**EEG Findings in ADHD and the Application of EEG Biofeedback in Treatment of ADHD**

Mohammad Ali Nazari *University of Tabriz, Tabriz, Iran* 

#### **1. Introduction**

268 Current Directions in ADHD and Its Treatment

Muëller A. (2006) Neurobiological diagnostics and therapy in ADHD, in: *News in Pediatrics.*

Pop-Jordanova N. (1999) Electrodermal response based biofeedback in pediatric patients.

Pop-Jordanova N. (2000) Biofeedback mitigation for eating disorders in preadolescents. *Int* 

Pop-Jordanova N. (2003) Eating disorders in the preadolescent period: psychological

Pop-Jordanova N, Zorcec T. (2004) Child trauma, attachment and biofeedback mitigation.

Pop-Jordanova N, Markovska-Simoska S, Zorcec T. (2005) Neurofeedback treatment of children with attention deficit hyperactivity disorder. *Prilozi* 1:71–80 Pop-Jordanova N, Pop-Jordanov J. (2005) Spectrum-weighted EEG frequency ("brain rate")

Pop-Jordanova N. (2006) Biofeedback modalities for children and adolescents. In: Columbus F. (Ed) New research on biofeedback. Nova Biomedical Book, New York. Pop-Jordanova N., Muller A., Zorcec T., Markovska-Simoska S. (2007): QEEG subtypes of

Pop-Jordanova N. (2008) EEG spectra in pediatric research and practice. *Prilozi* 1: 221–239 Pop-Jordanova N, Cakalaroska I. (2008) Biofeedback modalities for better achievement in

Pop-Jordanov J, Pop-Jordanova N. (2009) Neurophysical substrates of arousal and attention.

Pop-Jordanova N. (2009) Biofeedback application for somatoform disorders and attention deficit hyperactivity disorder (ADHD) in children. *Int J Med Sci* 1(2):17–22 Pop-Jordanov J. Pop-Jordanova N. (2010) Quantum transition probabilities and the level of

Scott F (1998) EEG biofeedback for children and adolescent: a pediatrician's perspective.

Smith and Jonides (1999) Storage and executive processes in the frontal lobe, *Science* 283

Stroop R. (1935) Studies of interference in serial verbal reactions, *Journal of Experimental* 

Zorcec T, Pop-Jordanova N, Müller A.(2007) Attention Deficit Hyperactivity Disorder in three

*collective behaviors*, COST B27. EU/ESF Florence, Italy, Book of abstracts p. 25. Zorcec T., Pop-Jordanova N., Muller A. (2007) QEEG characteristics of children with ADHD,

Zorcec T., Pop-Jordanova N., Mueller A., Gjoneska B. (2008) The Role of Q-EEG in

Zorcec T., Pop-Jordanova N. (2010) ADHD as executive dysfunction, *Prilozi*, 31 (2): 171-181

family generations (case report). *Neuroscience today: neuronal functional diversity and* 

Comprehensive Classification of ADHD Children; 2nd Neuromath Workshop,

Macedonian ADHD children*,* Applied Neuroscience for Healthy Brain Function,

high school students school students. *MJM* 2:25–30, also *Revista Espanola de* 

eating disorder research. Nova Biomedical books, New York .

as a quantitative indicator of mental arousal. *Prilozi* 2:35–42

Nijmegen, Netherlands, Book of abstracts, p.60

Consciousness. *Journal of Psychophysiology*, 24(2):136-140

Schwartz MS (1987) Biofeedback: a practitioner's guide. Guilford Press, New York.

characteristics and biofeedback mitigation, Chapter III. In: Swain P (Ed) Focus on

University of Skopje, Skopje, pp 135–147

*Paediatr Croat* 43:117–120

*Pediatr* 1:76–82

*Prilozi* 1–2:103–114

*Neuropsicologia*, 1:97–98

*Biofeedback* 26(3):18–20

*Psychology*, 18: 643-662

Jena. 28-29 April: 42-43

(5408): 1657-1661.

*Epilepsy*: 111-120

*Cogn Process*. 10(Suppl.1), S71-S79

As defined in the 4th edition of Diagnostic and Statistical Manual of Mental Disorders (American Psychiatric Association, 1994), Attention deficit hyperactivity disorder (ADHD) is characterized by a persistent pattern of inattention, hyperactivity, and impulsiveness, though it can present with or without hyperactivity. ADHD is the most common childhood mental health disorder, with an estimated prevalence of 7% to 10% in boys and 3% in girls aged 4-11 years (Sgrok et al., 2000). This disorder substantially affects the individual's normal cognitive and behavioral functioning. For example, children with ADHD can have a great deal of difficulty focusing on lessons presented by their teachers and remembering how to do their homework. They may often be easily distracted whereby they pay attention to other things than what they should.

The numerous studies support a model that defines ADHD as an inherited disorder whose core symptoms are founded in neuroanatomic, neurochemical, and neurophysiologic abnormalities of the brain (Monastra, 2005). Deficits associated with ADHD support a hypothesis that anatomical and biochemical abnormalities of the prefrontal cortex constitute the physical basis of this disorder (Barkley, 1997). In this line, neurodiagnostic procedures (e.g., positron emission tomography [PET], single photon emission tomography [SPECT] and magnetic resonance imaging [MRI]) studies have provided evidence of the neurological basis of ADHD (Boutros, et al., 2009). Nevertheless, new theories on the pathogenesis of psychopathological phenomena conceptualize as a consequence of the failure to integrate the activity of different brains' areas (Boutros et al., 2009). It needs techniques tapping the dynamics of complex interaction over time among cerebral regions involved in the integration of cognitive processing.

Electrophysiological techniques enable monitoring brain processing in real time, providing the best methods to describe the time course of brain electrical activities. Growth of this field came from the newer and quantifiable techniques such as quantitative electroencephalography (QEEG). QEEG methods provide a set of non-invasive tools that are capable of quantitatively assessing resting and evoked activity of the brain with sensitivity and temporal resolution superior to those of any other imaging methods (Hughes & John, 1999).

QEEG studies have explored brainwave profile in children with ADHD, compared to normal children. These brainwaves could be trained via operant conditioning (called EEG

EEG Findings in ADHD and the Application of EEG Biofeedback in Treatment of ADHD 271

A voluminous literature attests to the robustness of conventional EEG studies and their clinical utility in disorders of brain function (Hughes & John, 1999). However, many functional characteristics of brain activity could not be detected visually. Whereas, quantitative EEG (QEEG) transform the EEG into a format or domain that elucidates relevant information, or associate numerical results with the EEG data for subsequent review or comparison (Nuwer, 1997). Often, neurologically based disorders do not involve a structural abnormality, lesion, or disease process, but abnormalities are expressed in the way the brain evaluates information. These processes can be studied with QEEG techniques, but not with simple visual analysis of the raw EEG (Hoffman et al., 1999). Hence, one can say that QEEG might provide additional measurements and displays of EEG in many

In fact, QEEG reflects the ability of a network to locally synchronize. Such ability to synchronization is related to the integrative capacities of a network and to the characteristics of its inputs. This can be strongly modified by the active state of the brain. Thus, impairment of cognitive processing (i.e. attention) can be monitored by QEEG (Nazari, 2008). Furthermore, QEEG enables precise comparison of the individual patient's record with normative and psychopathologic patient databases (Hughes & John, 1999). QEEG procedures involve the mathematical processing of digitally recorded EEG. The most commonly used method for EEG quantification is the spectral analysis by means of Fast Fourier Transformation (FFT) algorithm. It provides measures of the power at each frequency of the EEG bands, known as the power spectrum. The test-retest of power spectra

The first step for doing a QEEG is digital EEG recording; a cap (usually 19 electrodes at standardized positions) placed on the head and two electrodes are placed on the ears. The electrodes are then made to conduct with the scalp and ears by using a conductive gel. Once this is achieved, a computer interfaces with the EEG machine, and a software program is used to display the traces of the brainwaves generated by the brain, and detected on the scalp. Data is recorded during resting states of eyes open, eyes closed, and in some instances during cognitive tests such as reading or attentional task. Approximately ten minutes of data are recorded in each state. A QEEG typically requires about an hour total in the clinic to

After recording the EEG data it is edited to remove artifacts which are distortions in the EEG signal due to muscle movement such as coughs, eye movement, and teeth clenching, muscle tension, pulse and other sources. Artifacts are electric potentials of non-brain origin that are in frequency and voltage range of EEG signals and that are detected by scalp electrodes (Boutros et al., 2009). Clinicians utilizing QEEG must be skillful in recognizing and minimizing artifacts, as well as in careful pre-recording preparation procedures to minimize artifacts in the EEG (Hammond and Gunkelman, 2001). Indeed, it needs to carefully study the raw EEG since abnormalities may be masked by the use of a QEEG alone (Hammond et al., 2004). During the editing process the data is examined visually to identify any patterns that might be of interest for training purposes or would suggest the need to refer to another

After the editing process is completed the EEG data is subjected to a variety of mathematical and statistical analyses. EEG recordings should be of sufficient quality and of sufficient length so that after artifacting there is a minimum of 40-50 seconds of artifact-free data

different ways that are not possible with visual inspection.

has been shown to be high (Hughes & John, 1999).

complete the data gathering.

specialist.

biofeedback or neurofeedback) and it is claimed that self regulation of brain electrical activity result in a therapeutic benefit in ADHD. The main purpose of this chapter is to look at one alternative method of treating children with ADHD. To fulfill this purpose, the present chapter will review:


Our brain is made up of many cells, including neurons and glial cells. There are about 100 billion neurons in the brain. Neurons are cells that send and receive information to and from the brain and nervous system. The language of these communications throughout the nervous system is electro-chemical signals. An electroencephalography (EEG) is a tool for measuring electrical activity generated in the brain. These electrical activities of neurons are very tiny. Hence, EEG activity always reflects the summation of the synchronous activity of thousands or millions of neurons; when many neurons shift towards being more ready to fire (excitatory) or to not fire (inhibitory) at the same time. The EEG signals are recorded using sensors (electrodes) placed on the scalp. Electrodes are attached to our head and hooked by wires to a computer and then the computer records our brain's electrical activity on the screen. Patterns of neuronal electrical activity recorded are called brainwaves.

