that these issues have been addressed and that the training proceeds effectively in spite of these factors due to proprietary signal processing methods (personal communication, 2021).
There have been multiple published studies regarding this infra-low frequency training as well (Sasu, R & Othmer, S, in Restoring the Brain, Kirk, H.W., ed., 2020; Legarda, S.B., et. al., 2011; Othmer, S., & Legarda S.B., 2011) and other clinicians have also developed their own applications associated with training in these areas of brain electrophysiology below 1 Hz.
Following the move to ILF frequencies training, the Othmers continued to incorporate A-T training, using a two channel sum training approach and more recently have included a two channel synchrony training approach using the alpha and gamma frequency bands.
The Othmers have a large following and many clinics and individual practitioners employ these methods and report positive results with their clients. An explanation of what is actually being trained in the ILF protocol is difficult to come by and must wait for future revelations.
Another prominent clinician in the neurofeedback field, Mark Smith, has also pursued a similar low frequency training that he terms infra-slow frequency training, utilizing different amplifiers and software. He initially trained with the Othmers but then began developing his own approach in developing a feedback signal for activity below 0.1 Hz. As time went on he began to incorporate database training, also known as z-score training, using the Neuroguide database developed by Robert Thatcher. He incorporated quantitative EEG, learning from Jonathan Walker, MD and others. His explanation of his approach to ISF training, in collaboration with Thomas Collura (Smith, et. al., 2014) is that the training involves providing the client with information regarding the phase of the ISF signal rather than the typical amplitude fluctuations of an alternating current EEG frequency derived from a peak to peak digital processing function (see the signal processing section). As noted in the above mentioned article, the time constant is quite long, requiring significant time (3 minutes is noted in the article example) for the filters to return to baseline following a major shift in the signal being measured.
Unfortunately, as noted in the discussion of the Othmer’s ILF training, significant voltage shifts occur with typical eye movement, eye blink, electrodermal and cable sway artifacts. There is no discussion in the article regarding these issues and therefore the validity of the training signal feedback is in question. There may be more to the signal processing than is explained in the article that would mitigate these effects.
Again, the results of training with this approach, using a combination of ISF, z-score, synchrony and “referential enhancement” training have been positive and clinical examples and book chapters (Smith, 2014, 2017, 2018) support continued study.
As mentioned, z-score training developed and began to be utilized by some practitioners in the neurofeedback field.
Robert Thatcher developed the Neuroguide database and in cooperation with Thomas Collura of Brainmaster Technologies, developed Live Z-Score Training (LZT). Beginning in about 1996 (Thatcher, et. al., 2019) they began with a simple 1 or 2 channel approach that quickly developed into a 4 channel approach for training clients by providing them with real time information about how closely their EEG matched a database of age matched, typical controls. There were approximately 72 variables available within the Thatcher database, including power, relative power, phase and coherence values and more for the standard EEG frequencies. The client’s EEG values were compared to these values and differences were displayed in standard deviations. The goal was to train toward z-zero standard deviations.
Subsequent advances resulted in 19 channel, real time surface z-score training and then 19 channel LORETA z-score training, using the LORETA source localization method to train in 3 dimensions. Ultimately, Collura and his colleagues shifted to the E. Roy John database, currently known as BrainDX and then they more recently shifted again to a database known as QEEGPro, which was developed by using client EEG recordings that were “cleaned” of clinical EEG patterns identified by a client questionnaire (QEEG.pro/database/). This approach is based upon questionable assumptions and most qEEG researchers consulted about this expressed skepticism regarding this database approach (personal communication, John Anderson 2019-2021).
Robert Thatcher and his team continued to work with the Neuroguide database and have continued to develop the features of 19 channel z-score training, most recently with the development of swLORETA, a more precise and accurate iteration of the LORETA source localization method. This has resulted in a large base of clinicians using this approach. More than 50 publications have presented evidence of the efficacy of z-score neurofeedback training, suggesting widespread acceptance within the science literature (https://www.appliedneuroscience.com/PDFs/Z_Score_NFB_Publications.pdf).
Other advances in protocols will likely be developed over time as clinicians and researchers use more and more sophisticated tools of hardware and software to define and refine the parameters of this type of training. There are many clinicians and researchers who have made substantial contributions who were not mentioned in this section. Hopefully a representative sample of protocols and their development has been presented allowing students to begin to evaluate the different offerings to be used in their own practices.