An EEG signal is characterized by three major components: phase, frequency and amplitude. Traditional EEG displays waveforms in the time domain, and the interpretation is based on amplitude and dominant frequency. Each brainwave frequency is expressed in Hertz (Hz). One Hz means 1 cycle per second; it is the rhythm of the wave. Amplitude represents the height (intensity) of the brainwave, and is expressed in microvolt (mV). Brainwaves have traditionally been separated into different frequency bands (Drongelen, 2007):


Conventional interpretation of the EEG is done visually by a trained specialist. The specialist will examine the EEG, by detecting features of waveshapes (morphology) of the brainwaves to identify certain characteristics that might indicate organic or neurological pathologies. Routine EEG is typically used in the following clinical circumstances:


biofeedback or neurofeedback) and it is claimed that self regulation of brain electrical activity result in a therapeutic benefit in ADHD. The main purpose of this chapter is to look at one alternative method of treating children with ADHD. To fulfill this purpose, the

 neurofeedback findings in the treatment of ADHD as supported by controlled studies. Our brain is made up of many cells, including neurons and glial cells. There are about 100 billion neurons in the brain. Neurons are cells that send and receive information to and from the brain and nervous system. The language of these communications throughout the nervous system is electro-chemical signals. An electroencephalography (EEG) is a tool for measuring electrical activity generated in the brain. These electrical activities of neurons are very tiny. Hence, EEG activity always reflects the summation of the synchronous activity of thousands or millions of neurons; when many neurons shift towards being more ready to fire (excitatory) or to not fire (inhibitory) at the same time. The EEG signals are recorded using sensors (electrodes) placed on the scalp. Electrodes are attached to our head and hooked by wires to a computer and then the computer records our brain's electrical activity

on the screen. Patterns of neuronal electrical activity recorded are called brainwaves.

An EEG signal is characterized by three major components: phase, frequency and amplitude. Traditional EEG displays waveforms in the time domain, and the interpretation is based on amplitude and dominant frequency. Each brainwave frequency is expressed in Hertz (Hz). One Hz means 1 cycle per second; it is the rhythm of the wave. Amplitude represents the height (intensity) of the brainwave, and is expressed in microvolt (mV). Brainwaves have traditionally been separated into different frequency bands (Drongelen,

Conventional interpretation of the EEG is done visually by a trained specialist. The specialist will examine the EEG, by detecting features of waveshapes (morphology) of the brainwaves to identify certain characteristics that might indicate organic or neurological pathologies.

to differentiate "organic" encephalopathy or delirium from primary psychiatric

to localize the region of brain from which a seizure originates (Niedermeyer and da

present chapter will review:

QEEG findings in ADHD

2007):

 Delta rhythm (δ): 0.1–4 Hz Theta rhythm (θ): 4–8 Hz Alpha rhythm (α): 8–12 Hz

Beta rhythm (β): 15–30 Hz

to distinguish epileptic seizures,

syndromes such as catatonia, to serve as an adjunct test of brain death,

Silva, 2004).

Sensory-motor rhythm (SMR): 12 to 15 Hz

Gamma rhythm (γ): the higher EEG frequencies, usually 30~70 Hz.

Routine EEG is typically used in the following clinical circumstances:

description of electroencephalography and QEEG

description of neurofeedback in practice

brief history and rationale for neurofeedback development

A voluminous literature attests to the robustness of conventional EEG studies and their clinical utility in disorders of brain function (Hughes & John, 1999). However, many functional characteristics of brain activity could not be detected visually. Whereas, quantitative EEG (QEEG) transform the EEG into a format or domain that elucidates relevant information, or associate numerical results with the EEG data for subsequent review or comparison (Nuwer, 1997). Often, neurologically based disorders do not involve a structural abnormality, lesion, or disease process, but abnormalities are expressed in the way the brain evaluates information. These processes can be studied with QEEG techniques, but not with simple visual analysis of the raw EEG (Hoffman et al., 1999). Hence, one can say that QEEG might provide additional measurements and displays of EEG in many different ways that are not possible with visual inspection.

In fact, QEEG reflects the ability of a network to locally synchronize. Such ability to synchronization is related to the integrative capacities of a network and to the characteristics of its inputs. This can be strongly modified by the active state of the brain. Thus, impairment of cognitive processing (i.e. attention) can be monitored by QEEG (Nazari, 2008). Furthermore, QEEG enables precise comparison of the individual patient's record with normative and psychopathologic patient databases (Hughes & John, 1999). QEEG procedures involve the mathematical processing of digitally recorded EEG. The most commonly used method for EEG quantification is the spectral analysis by means of Fast Fourier Transformation (FFT) algorithm. It provides measures of the power at each frequency of the EEG bands, known as the power spectrum. The test-retest of power spectra has been shown to be high (Hughes & John, 1999).

The first step for doing a QEEG is digital EEG recording; a cap (usually 19 electrodes at standardized positions) placed on the head and two electrodes are placed on the ears. The electrodes are then made to conduct with the scalp and ears by using a conductive gel. Once this is achieved, a computer interfaces with the EEG machine, and a software program is used to display the traces of the brainwaves generated by the brain, and detected on the scalp. Data is recorded during resting states of eyes open, eyes closed, and in some instances during cognitive tests such as reading or attentional task. Approximately ten minutes of data are recorded in each state. A QEEG typically requires about an hour total in the clinic to complete the data gathering.

After recording the EEG data it is edited to remove artifacts which are distortions in the EEG signal due to muscle movement such as coughs, eye movement, and teeth clenching, muscle tension, pulse and other sources. Artifacts are electric potentials of non-brain origin that are in frequency and voltage range of EEG signals and that are detected by scalp electrodes (Boutros et al., 2009). Clinicians utilizing QEEG must be skillful in recognizing and minimizing artifacts, as well as in careful pre-recording preparation procedures to minimize artifacts in the EEG (Hammond and Gunkelman, 2001). Indeed, it needs to carefully study the raw EEG since abnormalities may be masked by the use of a QEEG alone (Hammond et al., 2004). During the editing process the data is examined visually to identify any patterns that might be of interest for training purposes or would suggest the need to refer to another specialist.

After the editing process is completed the EEG data is subjected to a variety of mathematical and statistical analyses. EEG recordings should be of sufficient quality and of sufficient length so that after artifacting there is a minimum of 40-50 seconds of artifact-free data

EEG Findings in ADHD and the Application of EEG Biofeedback in Treatment of ADHD 273

(Hammond et al., 2011). For further details about standards and qualifications for doing QEEG and neurofeedback see Hoffman et al., 1999; Hammond et al, 2004 and Hammond et

Due to the non-invasive nature of the procedure, the convenience, not expensive, and specificity of the data the QEEG has been used extensively to examine a variety of aspects of brain function. As mentioned before, with the quantitative EEG and topographic brain maps, it is often possible to observe attributes of brain function that cannot be seen in the raw EEG signal. These processes can be observed and quantified through subtle frequencyrelated and coherence related activities in the QEEG brain maps that index the degree of difficulty of cognitive tasks (Hoffman et al., 1999). Furthermore, it is well known that a great many medications as well as psychoactive drugs can produce some alteration in the EEG (Boutros et al., 2009). The availability of QEEG let to the development of a new research field that named pharmaco-EEG. Pharmaco-EEG methods were included in preclinical studies to identify at early stages of drug development, the therapeutic indications of new drugs, determining onset, peak effect, and duration of drug effect on CNS, and predict

In the clinical setting, many studies have been reported that QEEG can be useful for the evaluation and understanding of mild traumatic brain injury, learning disabilities, ADHD, alcoholism, depression, and other types of substance abuse (Hoffman et al., 1999). Specifically, QEEG studies have reported different brainwave patterns in children with

Most studies of the electrophysiological correlates of ADHD have compared the QEEG from ADHD sufferers with those of healthy children under resting conditions (for a review, see Barry et al., 2003; for a meta-analysis, see Snyder & Hall, 2006). However, the allocation of neural resources differs when the subject directs his/her attention to an experimentally controlled situation (Thatcher, 1998). It is therefore important to evaluate a neural network's ability to change from a passive to an active condition. Since inattentiveness and distractibility are the major symptoms of ADHD, assessment of these symptoms would require tasks specifically designed to highlight attentional deficits, such as the continuous performance task (CPT) or the go/no-go task. Hence, in a study, Nazari et al (2011) set out to establish the functional reactivity of frequency-specific EEG activities during eyes-open resting and CPT in children with ADHD. High-resolution EEG was recorded during eyesopen resting and CPT performance in 16 children meeting the DSM-IV criteria (APA, 1994) for ADHD and 16 age-matched controls. Significant CPT vs. eyes-open differences in EEG activities was observed in children with ADHD. In particular, switching to CPT induced an alpha power increase in children with ADHD and an alpha power decrease in controls. Lower alpha power at baseline (eyes-open resting condition) might be interpreted as meaning that children with ADHD are unable to attend to and process visual stimuli as efficiently as healthy children. Klimesch et al (1996) suggested that alpha synchronization during mental inactivity may be important for introducing powerful inhibitory effects, which could prevent a memory search from entering irrelevant parts of neural networks. Based on this explanation, we suggested that impaired inhibition of neural networks in children with ADHD at baseline alters not only energy demands but also control excitatory processes. Opposite alpha changes may also reflect a primary deficit associated with cortical hypoarousal in ADHD. These EEG results agree with behavioral findings leading the

therapeutically useful dosage of psychotropic drugs (Boutros et al., 2009).

ADHD than those of the normal population.

al, 2011.

available for analysis (Hammond et al., 2004). A sample of artifact-free EEG data, usually 1 to 2 minutes, is analyzed, using the FFT to quantify the power at each frequency of the EEG averaged across the entire sample (Hughes & John, 1999). Results from each electrode can be represented as following measures:


The final analysis is the database comparison. This procedure allows for an individual's EEG to be compared to an 'average' EEG. One can use a reference EEG database to reveal the location and type of EEG feature abnormalities greater than two standard deviations from a normative group (Thatcher, 1998). This comparison data is derived from the analysis of EEG's gathered from hundreds of individuals; same sex, same handedness, approximate same age; who do not exhibit or report historically any significant mental health issues. Often the EEG will be compared to multiple databases. The aspects of an individual's EEG to be analyzed by the QEEG are:


The QEEG data is used to generate a series of analyses presented in tables and graphics in brain map. Brain map is a computerized EEG topography that enables the construction of a bi- or three-dimensional matrix for a topographic representation of Q-EEG parameters, such as instant amplitude or band power (Boutros et al., 2009). Different algorithms have been proposed to localize underlying brain generators. Among the distributed source models, Low Resolution Brain Electromagnetic Tomography-LORETA (Pasqual-Marqui et al., 1994) has been proven to present the smallest localization error (Boutros et al., 2009). The LORETA is one of the QEEG topographic analysis method by which one can provide a 3-D analysis of the EEG identifying localized disruptions in brain activity within the interior of the brain.

An individual who has received specialized training in these fields (see Hoffman et al., 1999; Hammond et al, 2004; Hammond et al, 2011) could examine the QEEG results. Individuals conducting assessment utilizing quantitative EEG or any type of brain mapping should be able to gather reliable data. A much higher standard is required for someone to hold himself or herself out as competent to analyze and interpret QEEG data (Hammond et al., 2004). It is strongly recommended that the QEEG providers *should hold diplomate status in QEEG from the Quantitative Electroencephalography Certification Board or be certified by the EEG and Clinical Neuroscience Society (or a comparable neurology board in the case of physicians), or be analyzing data under the supervision of such a certified person, or at a minimum be able to demonstrate thorough education, training, and work product documenting their competence to interpret QEEGs. Otherwise, the QEEG data should be submitted for analysis by an individual with such certification*

available for analysis (Hammond et al., 2004). A sample of artifact-free EEG data, usually 1 to 2 minutes, is analyzed, using the FFT to quantify the power at each frequency of the EEG averaged across the entire sample (Hughes & John, 1999). Results from each electrode can be

absolute power: amount of amplitude in each band (total µV²),

Does the individual's EEG features differ from the 'average' EEG?

Where (what areas of the brain) does it look different?

relative power: in each band percentage of absolute power/total power,

 power ratio: i.e. absolute power of theta/absolute power of beta (theta/beta ratio), coherence: a measure of synchronization between activity in two channels (similarity of

symmetry (the ratio of power in each band between a symmetrical pair of electrodes (no

The final analysis is the database comparison. This procedure allows for an individual's EEG to be compared to an 'average' EEG. One can use a reference EEG database to reveal the location and type of EEG feature abnormalities greater than two standard deviations from a normative group (Thatcher, 1998). This comparison data is derived from the analysis of EEG's gathered from hundreds of individuals; same sex, same handedness, approximate same age; who do not exhibit or report historically any significant mental health issues. Often the EEG will be compared to multiple databases. The aspects of an individual's EEG

How does it look different (the level of statistical significance and the degree of

The QEEG data is used to generate a series of analyses presented in tables and graphics in brain map. Brain map is a computerized EEG topography that enables the construction of a bi- or three-dimensional matrix for a topographic representation of Q-EEG parameters, such as instant amplitude or band power (Boutros et al., 2009). Different algorithms have been proposed to localize underlying brain generators. Among the distributed source models, Low Resolution Brain Electromagnetic Tomography-LORETA (Pasqual-Marqui et al., 1994) has been proven to present the smallest localization error (Boutros et al., 2009). The LORETA is one of the QEEG topographic analysis method by which one can provide a 3-D analysis of the EEG identifying localized disruptions in brain activity within the interior of

An individual who has received specialized training in these fields (see Hoffman et al., 1999; Hammond et al, 2004; Hammond et al, 2011) could examine the QEEG results. Individuals conducting assessment utilizing quantitative EEG or any type of brain mapping should be able to gather reliable data. A much higher standard is required for someone to hold himself or herself out as competent to analyze and interpret QEEG data (Hammond et al., 2004). It is strongly recommended that the QEEG providers *should hold diplomate status in QEEG from the Quantitative Electroencephalography Certification Board or be certified by the EEG and Clinical Neuroscience Society (or a comparable neurology board in the case of physicians), or be analyzing data under the supervision of such a certified person, or at a minimum be able to demonstrate thorough education, training, and work product documenting their competence to interpret QEEGs. Otherwise, the QEEG data should be submitted for analysis by an individual with such certification*

represented as following measures:

frequency between two channels),

similarity is called asymmetry).

to be analyzed by the QEEG are:

difficulty)?

the brain.

(Hammond et al., 2011). For further details about standards and qualifications for doing QEEG and neurofeedback see Hoffman et al., 1999; Hammond et al, 2004 and Hammond et al, 2011.

Due to the non-invasive nature of the procedure, the convenience, not expensive, and specificity of the data the QEEG has been used extensively to examine a variety of aspects of brain function. As mentioned before, with the quantitative EEG and topographic brain maps, it is often possible to observe attributes of brain function that cannot be seen in the raw EEG signal. These processes can be observed and quantified through subtle frequencyrelated and coherence related activities in the QEEG brain maps that index the degree of difficulty of cognitive tasks (Hoffman et al., 1999). Furthermore, it is well known that a great many medications as well as psychoactive drugs can produce some alteration in the EEG (Boutros et al., 2009). The availability of QEEG let to the development of a new research field that named pharmaco-EEG. Pharmaco-EEG methods were included in preclinical studies to identify at early stages of drug development, the therapeutic indications of new drugs, determining onset, peak effect, and duration of drug effect on CNS, and predict therapeutically useful dosage of psychotropic drugs (Boutros et al., 2009).

In the clinical setting, many studies have been reported that QEEG can be useful for the evaluation and understanding of mild traumatic brain injury, learning disabilities, ADHD, alcoholism, depression, and other types of substance abuse (Hoffman et al., 1999). Specifically, QEEG studies have reported different brainwave patterns in children with ADHD than those of the normal population.

Most studies of the electrophysiological correlates of ADHD have compared the QEEG from ADHD sufferers with those of healthy children under resting conditions (for a review, see Barry et al., 2003; for a meta-analysis, see Snyder & Hall, 2006). However, the allocation of neural resources differs when the subject directs his/her attention to an experimentally controlled situation (Thatcher, 1998). It is therefore important to evaluate a neural network's ability to change from a passive to an active condition. Since inattentiveness and distractibility are the major symptoms of ADHD, assessment of these symptoms would require tasks specifically designed to highlight attentional deficits, such as the continuous performance task (CPT) or the go/no-go task. Hence, in a study, Nazari et al (2011) set out to establish the functional reactivity of frequency-specific EEG activities during eyes-open resting and CPT in children with ADHD. High-resolution EEG was recorded during eyesopen resting and CPT performance in 16 children meeting the DSM-IV criteria (APA, 1994) for ADHD and 16 age-matched controls. Significant CPT vs. eyes-open differences in EEG activities was observed in children with ADHD. In particular, switching to CPT induced an alpha power increase in children with ADHD and an alpha power decrease in controls. Lower alpha power at baseline (eyes-open resting condition) might be interpreted as meaning that children with ADHD are unable to attend to and process visual stimuli as efficiently as healthy children. Klimesch et al (1996) suggested that alpha synchronization during mental inactivity may be important for introducing powerful inhibitory effects, which could prevent a memory search from entering irrelevant parts of neural networks. Based on this explanation, we suggested that impaired inhibition of neural networks in children with ADHD at baseline alters not only energy demands but also control excitatory processes. Opposite alpha changes may also reflect a primary deficit associated with cortical hypoarousal in ADHD. These EEG results agree with behavioral findings leading the

EEG Findings in ADHD and the Application of EEG Biofeedback in Treatment of ADHD 275

the formwork of learning theory. This brainwave training and learning self regulation of brain activity is called EEG biofeedback or neurofeedback. Neurofeedback postulates that normalizing the target signal will result in a therapeutic benefit. Definition of neurofeedback

*"neurofeedback is a process in which sensors are placed on the scalp and devices are used to monitor and provide moment-to-moment information that is fed back to the individual about his or her physiological brain activity for purposes of improving brain functioning" (Hammond et al., 2001; p.55).* For detailed information about neurofeedback see the website of the

Figure 1 shows the neurofeedback procedure. During neurofeedback training, neuroelectrical activity is detected via surface electrodes (step 1). Note that no electrical current is put into the brain. This activity is then amplified (step 2) and processed by software programs (step 3) that provide contingent auditory or visual feedback to the patient on a computer monitor (step 4); brain activity is monitored and desired changes are rewarded similar to a videogame. The patient watches the dynamic display of the amplitude of the brainwaves in the areas where the electrodes are attached by a gel paste. The computer program gives a reinforcement each time the goal level of the EEG power (an optimal brain state) is reached. This processing continues during the neurofeedback session

For example, there might be areas of the brain where there is an excess of neurons firing slowly during tasks requiring concentration. This is often the case with ADHD. On the basis of QEEG findings in ADHD, typically the EEG of a person with ADHD will reveal excess theta activity, but diminished beta activity. Hence, during the neurofeedback training a puzzle advances and sounds a tone whenever a child with ADHD maintains waves in the 15-18 Hz range above a certain amplitude threshold (beta increasing) while keeping waves in the 4-8 Hz range below a certain threshold (theta decreasing). Clients require 20 to 60

by the International Society for Neurofeedback and Research (ISNR) is the following:

ISNR (http://www.isnr.org).

for a period of 15 to 40 minutes (step 5).