Glossary
ABA reversal design: small N design where a baseline is followed by treatment and then a return to baseline.
assignment threat: a classic threat to internal validity in which individual differences are not balanced across treatment conditions by the assignment procedure.
assignment-interaction threat: the combination of a selection threat with at least one other threat (history, maturation, testing, instrumentation, statistical regression, or mortality).
balancing: a method of controlling a physical variable by distributing its effects across all treatment conditions (running half of each condition's participants in the morning and half in the evening).
baseline: a control condition in which participants receive a zero level of the independent variable (sitting quietly without receiving feedback about physiological performance).
baseline-only control condition: participants sit quietly without receiving feedback about their physiological performance.
bidirectional causation: reason that correlation does not imply causation. Each of two variables could influence the other.
case study: a nonexperimental descriptive study of a participant’s experiences, observable behaviors, and archival records kept by an outside observer.
confounding: loss of internal validity when an extraneous variable systematically changes across the experimental conditions.
constancy of conditions: controlling a physical variable by keeping it constant across all treatment conditions (running all subjects in the evening).
contingent feedback: feedback of a participant's actual physiological performance.
control group: in an experiment, a group that receives a receives a zero-level of the independent variable (placebo or wait-list).
correlational study: a nonexperimental procedure in which the researcher does not manipulate an independent variable and only records data concerning traits or behaviors (investigating the relationship between body mass index and severity of low back pain).
demand characteristics: situational cues (like placing EEG sensors on a client's forehead) that signal expected behavior (increased attention).
dependent variable (DV): the outcome measure the experimenter uses to assess the change in behavior produced by the independent variable (airway resistance could be used to measure the effectiveness of HRV training for asthma).
detectable noncontingent feedback: noncontingent feedback that a participant can detect as false when the display fails to mirror voluntary behavior like muscle bracing.
double-blind crossover experiment: an experimental approach using a double-blind design where participants start with one treatment and conclude with an alternative treatment (controls demand characteristics, experimenter bias, and individual differences).
double-blind experiment: the experimenter and participant do not know the condition to which the participant has been assigned to control both demand characteristics and experimenter bias.
effect size: magnitude of an IV's effect on the DV.
elimination: method of controlling a physical variable by removing it (soundproofing a room).
ex post facto study: quasi-experimental design in which a researcher compares participants on pre-existing characteristics.
experimenter bias: confounding that occurs when the researcher knows the subjects' treatment condition and acts in a manner that confirms the experimental hypothesis.
extraneous variable (EV): variable not controlled by the experimenter (room temperature).
file drawer effect: excluding unpublished studies that did not obtain significant treatment effects from a meta-analysis or systematic review.
history threat: classic threat to internal validity that occurs when an event outside the experiment threatens internal validity by changing the DV.
independent variable (IV): the variable (antecedent condition) an experimenter intentionally manipulates (HRV or SEMG biofeedback).
instrumentation threat: a classic threat to internal validity in which changes in the measurement instrument or measurement procedure threaten internal validity.
internal validity: the degree to which the experiment can demonstrate that changes in the dependent variable across treatment conditions are due to the independent variable.
large N designs: studies that examine the performance of groups of participants.
level 1: not empirically supported.
level 2: possibly efficacious.
level 3: probably efficacious.
level 4: efficacious.
level 5: efficacious and specific.
maturation threat: a classic threat to internal validity that occurs when physical or psychological changes in participants threaten internal validity by changing the dependent variable.
mean: arithmetic average and the most commonly reported measure of central tendency.
measures of central tendency: descriptive statistics (mean, median, and mode) that describe the typical score within a sample.
measures of variability: descriptive statistics (range, standard deviation, and variance) that describe the dispersion of scores within a sample and allow us to compare different samples.
median: the score that divides a sample distribution in half. It is the middle score or the average of two middle scores.
meta-analysis: a statistical analysis that combines and quantifies data from many experiments that use the same operational definitions for their independent and dependent variables to calculate an average effect size.
mode: the most frequent score calculated when there are at least two different sample values.
mortality threat: a classic threat to internal validity that occurs when participants drop out of experimental conditions at different rates.
N: number of participants.
No-biofeedback control group: control condition in which participants do not receive physiological feedback.
nonspecific treatment effect: a measurable symptom change that is not correlated with a specific psychophysiological change.
observational studies: nonexperimental procedures like naturalistic observation and correlational studies.