Fig. 1. Neurofeedback procedure

authors to suggest that dynamic changes in neural network activities are impaired in children with ADHD (Nazari et al., 2011).

Lubar (1995) compared QEEG data for ADHD children with controls. He concluded, "Excessive theta activity and lack of beta activity are the primary neurological landmarks of ADHD" (p. 505). Furthermore, "during academic challenges, there were significant increases in slow (4-8 Hertz) theta activity along the midline and in the frontal regions and decreased beta activity, especially along the midline posteriorly" (p. 502). Lubar's review of the literature revealed the following:

*"Abnormalities in EEG were reported in children now classified as ADD and ADHD as early as 1938 (Jasper, Solomon & Bradley, 1938). There is extensive literature, much of it reviewed in the supplement to the Journal of Child Neurology published in 1991. Basically, EEC studies show excessive slow activity in central and frontal regions of the brain. These studies are supported by recent PET [positron emission tomography] scan and SPECT [single photon emission computerized tomography] scan studies that also indicate abnormalities in cerebral metabolism in these particular brain areas" (p. 50I).* 

Based on Lubar's finding, studies have repeatedly reported a QEEG pattern that might be present in ADHD but not in controls (normal children, adolescents, and adults). A considerable number of these studies have reported an increase in low-frequency power (predominantly theta) and a decrease in high-frequency power (especially beta) in children with ADHD compared with the age-matched control group (Barry et al., 2003; Snyder & Hall, 2006). Some researchers have tried to examine the theta/beta ratio as a measure of ADHD-related abnormality with a higher detection power. As reported by Snyder & Hall (2006) results of 9 DSM-IV studies and the results of 29 pre–DSM-IV studies support that a theta/beta ratio increase is a commonly observed trait in ADHD relative to controls. By meta-analytic statistical extrapolation, the effect size of 3.08 predicts a sensitivity and specificity of 94%, which is similar to values predicted by retrospective studies examining ADHD and normal controls in group comparisons (Snyder & Hall, 2006).

As emphasized by the committee of the Association for Applied Psychophysiology and Biofeedback (AAPB) and the Society for the Study of Neuronal Regulation (SSNR), QEEG should not be the only tool used for diagnosis of attention-deficit/hyperactivity disorder (Hoffman et al., 1999). *There is no single technique that can be solely relied upon for the diagnosis. Manifestations of ADD/ADHD reflect behavior problems, learning style, cognitive processing, social interaction, and many other developmental factors. The current diagnosis of ADD/ADHD depends also on the use of computerized continuous performance tasks, detailed history, school performance, and evaluation for learning disabilities and other comorbidities, as well as other measures. QEEG data complement these other findings by providing for a comparison of brain activity with databases for both normal and ADD/ADHD groups* (Hoffman et al., 1999).

Having diagnosed the locations in the brain that are producing high or low activity, it is now possible to intervene with training the brain to normalize the activity of the various locations in the brain. On the other words, the power in being able to define deviations of brain's electrical patterns within a normally distributed measurement set is that one can target deviant measures to "normalize" by a variety of intervention modalities. In fact, the EEG (as a physiological measure) is considered a form of behavior, which is subject to behavior modification through basic "operant conditioning" and "shaping" principles within

authors to suggest that dynamic changes in neural network activities are impaired in

Lubar (1995) compared QEEG data for ADHD children with controls. He concluded, "Excessive theta activity and lack of beta activity are the primary neurological landmarks of ADHD" (p. 505). Furthermore, "during academic challenges, there were significant increases in slow (4-8 Hertz) theta activity along the midline and in the frontal regions and decreased beta activity, especially along the midline posteriorly" (p. 502). Lubar's review of the

*"Abnormalities in EEG were reported in children now classified as ADD and ADHD as early as 1938 (Jasper, Solomon & Bradley, 1938). There is extensive literature, much of it reviewed in the supplement to the Journal of Child Neurology published in 1991. Basically, EEC studies show excessive slow activity in central and frontal regions of the brain. These studies are supported by recent PET [positron emission tomography] scan and SPECT [single photon emission computerized tomography] scan studies that also indicate abnormalities in cerebral metabolism* 

Based on Lubar's finding, studies have repeatedly reported a QEEG pattern that might be present in ADHD but not in controls (normal children, adolescents, and adults). A considerable number of these studies have reported an increase in low-frequency power (predominantly theta) and a decrease in high-frequency power (especially beta) in children with ADHD compared with the age-matched control group (Barry et al., 2003; Snyder & Hall, 2006). Some researchers have tried to examine the theta/beta ratio as a measure of ADHD-related abnormality with a higher detection power. As reported by Snyder & Hall (2006) results of 9 DSM-IV studies and the results of 29 pre–DSM-IV studies support that a theta/beta ratio increase is a commonly observed trait in ADHD relative to controls. By meta-analytic statistical extrapolation, the effect size of 3.08 predicts a sensitivity and specificity of 94%, which is similar to values predicted by retrospective studies examining

As emphasized by the committee of the Association for Applied Psychophysiology and Biofeedback (AAPB) and the Society for the Study of Neuronal Regulation (SSNR), QEEG should not be the only tool used for diagnosis of attention-deficit/hyperactivity disorder (Hoffman et al., 1999). *There is no single technique that can be solely relied upon for the diagnosis. Manifestations of ADD/ADHD reflect behavior problems, learning style, cognitive processing, social interaction, and many other developmental factors. The current diagnosis of ADD/ADHD depends also on the use of computerized continuous performance tasks, detailed history, school performance, and evaluation for learning disabilities and other comorbidities, as well as other measures. QEEG data complement these other findings by providing for a comparison of brain activity with databases* 

Having diagnosed the locations in the brain that are producing high or low activity, it is now possible to intervene with training the brain to normalize the activity of the various locations in the brain. On the other words, the power in being able to define deviations of brain's electrical patterns within a normally distributed measurement set is that one can target deviant measures to "normalize" by a variety of intervention modalities. In fact, the EEG (as a physiological measure) is considered a form of behavior, which is subject to behavior modification through basic "operant conditioning" and "shaping" principles within

ADHD and normal controls in group comparisons (Snyder & Hall, 2006).

*for both normal and ADD/ADHD groups* (Hoffman et al., 1999).

children with ADHD (Nazari et al., 2011).

*in these particular brain areas" (p. 50I).* 

literature revealed the following:

the formwork of learning theory. This brainwave training and learning self regulation of brain activity is called EEG biofeedback or neurofeedback. Neurofeedback postulates that normalizing the target signal will result in a therapeutic benefit. Definition of neurofeedback by the International Society for Neurofeedback and Research (ISNR) is the following:

*"neurofeedback is a process in which sensors are placed on the scalp and devices are used to monitor and provide moment-to-moment information that is fed back to the individual about his or her physiological brain activity for purposes of improving brain functioning" (Hammond et al., 2001; p.55).* For detailed information about neurofeedback see the website of the ISNR (http://www.isnr.org).

Figure 1 shows the neurofeedback procedure. During neurofeedback training, neuroelectrical activity is detected via surface electrodes (step 1). Note that no electrical current is put into the brain. This activity is then amplified (step 2) and processed by software programs (step 3) that provide contingent auditory or visual feedback to the patient on a computer monitor (step 4); brain activity is monitored and desired changes are rewarded similar to a videogame. The patient watches the dynamic display of the amplitude of the brainwaves in the areas where the electrodes are attached by a gel paste. The computer program gives a reinforcement each time the goal level of the EEG power (an optimal brain state) is reached. This processing continues during the neurofeedback session for a period of 15 to 40 minutes (step 5).

Fig. 1. Neurofeedback procedure

For example, there might be areas of the brain where there is an excess of neurons firing slowly during tasks requiring concentration. This is often the case with ADHD. On the basis of QEEG findings in ADHD, typically the EEG of a person with ADHD will reveal excess theta activity, but diminished beta activity. Hence, during the neurofeedback training a puzzle advances and sounds a tone whenever a child with ADHD maintains waves in the 15-18 Hz range above a certain amplitude threshold (beta increasing) while keeping waves in the 4-8 Hz range below a certain threshold (theta decreasing). Clients require 20 to 60

EEG Findings in ADHD and the Application of EEG Biofeedback in Treatment of ADHD 277

Protocol 1- SMR enhancement/theta suppression: in this protocol, patients (ADHD who present with primary symptoms of hyperactivity and impulsivity) instructed to increase their SMR (12–15 Hz) over one of two sites (C3 or C4) while simultaneously suppressing the production of theta (4–7 or 4–8 Hz) activity. EEG recordings are obtained from one active site, referenced to linked earlobes. Auditory and visual feedback is provided based on patient success in controlling power of theta below and SMR above pretreatment

Protocol 2- Theta suppression/beta1 enhancement: In this protocol, patients are reinforced for increasing production of beta1 activity (16–20 Hz) while suppressing theta activity (4–8 Hz). Recordings are obtained at Cz with linked ear references, at FCz-PCz with single ear reference, or at Cz-Pz with ear reference. A variation of this protocol also has been reported in the treatment of ADHD, predominately inattentive type (Fuchs et al., 2003). In this

Protocol 3- SMR enhancement/beta2 suppression: in this protocol, children with ADHD, predominately hyperactive/impulsive type, are trained to increase SMR (12–15 Hz) while suppressing beta2 activity (22–30 Hz) (Fuchs et al., 2003) Recordings are obtained at C4 with linked ear reference. In ADHD, combined type, this protocol is used during half of each session. During the other portion of each training session, SMR enhancement/theta suppression at C3 is used. Selection of a neurofeedback protocol should be based on level of experience and training, accreditation, the fraction of the therapist's practice devoted to neurofeedback, reports from clients and objective assessments, and the therapist's specific experience in treating AD/HD (for more information see Monastra, 2005; Demos, 2005;

Since the work of Lubar and Shous (1976), numerous studies have used neurofeedback approaches for treating ADHD and reported successful diminution of inattentivity and hyperactivity, and improvement in academic performance and concluded that despite some limitations, neurofeedback may be worthy of further consideration as a viable treatment approach for ADHD (Shouse and Lubar , 1979; Lubar and Lubar, 1984; Lubar et al., 1995; Rossiter and La Vaque, 1995; Linden et al., 1996; Thompson and Thompson, 1998; Kaiser and Othmer, 2000; Carmody et al., 2001; Monastra, 2002; Fuchs et al., 2003; Heywood and Beale, 2003; Cho et al., 2004; Heinrich et al., 2004; Rossiter et al., 2004; Xiong et al., 2005; Kropotov et al., 2005; Beauregard and Levesque, 2006; Levesque et al., 2006; Strehl et al., 2006; Gevensleben et al., 2010; Nazari et al., 2011; for review see Rossiter, 2004; ; Vernon et al., 2004; Monastra, 2005; Butnik, 2005; Friel, 2007; Toplak et al., 2008; John and Prichep, 2009; Coben and Evans, 2011). In an excellent meta-analytic study, Arns et al (2009) investigated results of 15 controlled studies. They concluded that neurofeedback treatment for ADHD can be considered "efficacious and specific" with a high effect size for inattention

Gevensleben et al (2009) conducted a randomized controlled trial encompassing 102 children with ADHD. In this trial behavioral and neurophysiological effects of neurofeedback, were analyzed in comparison to a computerised attention skills training (as a semi-active control group). They have shown neurofeedback to be superior to control group (Gevensleben et al., 2009). They reported follow-up behavioral data assessed 6

and impulsivity and a medium for hyperactivity (Arns et al., 2009).

training protocol, theta suppression and beta enhancement are reinforced at C3.

thresholds.

Hammond et al., 2011).

training sessions to achieve their goals. Training takes place 1-3 times for at least one hour of training per week. Once the original goals of treatment have been met, the client continues to train for an additional 5 to 10 sessions to prevent relapse (Demos, 2005).

Prior to beginning neurofeedback training an assessment is conducted to examine presenting problems, client history, contributing factors, current medications the patient may be taking, and other relevant information. Interviews, symptom checklists, computer based tests (i.e. CPT, TOVA, IVA), and review of relevant documentation are common components of the assessment. A pre- and post treatment objective assessment of the client's QEEG should be performed. The QEEG objectively assess the functioning of the brain in comparison with normative database (Hammond et al., 2011). One can use QEEG database and topographical brain maps to evaluate the location and type of EEG feature to target for neurofeedback training.

After reviewing the data gathered during the assessment a training protocol is developed. The neurofeedback protocols cover the following questions:


The rationale for neurofeedback protocols is based on solid research and clinical practice. Initially, neurofeedback treatments for ADHD are founded on the groundbreaking research conducted by Sterman (roth et al., 1967; sterman and Wyrwicka, 1967; Wyrwicka and sterman, 1968; sterman et al., 1969 and Lubar and Shouse, 1976; Lubar and Lubar, 1984). A brief history could be interesting. Sterman's research team conducted a systematic examination of EEG patterns and identified the sensory motor rhythm-SMR (12 -15 Hz) over the Rolandic cortex. They were able to train cats to increase production of this rhythm by providing food as an immediate reward. In later research those cats exhibited a significant improvement in stability when exposed to Hydrazine observed to evoke seizure activity in cats that had not received the SMR increasing. Subsequently, they demonstrated that patients with seizure disorders could develop improved control over epileptiform activity by learning self-regulation of the SMR (Sterman, 2000).

Sterman's procedure was replicated by Lubar who used the same training to reduce the symptoms exhibited by 'hyperkinetic' children. Initially, Lubar and Shouse (1976) reported some improvements in a hyperactive child who had learned to reduce theta and increase production of SMR. Subsequently, Lubar and Lubar (1984) reported that children diagnosed with an attention deficit disorder demonstrated improved attention and behavioral control after being trained to increase production of EEG activity in a fast frequency range (beta) while learning to suppress slow wave activity (theta).

These two primary training approaches provide the foundation for each of the protocols that have been examined in the controlled group studies of neurfeedback for ADHD. In a review study, Monastra (2005) has summarized three neurofeedback protocols that have been investigated in controlled group studies. These research-based protocols are the following (Monastra, 2005):

training sessions to achieve their goals. Training takes place 1-3 times for at least one hour of training per week. Once the original goals of treatment have been met, the client continues

Prior to beginning neurofeedback training an assessment is conducted to examine presenting problems, client history, contributing factors, current medications the patient may be taking, and other relevant information. Interviews, symptom checklists, computer based tests (i.e. CPT, TOVA, IVA), and review of relevant documentation are common components of the assessment. A pre- and post treatment objective assessment of the client's QEEG should be performed. The QEEG objectively assess the functioning of the brain in comparison with normative database (Hammond et al., 2011). One can use QEEG database and topographical brain maps to evaluate the location and type of EEG feature to target for

After reviewing the data gathered during the assessment a training protocol is developed.

Which montage must be used (referential or bipolar)? Which locations are chosen for

The rationale for neurofeedback protocols is based on solid research and clinical practice. Initially, neurofeedback treatments for ADHD are founded on the groundbreaking research conducted by Sterman (roth et al., 1967; sterman and Wyrwicka, 1967; Wyrwicka and sterman, 1968; sterman et al., 1969 and Lubar and Shouse, 1976; Lubar and Lubar, 1984). A brief history could be interesting. Sterman's research team conducted a systematic examination of EEG patterns and identified the sensory motor rhythm-SMR (12 -15 Hz) over the Rolandic cortex. They were able to train cats to increase production of this rhythm by providing food as an immediate reward. In later research those cats exhibited a significant improvement in stability when exposed to Hydrazine observed to evoke seizure activity in cats that had not received the SMR increasing. Subsequently, they demonstrated that patients with seizure disorders could develop improved control over epileptiform activity

Sterman's procedure was replicated by Lubar who used the same training to reduce the symptoms exhibited by 'hyperkinetic' children. Initially, Lubar and Shouse (1976) reported some improvements in a hyperactive child who had learned to reduce theta and increase production of SMR. Subsequently, Lubar and Lubar (1984) reported that children diagnosed with an attention deficit disorder demonstrated improved attention and behavioral control after being trained to increase production of EEG activity in a fast frequency range (beta)

These two primary training approaches provide the foundation for each of the protocols that have been examined in the controlled group studies of neurfeedback for ADHD. In a review study, Monastra (2005) has summarized three neurofeedback protocols that have been investigated in controlled group studies. These research-based protocols are the following

to train for an additional 5 to 10 sessions to prevent relapse (Demos, 2005).

The neurofeedback protocols cover the following questions:

active electrode, reference and ground? How threshold levels are set for each client?

by learning self-regulation of the SMR (Sterman, 2000).

while learning to suppress slow wave activity (theta).

(Monastra, 2005):

 Power of which frequency bandwidth targeted to be changed? Which areas of the brain are to be trained (electrodes location)?

neurofeedback training.

Protocol 1- SMR enhancement/theta suppression: in this protocol, patients (ADHD who present with primary symptoms of hyperactivity and impulsivity) instructed to increase their SMR (12–15 Hz) over one of two sites (C3 or C4) while simultaneously suppressing the production of theta (4–7 or 4–8 Hz) activity. EEG recordings are obtained from one active site, referenced to linked earlobes. Auditory and visual feedback is provided based on patient success in controlling power of theta below and SMR above pretreatment thresholds.

Protocol 2- Theta suppression/beta1 enhancement: In this protocol, patients are reinforced for increasing production of beta1 activity (16–20 Hz) while suppressing theta activity (4–8 Hz). Recordings are obtained at Cz with linked ear references, at FCz-PCz with single ear reference, or at Cz-Pz with ear reference. A variation of this protocol also has been reported in the treatment of ADHD, predominately inattentive type (Fuchs et al., 2003). In this training protocol, theta suppression and beta enhancement are reinforced at C3.

Protocol 3- SMR enhancement/beta2 suppression: in this protocol, children with ADHD, predominately hyperactive/impulsive type, are trained to increase SMR (12–15 Hz) while suppressing beta2 activity (22–30 Hz) (Fuchs et al., 2003) Recordings are obtained at C4 with linked ear reference. In ADHD, combined type, this protocol is used during half of each session. During the other portion of each training session, SMR enhancement/theta suppression at C3 is used. Selection of a neurofeedback protocol should be based on level of experience and training, accreditation, the fraction of the therapist's practice devoted to neurofeedback, reports from clients and objective assessments, and the therapist's specific experience in treating AD/HD (for more information see Monastra, 2005; Demos, 2005; Hammond et al., 2011).

Since the work of Lubar and Shous (1976), numerous studies have used neurofeedback approaches for treating ADHD and reported successful diminution of inattentivity and hyperactivity, and improvement in academic performance and concluded that despite some limitations, neurofeedback may be worthy of further consideration as a viable treatment approach for ADHD (Shouse and Lubar , 1979; Lubar and Lubar, 1984; Lubar et al., 1995; Rossiter and La Vaque, 1995; Linden et al., 1996; Thompson and Thompson, 1998; Kaiser and Othmer, 2000; Carmody et al., 2001; Monastra, 2002; Fuchs et al., 2003; Heywood and Beale, 2003; Cho et al., 2004; Heinrich et al., 2004; Rossiter et al., 2004; Xiong et al., 2005; Kropotov et al., 2005; Beauregard and Levesque, 2006; Levesque et al., 2006; Strehl et al., 2006; Gevensleben et al., 2010; Nazari et al., 2011; for review see Rossiter, 2004; ; Vernon et al., 2004; Monastra, 2005; Butnik, 2005; Friel, 2007; Toplak et al., 2008; John and Prichep, 2009; Coben and Evans, 2011). In an excellent meta-analytic study, Arns et al (2009) investigated results of 15 controlled studies. They concluded that neurofeedback treatment for ADHD can be considered "efficacious and specific" with a high effect size for inattention and impulsivity and a medium for hyperactivity (Arns et al., 2009).