Pearson r: a statistical procedure that calculates the strength of the relationship (from -1.0 to +1.0) between pairs of variables measured using interval or ratio scales.
personality variables: personal aspects of participants or experimenters like anxiety or warmth.
physical variables: properties of the physical environment like time of day, room size, or noise.
placebo response: associatively conditioned homeostatic response.
pre-test/post-test design: researchers measure participants on the DV at least twice, before and after administering training.
pre-registration: submission of a study on the Open Science Framework before data collection.
random sample: a subset of a target population selected using an unbiased method so that every member of the population has an equal chance to be chosen.
randomized controlled trial (RCT): researchers manipulate an independent variable and randomly assign participants to conditions, with or without prior matching on participant variables.
range: the difference between the lowest and highest values.
registration: submission of study design to a journal before data collection for Stage 1 and Stage 2 peer review.
relaxation control condition: participants receive a non-biofeedback relaxation procedure.
reverse contingent feedback: feedback that trains participants to produce changes that are the reverse of those shaped by a clinical protocol (beta decrease and theta increase in children diagnosed with ADHD).
sample: selected subset of a target population.
selection interactions: the combination of a selection threat with at least one other threat (history, maturation, testing, instrumentation, statistical regression, or subject mortality).
single-blind experiment: subjects are not told their treatment condition.
small N designs: studies involving one or two participants.
social variables: aspects of the relationships between researchers and participants like demand characteristics and experimental bias.
specific treatment effect: a measurable symptom change associated with a measurable psychophysiological change produced by biofeedback.
standard deviation: the square root of the average squared deviations from the mean.
statistical regression threat: a classic threat to internal validity that occurs when participants are assigned to conditions on using extreme scores, the measurement procedure is not wholly reliable, and participants are retested using the same method to show change on the DV. The scores of both extreme groups tend to regress to the mean on the second measurement so that high scorers are lower and low scorers are higher on the second testing.
testing threat: a classic threat to internal validity that occurs when prior exposure to a measurement procedure affects performance on this measure during the experiment.
third variable problem: a reason that correlation does not mean causation. A hidden variable may affect both correlated variables. For example, alcohol abuse could both disrupt sleep and increase depression.
variance: the average squared deviation of scores from their mean.
wait-list control group: participants are measured like the experimental group(s) but are placed on a waiting list for an experimental treatment.
z-score training: neurofeedback protocol that reinforces in real-time closer approximations of client EEG values to those in a normative database.
Glossary
AB design: in a descriptive case study, A is the baseline phase and B is the
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Assignment
Now that you have completed this unit, consider how you could use the case study approach in your clinical practice to assess treatment efficacy?
References
Ayers, M. E. EEG Neurofeedback to Bring Individuals out of Level Two Coma. (ABS) Biofeedback and Self-Regulation 20(3). 304–305.1977
Berger H (1929) Über das elektroenkephalogramm des menschen. Arch Psychiatr Nervenkr 87(1):527–570
Budzynski , T. H. ( 1977 ). Tuning in on the twilight zone . Psychology Today , 11 , 38 – 44
Budzynski , T. H. ( 1996 ). Brain brightening: Can neurofeedback improve cognitive process? Biofeedback , 24 , 14 – 17
Byers A.P. Neurotherapy reference library . 2nd ed. Applied Psychophysiology and Biofeedback; 1998.
Evans, JR, Dellinger, MB & Russell, HL, eds., Neurofeedback The First Fifty Years, Academic Press, 2020 Chap. 1
Legarda SB, McMahon D, Othmer S, Othmer S. Clinical neurofeedback: case studies, proposed mechanism, and implications for pediatric neurology practice. J Child Neurol. 2011 Aug;26(8):1045-51
Lubar JF, Bahler WW. Behavioral management of epileptic seizures following EEG biofeedback training of the sensorimotor rhythm. Biofeedback Self Regul. 1976 Mar;1(1):77-104. doi: 10.1007/BF00998692. PMID: 825150.
Lubar J.F, Shabsin H.S, Natelson S.E, Holdson S.E, Holder G.S, Whitt S.F, Pam al. EEG operant conditioning in intractable epileptics. Archives of Neurology . 1981;38:700–704.
Lubar JF, Shouse MN. EEG and behavioral changes in a hyperkinetic child concurrent with training of the sensorimotor rhythm (SMR): a preliminary report. Biofeedback Self Regul. 1976 Sep;1(3):293-306. doi: 10.1007/BF01001170. PMID: 990355.