Gevensleben et al (2009) conducted a randomized controlled trial encompassing 102 children with ADHD. In this trial behavioral and neurophysiological effects of neurofeedback, were analyzed in comparison to a computerised attention skills training (as a semi-active control group). They have shown neurofeedback to be superior to control group (Gevensleben et al., 2009). They reported follow-up behavioral data assessed 6

EEG Findings in ADHD and the Application of EEG Biofeedback in Treatment of ADHD 279

treatments, and seeking consultation (Hammond et al., 2011). It should be mentioned that patients with a history of epilepsy should only receive neurofeedback from practitioners who

Some people interested in alternative health react to the neurofeedback with hesitation. Neurofeedback has been considered as a relatively unstudied treatment, and the studies that have been conducted have reportedly been problematic, due to methodological problems such as confounded treatments, inconsistent use of dependent measures, small sample size, and a lack of clinically meaningful dependent measures (Kline, Brann, & Loney, 2002; Waschbusch & Hill, 2003; Loo and Barkley, 2005; Holtmann and Stadler, 2006). In this line,

Is there really an effect that leads to significant modifications in cognition and behavior?

For validating purpose, some controlled studies on healthy subjects (i.e. Egner and Gruzelier, 2001; Egner et al., 2002; Vernon et al., 2003; Egner and Gruzelier, 2004) assessed specific cognitive, neuropsychological and electrocortical effects from training of specific frequency bands. They concluded that the modulation of specific frequency bands led to significant and protocol-specific effects. It seems that despite these validation works much

It has been argued that a potential explanation of the effects of neurofeedback could be cognitive-behavioral training effect as well as client-therapist relationship effect since children are engaging in a training for often 30-50 sessions. Such concerns could be addressed by double-blind controlled studies. Considering the ethical problem of including untreated patient or patient undergoing placebo and the difficulty of conducting a doubleblind placebo controlled study in neurofeedback, some groups (Drechsler et al., 2007; Gevensleben et al., 2009) have still addressed these concerns by comparing neurofeedback group with a semi-active control group (can be considered a credible sham control). In these studies neurofeedback in comparison to this semi-active control group still had medium to large ES for inattention and impulsivity, and small to medium ES for hyperactivity (Arns et

La Vaque and Rossiter (2001) pointed out that, rather than comparing a new treatment (e.g., neurofeedback) to a no-treatment placebo, it should be compared to a protocol of 'known efficacy' to determine whether such an intervention would result in an equivalent effect. This type of design is often referred to equivalent study (Vernon et al., 2004). Regarding the well established efficacy of methylphenidate, several studies have compared the effects of neurofeedback and methylphenidate. Results revealed that although averaged effect size for methylphenidate was greater than for neurofeedback, both were in medium range and the difference was not significant (i.e. Nazari et al., 2011). None of the studies comparing neurofeedback with stimulant medication used random assignment. Although self-selection

are well versed in neurofeedback for seizure disorders.

Does neurofeedback result in the intended EEG changes?

How does it compare to the current standard of treatment?

remains to be done to provide a scientific basis for neurofeedback.

Could these changes be reliably linked to neurofeedback training?

there are some fundamental questions:

 Are these changes retained over time? How does neurofeedback work?

al., 2009).

months after completion of the training (either neurofeedback training or attention skills training). Improvements in the neurofeedback group at follow-up were superior to those of the control group and comparable to the effects at the end of the training. They concluded that "though treatment effects appear to be limited, the results confirm the notion that neurofeedback is a clinically efficacious module in the treatment of children with ADHD" (Gevensleben et al., 2010).

In a clinical outcome study, Nazari et al (2011) investigated whether neurofeedback compared to methylphenidate achieves an equally effective outcome. Participants were 39 children: 13 children with ADHD were trained to enhance the amplitude of the beta1 activity and decrease the amplitude of the theta activity, 13 of which were treated with methylphenidate alone, and 13 healthy children did not receive intervention. Several behavioral, neuropsychological and experimental tests were administered before and after intervention. While behavioral measures were improved by both types of method, methylphenidate was significantly more effective than neurofeedback. Response inhibition (assessed by Stroop) was improved only by neurofeedback. Both neurofeedback and methylphenidate were associated with improvements on the variability and accuracy measures of computerized attention tests. Intellectual ability (measured by full version of WISC-III) increased also by both methods. Although averaged effect size for methylphenidate seems to be greater than for neurofeedback, the difference was not significant. In conjunction with other studies they concluded that neurofeedback can significantly improve several behavioral and cognitive functions in children with ADHD and it might be an alternative treatment for ADHD, particularly for those their parents favor a nonpharmacological treatment (Nazari et al., 2011).

Neurofeedback is contraindicated with subjects under age six years, or subjects with mental retardation, developmental delay or other significant medical, neurological, or psychiatric disease. Subjects from families with significant marital discord that could interfere with participation in the treatment process (Friel, 2007).

Side effect can sometimes occur during neurofeedback and practitioners should be aware that occasionally negative effects may occur (Hammond & Kirk, 2008; Hammond et al., 2001; Lubar & Shouse, 1976; Todder et al., 2010) if training is not being supervised by a knowledgeable and certified professional. Adverse effects that have been reported by some clinicians include increased anxiety and agitation, headaches, fatigue, sleep disturbance, anger and irritability, crying and emotional lability, enuresis, an increase in depression, increase in somatic symptoms (including tics and twitches), seizures, and temporary disorientation. These reports are uncontrolled case reports from which one cannot know the degree to which other confounding events in the patients' lives may have contributed to these negative symptoms (Hammond & Kirk, 2008). However, neurofeedback provider as a health-related profession should promote the welfare of their clients. Therefore, they should perform appropriate and objective assessments prior to, during and after providing neurofeedback to assess regularly the effectiveness of the services provided, and they inquire frequently about any side effects or adverse reactions. When it is observed that side effects or negative effects are occurring, providers document the details, discuss them with the client, and take appropriate action to remediate negative effects as quickly as possible. Such action may include modifying neurofeedback protocols, verifying the amount or frequency of treatment, utilizing adjunctive treatments, and seeking consultation (Hammond et al., 2011). It should be mentioned that patients with a history of epilepsy should only receive neurofeedback from practitioners who are well versed in neurofeedback for seizure disorders.

Some people interested in alternative health react to the neurofeedback with hesitation. Neurofeedback has been considered as a relatively unstudied treatment, and the studies that have been conducted have reportedly been problematic, due to methodological problems such as confounded treatments, inconsistent use of dependent measures, small sample size, and a lack of clinically meaningful dependent measures (Kline, Brann, & Loney, 2002; Waschbusch & Hill, 2003; Loo and Barkley, 2005; Holtmann and Stadler, 2006). In this line, there are some fundamental questions:


278 Current Directions in ADHD and Its Treatment

months after completion of the training (either neurofeedback training or attention skills training). Improvements in the neurofeedback group at follow-up were superior to those of the control group and comparable to the effects at the end of the training. They concluded that "though treatment effects appear to be limited, the results confirm the notion that neurofeedback is a clinically efficacious module in the treatment of children with ADHD"

In a clinical outcome study, Nazari et al (2011) investigated whether neurofeedback compared to methylphenidate achieves an equally effective outcome. Participants were 39 children: 13 children with ADHD were trained to enhance the amplitude of the beta1 activity and decrease the amplitude of the theta activity, 13 of which were treated with methylphenidate alone, and 13 healthy children did not receive intervention. Several behavioral, neuropsychological and experimental tests were administered before and after intervention. While behavioral measures were improved by both types of method, methylphenidate was significantly more effective than neurofeedback. Response inhibition (assessed by Stroop) was improved only by neurofeedback. Both neurofeedback and methylphenidate were associated with improvements on the variability and accuracy measures of computerized attention tests. Intellectual ability (measured by full version of WISC-III) increased also by both methods. Although averaged effect size for methylphenidate seems to be greater than for neurofeedback, the difference was not significant. In conjunction with other studies they concluded that neurofeedback can significantly improve several behavioral and cognitive functions in children with ADHD and it might be an alternative treatment for ADHD, particularly for those their parents favor a non-

Neurofeedback is contraindicated with subjects under age six years, or subjects with mental retardation, developmental delay or other significant medical, neurological, or psychiatric disease. Subjects from families with significant marital discord that could interfere with

Side effect can sometimes occur during neurofeedback and practitioners should be aware that occasionally negative effects may occur (Hammond & Kirk, 2008; Hammond et al., 2001; Lubar & Shouse, 1976; Todder et al., 2010) if training is not being supervised by a knowledgeable and certified professional. Adverse effects that have been reported by some clinicians include increased anxiety and agitation, headaches, fatigue, sleep disturbance, anger and irritability, crying and emotional lability, enuresis, an increase in depression, increase in somatic symptoms (including tics and twitches), seizures, and temporary disorientation. These reports are uncontrolled case reports from which one cannot know the degree to which other confounding events in the patients' lives may have contributed to these negative symptoms (Hammond & Kirk, 2008). However, neurofeedback provider as a health-related profession should promote the welfare of their clients. Therefore, they should perform appropriate and objective assessments prior to, during and after providing neurofeedback to assess regularly the effectiveness of the services provided, and they inquire frequently about any side effects or adverse reactions. When it is observed that side effects or negative effects are occurring, providers document the details, discuss them with the client, and take appropriate action to remediate negative effects as quickly as possible. Such action may include modifying neurofeedback protocols, verifying the amount or frequency of treatment, utilizing adjunctive

(Gevensleben et al., 2010).

pharmacological treatment (Nazari et al., 2011).

participation in the treatment process (Friel, 2007).