Monastra V.J, Lubar J.F, Linden M, VanDeusen P, Green G, Wing W, et al. Assessing attention deficit hyperactivity disorder via quantitative electroen- cephalography: An initial validation study. Neuropsychology . 1999;13(3):424– . 1999;13(3):424–
Peniston , E. G. , & Kulkosky , P. J. ( 1989 ). Alpha-theta brain wave training and beta-endorphin levels in alcoholics . Alcoholism, Clinical and Experimental Research , 13 , 271 – 279
Peniston , E. G. , & Kulkosky , P. J. ( 1990 ). Alcoholic personality and alpha-theta brain wave training . Medical Psychotherapy , 3 , 37 – 55
Fehmi, L.G. and Robbins, J., “Sweet Surrender- Discovering the Benefits of Synchronous Alpha Brainwaves” Measuring the Immeasurable: The Scientific Case for Spirituality; Sounds True, Inc., Boulder, Colorado, 2008, 231-254.LFCVRevised 11/2007
Hardt JV, Kamiya J. Anxiety change through electroencephalographic alpha feedback seen only in high anxiety subjects. Science. 1978 Jul 7;201(4350):79-81. doi: 10.1126/science.663641. PMID: 663641.
Kamiya, J. (1968). Conscious control of brain waves. Psychology Today, 1, 56-60. (November)
Leong, S.L., Vanneste, S., Lim, J. et al. A randomised, double-blind, placebo-controlled parallel trial of closed-loop infraslow brain training in food addiction. Sci Rep 8, 11659 (2018)
J. A. Putman MA and MS , S. F. Othmer BA , S. Othmer PhD & V. E. Pollock PhD (2005) TOVA Results Following Inter-Hemispheric Bipolar EEG Training, Journal of Neurotherapy: Investigations in Neuromodulation, Neurofeedback and Applied Neuroscience, 9:1, 37-52
Rogala Jacek, Jurewicz Katarzyna, Paluch Katarzyna, Kublik Ewa, Cetnarski Ryszard, Wróbel Andrzej, 2016, The Do's and Don'ts of Neurofeedback Training: A Review of the Controlled Studies Using Healthy Adults, Frontiers in Human Neuroscience, volume 10
Sittenfeld , P. , Budzynski , T. , & Stoyva , J. (1976). Differential shaping of EEG theta rhythms. Biofeedback and Self-Regulation , 1, 31–45
Shouse, M.N., Lubar, J.F. Operant conditioning of EEG rhythms and ritalin in the treatment of hyperkinesis. Biofeedback and Self-Regulation 4, 299–312 (1979). https://doi.org/10.1007/BF00998960
Smith M.L, Collura T.F, Ferrara J, de Vries J. Infra-slow fluctuation training in clinical practice: A technical history. NeuroRegulation . 2014;1(2):187–207
Leong S.L, Vanneste S, Lim J, Smith M, Manning P, De Ridder D. A randomised, double-blind, placebo-controlled parallel trial of closed-loop infraslow brain training in food addiction. Scientific Reports . 2018;8(1):11659. doi: 10.1038/s41598-018-30181-7.
Smith M.L, Leiderman L, de Vries J. Infra-slow fluctuation (ISF) for autism spectrum disorders. In: Collura T.F, Frederick J.A, eds. Handbook of clinical QEEG and neurotherapy . New York: Routledge Taylor and Francis Group; 2017.
M. B. Sterman PhD , R. W. LoPresti PhD & M. D. Fairchild PhD (2010) Electroencephalographic and Behavioral Studies of Monomethyl Hydrazine Toxicity in the Cat, Journal of Neurotherapy, 14:4, 293-300, DOI: 10.1080/10874208.2010.523367
Sterman MB, Friar L. Suppression of seizures in an epileptic following sensorimotor EEG feedback training. Electroencephalogr Clin Neurophysiol. 1972 Jul;33(1):89-95. doi: 10.1016/0013-4694(72)90028-4. PMID: 4113278.
STERMAN, M. B. 1976. Effects of brain surgery and EEG operant conditioning on seizure latency following monomethylhydrazine in the cat. Exp. Neurol. 50:
757-765. STERMAN, M. B., R. W. LOPRESTI, AND M. D. FAIRCHILD. 1969. Electroencephalographic and behavioral studies of monomethylhydrazine toxicity in the cat. AMRL-TR-69-3, Aerospace Medical Research Laboratory, Air Force Systems Command, Wright-Patterson Air Force Base, Ohio.
Strehl, U., 2014, What learning theories can teach us in designing neurofeedback treatments, Frontiers in Human Neuroscience www.frontiersin.org November 2014 | Volume 8 | Article 894
Robert W. Thatcher, Joel F. Lubar, J. Lucas Koberda; Z-Score EEG Biofeedback: Past, Present, and Future. Biofeedback 1 December 2019; 47 (4): 89–103
Marzbani, H., Marateb, H. R., & Mansourian, M. (2016). Neurofeedback: A Comprehensive Review on System Design, Methodology and Clinical Applications. Basic and clinical neuroscience, 7(2), 143–158.