For validating purpose, some controlled studies on healthy subjects (i.e. Egner and Gruzelier, 2001; Egner et al., 2002; Vernon et al., 2003; Egner and Gruzelier, 2004) assessed specific cognitive, neuropsychological and electrocortical effects from training of specific frequency bands. They concluded that the modulation of specific frequency bands led to significant and protocol-specific effects. It seems that despite these validation works much remains to be done to provide a scientific basis for neurofeedback.

It has been argued that a potential explanation of the effects of neurofeedback could be cognitive-behavioral training effect as well as client-therapist relationship effect since children are engaging in a training for often 30-50 sessions. Such concerns could be addressed by double-blind controlled studies. Considering the ethical problem of including untreated patient or patient undergoing placebo and the difficulty of conducting a doubleblind placebo controlled study in neurofeedback, some groups (Drechsler et al., 2007; Gevensleben et al., 2009) have still addressed these concerns by comparing neurofeedback group with a semi-active control group (can be considered a credible sham control). In these studies neurofeedback in comparison to this semi-active control group still had medium to large ES for inattention and impulsivity, and small to medium ES for hyperactivity (Arns et al., 2009).

La Vaque and Rossiter (2001) pointed out that, rather than comparing a new treatment (e.g., neurofeedback) to a no-treatment placebo, it should be compared to a protocol of 'known efficacy' to determine whether such an intervention would result in an equivalent effect. This type of design is often referred to equivalent study (Vernon et al., 2004). Regarding the well established efficacy of methylphenidate, several studies have compared the effects of neurofeedback and methylphenidate. Results revealed that although averaged effect size for methylphenidate was greater than for neurofeedback, both were in medium range and the difference was not significant (i.e. Nazari et al., 2011). None of the studies comparing neurofeedback with stimulant medication used random assignment. Although self-selection

EEG Findings in ADHD and the Application of EEG Biofeedback in Treatment of ADHD 281

American Psychiatric Association. Diagnostic and Statistical Manual of Mental Disorders,

Arns M, de Ridder S, Strehl U, Breteler M, Coenen A. Efficacy of neurofeedback treatment in

Barkley RA. Behavioral inhibition, sustained attention, and executive functions: constructing

Barry RJ, Clarke AR, Johnstone SJ. A review of electrophysiology in attention-

Beauregard M, Levesque J. Functional magnetic resonance imaging investigation of the

Boutros NN, IaconoWG, Galderisi S. Applied electrophysiology. In Sodock, B. J., Sodock, V.

Carmody DP, Radvanski DC, Wadhwani S, Sabo MJ, Vergara L. EEG biofeedback training

Cho BH, Kim S, Shin DI, Lee JH, Lee SM, Kim IY, et al. Neurofeedback training with

Coben and Evans (eds). Neurofeedback and neuromodulation techniques and applications.

Drechsler R, Straub M, Doehnert M, Heinrich H, Steinhausen H, Brandeis D. Controlled

Drongelen W van . Signal Processing for Neuroscientists: Introduction to the Analysis of

Egner T, Gruzelier JH. EEG biofeedback of low beta band components: frequency-specific

Egner T, Gruzelier JH. Learned self-regulation of EEG frequency components affects

Demos JN. Getting Started with Neurofeedback. (2005). W.W. Norton & Co.

Physiological Systems. 2007. Elsevier, Amsterdam.

ADHD: The effects on inattention, impulsivity and hyperactivity: A meta-analysis.

deficit/hyperactivity disorder: I. Qualitative and quantitative

effects of neurofeedback training on the neural bases of selective attention and response inhibition in children with attention-deficit/hyperactivity disorder. Appl

A. and Rouiz, P. (editors). Kaplan and Sodock's comprehensive textbook of psychiatry. Ninth edition, Philadelphia: Lippincott Williams & Wilkins. 2009. Budzynski TH, Budzynski HK, Evans JR, Abarbanel A. Introduction to Quantitative EEG and Neurofeedback: advanced theory and applications. Elsevier Inc. 2009. Butnik SM. Neurofeedback in adolescents and adults with attention deficit hyperactivity

and Attention-Deficit/Hyperactivity Disorder in an elementary school setting.

virtual reality for inattention and impulsiveness. Cyberpsychol Behav.

evaluation of a neurofeedback training of slow cortical potentials in children with

effects on variables of attention and event-related brain potentials. Clin.

attention and event-related brain potentials in humans. NeuroReport. 2001;12:4155-

4th edn, American Psychiatric Press, Washington DC. 1994

a unifying theory of ADHD. Psychol Bull. 1997;121(1):65-94.

electroencephalography. Clin Neurophysiol. 2003;114(2):171-183.

Clinical EEG and Neuroscience. 2009;40(3):180-189.

Psychophysiol Biofeedback. 2006;31(1):3-20.

disorder. J Clin Psychol. 2005;61(5):621-5.

Journal of Neurotherapy. 2001;4:5−27.

ADHD. Behav Brain Funct. 2007; 3:35.

Neurophysiol. 2004;115:131-139.

2004;7(5):519-26.

Elsevier Inc. 2011

4159.

**2. References** 

the treatment may bias these findings, self selection potentially maximizes the effects of expectancy in both groups. However, more studies using randomization and larger sample sizes are needed to investigate further how neurofeedback compares to stimulant medication in the treatment of ADHD.

Several follow-up studies (Monastra et al., 2002; Strehl et al., 2006; Gani et al., 2008; Gevensleben et al., 2010) showed that improvements in behavior and attention turned out to be stable. Test results for attention and some of the parents' ratings once more improved significantly. Based on these researches, it can be concluded that the clinical effects of neurofeedback are stable and might even improve further with time. This, in contrast to stimulant medication where it is known that when the medication is stopped often the initial complaints will come back again and recent evidence showing that temporary treatment with stimulant medication is not likely to improve long-term outcomes (Molina et al., 2009).

Yet another domain in need of further investigation involves the theoretical basis and the underlying mechanisms of neurofeedback impact. Today, to understand "how does neurofeedback work?" is one of the most interesting and challenging tasks. It is not surprising that the field of neuroscience attracts a lot of researchers try to answer this question.

Despite some limitations, neurofeedback may be worthy of further consideration as a viable treatment approach for ADHD (See Vernon, 2005; Friel, 2007; Toplak et al., 2008; Yucha and Montgomery, 2008; Arns et al., 2009). On the basis of currently available research results, the success of this therapeutic method is indicated by widespread utilization, and reports of carefully designed studies suggest the utility of this method (John and Prichep, 2009). EEG biofeedback therapy for AD/HD results in significant improvement in cognitive functioning for 75-85 percent of patients. It is possible that faster and better outcomes might be achieved by combining other alternative therapies with EEG biofeedback (Friel, 2007). Neurofeedback meets the American Academy of Child and Adolescent Psychiatry criteria for clinical guideline for treatment of ADHD. As mentioned before, meta-analysis results of Arns and his colleagues (2009) demonstrated that neurofeedback treatment for ADHD can be considered "efficacious and specific".

Frank H. Duffy, M.D., Professor and Pediatric Neurologist at Harvard Medical School, stated in an editorial in the January 2000 issue of the Journal Clinical Electroencephalography that the scholarly literature suggests that neurofeedback should play a major therapeutic role in many difficult areas:

*"In my opinion, if any medication had demonstrated such a wide spectrum of efficacy it would be universally accepted and widely used" (p. v). "It is a field to be taken seriously by all." (p. vii).* 

Until 2005, neurofeedback was reportedly used by more than 1500 practitioners (Butnik, 2005) and the last years have seen a rapid growth of the field of neurofeedback in the US and at least 27 countries (Budzynski et al., 2009). There are more than 100 health-related professions in Iran that using neurofeedback in their routine clinical practice. All of them have been trained by the Biofeedback Foundation of Europe-BFE (www.bfe.org) instructors in the Paarand Specialized Center for Human Enhancement-PSCHE (www.paarand.org).

#### **2. References**

280 Current Directions in ADHD and Its Treatment

the treatment may bias these findings, self selection potentially maximizes the effects of expectancy in both groups. However, more studies using randomization and larger sample sizes are needed to investigate further how neurofeedback compares to stimulant

Several follow-up studies (Monastra et al., 2002; Strehl et al., 2006; Gani et al., 2008; Gevensleben et al., 2010) showed that improvements in behavior and attention turned out to be stable. Test results for attention and some of the parents' ratings once more improved significantly. Based on these researches, it can be concluded that the clinical effects of neurofeedback are stable and might even improve further with time. This, in contrast to stimulant medication where it is known that when the medication is stopped often the initial complaints will come back again and recent evidence showing that temporary treatment with stimulant medication is not likely to improve long-term outcomes (Molina et

Yet another domain in need of further investigation involves the theoretical basis and the underlying mechanisms of neurofeedback impact. Today, to understand "how does neurofeedback work?" is one of the most interesting and challenging tasks. It is not surprising that the field of neuroscience attracts a lot of researchers try to answer this

Despite some limitations, neurofeedback may be worthy of further consideration as a viable treatment approach for ADHD (See Vernon, 2005; Friel, 2007; Toplak et al., 2008; Yucha and Montgomery, 2008; Arns et al., 2009). On the basis of currently available research results, the success of this therapeutic method is indicated by widespread utilization, and reports of carefully designed studies suggest the utility of this method (John and Prichep, 2009). EEG biofeedback therapy for AD/HD results in significant improvement in cognitive functioning for 75-85 percent of patients. It is possible that faster and better outcomes might be achieved by combining other alternative therapies with EEG biofeedback (Friel, 2007). Neurofeedback meets the American Academy of Child and Adolescent Psychiatry criteria for clinical guideline for treatment of ADHD. As mentioned before, meta-analysis results of Arns and his colleagues (2009) demonstrated that neurofeedback treatment for ADHD can be

Frank H. Duffy, M.D., Professor and Pediatric Neurologist at Harvard Medical School, stated in an editorial in the January 2000 issue of the Journal Clinical Electroencephalography that the scholarly literature suggests that neurofeedback should

*"In my opinion, if any medication had demonstrated such a wide spectrum of efficacy it would be universally accepted and widely used" (p. v). "It is a field to be taken seriously by all." (p. vii).*  Until 2005, neurofeedback was reportedly used by more than 1500 practitioners (Butnik, 2005) and the last years have seen a rapid growth of the field of neurofeedback in the US and at least 27 countries (Budzynski et al., 2009). There are more than 100 health-related professions in Iran that using neurofeedback in their routine clinical practice. All of them have been trained by the Biofeedback Foundation of Europe-BFE (www.bfe.org) instructors in the Paarand Specialized Center for Human Enhancement-PSCHE (www.paarand.org).

medication in the treatment of ADHD.

considered "efficacious and specific".

play a major therapeutic role in many difficult areas:

al., 2009).

question.


EEG Findings in ADHD and the Application of EEG Biofeedback in Treatment of ADHD 283

Holtmann M, Stadler C. Electroencephalographic biofeedback for the treatment of attention-

Hughes JR, John ER. Conventional and quantitative electroencephalography in psychiatry. J

Jasper HH, Solomon P, Bradley C. Electroencephalographic analysis of behavior problems in

John ER, Prichep LS. Principles and applications of quantitative electroencephalography in

Kaiser DA, Othmer S. Effects of neurofeedback on variables of attention in a large multi-

Klimesch W, Schimke H, Doppelmayr M, Ripper B, Schwaiger J, Pfurtscheller G. Event-

Kline JP, Brann CN, Loney BR. A cacophony in the brainwaves: A critical appraisal of

Kropotov JD, Grin-Yatsenko VA, Pomarev VA, Chutko LS, Yakovenko EA, Nikishena IS.

La Vaque TJ, Rossiter T. The ethical use of placebo controls in clinical research: the Declaration of Helsinki. Appl Psychophysiol Biofeedback. 2001;26(1):23-37. Levesque J, Beauregard M, Mensour B. Effect of neurofeedback training on the neural

Linden M, Habib T, Radojevic V. A controlled study of the effects of EEG biofeedback on

Loo SK, Barkley RA. Clinical utility of EEG in attention deficit hyperactivity disorder. Appl

Lubar JF, Shouse MN. EEG and behavioral changes in a hyperkinetic child concurrent with

Lubar JF, Swartwood MO, Swartwood JN, O'Donnell PH. Evaluation of the effectiveness of

Lubar JO, Lubar JF. Electroencephalographic biofeedback of SMR and beta for treatment of

disabilities. Biofeedback Self Regul. 1996;21(1):35-49.

Neurotherapeutics 2006; 6(4): 533-540.

Lippincott Williams & Wilkins. 2009.

Health Practice. 2002;1:46-56.

Psychophysiol. 2005;55:23-34.

Neuropsychol 2005; 12(2): 64-76.

Regul. 1976;1(3):293-306.

Regul. 1995;20(1):83-99.

23.

2006;394(3):216-21.

47-60.

center trial. J Neurotherapy. 2000;4(1):5-5.

Neuropsychiatry Clin Neurosci. 1999;11(2):190-208.

children. *American Journal of Psychiatry*, 1938;95:641-658.

deficit hyperactivity disorder in childhood and adolescence. Expert Rev

psychiatry. In Sodock, B. J., Sodock, V. A. and Rouiz, P. (editors). Kaplan and Sodock's comprehensive textbook of psychiatry. Ninth edition, Philadelphia:

related desynchronization (ERD) and the Dm-effect: does alpha desynchronization during encoding predict later recall performance? Int. J. Psychophysiol. 1996; 24:

neurotherapy for Attention Deficit Disorders. The Scientific Review of Mental

ERPs correlates of EEG relative beta training in ADHD children. Int J

substrates of selective attention in children with attention-deficit/hyperactivity disorder: a functional magnetic resonance imaging study. Neurosci Lett.

cognition and behavior of children with attention deficit disorder and learning

training of the sensorimotor rhythm (SMR): a preliminary report. Biofeedback Self

EEG neurofeedback training for ADHD in a clinical setting as measured by changes in T.O.V.A. scores, behavioral ratings, and WISC-R performance. Biofeedback Self

attention deficit disorders in a clinical setting. Biofeedback Self Regul. 1984;9(1):1–


Egner T, Strawson E, Gruzelier JH. EEG signature and phenomenology of alpha/theta

Friel PN. EEG Biofeedback in the Treatment of Attention Deficit/Hyperactivity Disorder.

Fuchs T, Birbaumer N, Lutzenberger W, Gruzelier JH, Kaiser J. Neurofeedback treatment for

Gevensleben H, Holl B, Albrecht B, Schlamp D, Kratz O, Studer P, Rothenberger A, Moll

Gevensleben H, Holl B, Albrecht B, Vogel C, Schlamp D, Kratz O, et al. Is neurofeedback an

Hammond D. Corydon , Bodenhamer-Davis, Genie , Gluck, Gerald , Stokes, Deborah ,

Hammond DC, Gunkelman J. The art of artifacting. Corpus Christi, TX: International Society

Hammond DC, Kirk L. First, do no harm: Adverse effects and the need for practice standards in neurofeedback. Journal of Neurotherapy. 2008;12(1):79–88. Hammond DC, Stockdale S, Hoffman D, Ayers ME, Nash J. Adverse reactions and potential

Hammond DC, Walker J, Hoffman D, Lubar JF, Trudeau D, Gurnee R, Horvat J. Standards

Heinrich H, Gevensleben H, Freisleder FJ, Moll GH, Rothenberger A. Training of slow

behavioral and neurophysiological effects. Biol Psychiatry. 2004;55(7):772-5. Heywood C, Beale I. EEG biofeedback vs. placebo treatment for attentiondeficit/hyperactivity disorder: a pilot study. J Atten Disord. 2003;7(1):43-55. Hoffman DA, Lubar JF, Thatcher RW, Sterman MB, Rosenfeld PJ, Striefel S, et al..

for Neuronal Regulation. Journal of Neurotherapy. 2004;8(1): 5–26.

methylphenidate. Appl Psychophysiol Biofeedback. 2003;28(1):1-12. Gani C, Birbaumer N, Strehl U. Long term effects after feedback of slow cortical potentials

disorder (ADHD). Int J Bioelectromagn. 2008;10(4):209-232.

Alternative Medicine Review. 2007;12(2):146-151.

Psychol Psychiatry. 2009;50(7):780-789.

Neurotherapy. 2011;15(1):54-64.

for Neuronal Regulation. 2001.

Neuroscience. 1999;11(3):401-405.

2002;27:261-270.

24.

69.

neurofeedback training versus mock feedback. Appl. Psychophysiol. Biofeedback.

attention-deficit/hyperactivity disorder in children: a comparison with

and of theta-beta-amplitudes in children with attentiondeficit/hyperactivity

GH, Heinrich H. Neurofeedback training in children with ADHD: 6-month followup of a randomized controlled trial. Eur Child Adolesc Psychiatry. 2010;19(9):715-

efficacious treatment for ADHD? A randomised controlled clinical trial. J Child

Harper, Sara Hunt , Trudeau, David , MacDonald, Margaret , Lunt, Joy and Kirk, Lynda .Standards of Practice for Neurofeedback and Neurotherapy: A Position Paper of the International Society for Neurofeedback & Research. Journal of

iatrogenic effects in neurofeedback training. Journal of Neurotherapy. 2001;4(4):57–

for the use of QEEG in neurofeedback: A position paper of the International Society

cortical potentials in attention-deficit/hyperactivity disorder: evidence for positive

Limitations of the American Academy of Neurology and American Clinical Neurophysiology Society paper on QEEG. Journal of Neuropsychiatry & Clinical


EEG Findings in ADHD and the Application of EEG Biofeedback in Treatment of ADHD 285

Sgrok, M., Roberts, W., Grossman, S., & Barozzine, T. School board survey of attention

Shouse MN, Lubar JF. Operant conditioning of EEG rhythms and ritalin in the treatment of

Snyder SM, Hall JR. A meta-analysis of quantitative EEG power associated with attentiondeficit hyperactivity disorder. J Clin Neurophysiol. 2006;23(5):440-455. Sterman MB, Wyrwicka W, Roth SR. Electrophysiological correlates and neural substrates of

Sterman MB, Wyrwicka W. EEG correlates of sleep: evidence for separate forebrain

Sterman MB. Basic concepts and clinical findings in the treatment of seizure disorders with

Strehl U, Leins U, Goth G, Klinger C, Hinterberger T, Birbaumer N. Self-regulation of slow

Thatcher TW. EEG normative databases and EEG biofeedback. Journal of Neurotherapy.

Thompson L, Thompson M. Neurofeedback combined with training in metacognitive

Todder D, Levine J, Dwolatzky T, Kaplan Z. Case report: Impaired memory and

Vernon DJ. Can neurofeedback training enhance performance? An evaluation of the

Vernon D, Frick A, Gruzelier J. Neurofeedback as a Treatment for ADHD: A Methodological

Vernon D, Egner T, Cooper N, Compton T, Neilands C, Sheri A, Gruzelier JH. The effect of

Waschbusch DA, Hill, GP. Empirically supported, promising, and unsupported treatments

Wywricka W, Sterman MB. Instrumental conditioning of sensorimotor cortex EEG spindles

Disorder (ADHD). Clin Psychol Rev. 2008; 28(5):801-823.

cortical potentials: a new treatment for children with attention-deficit/hyperactivity

strategies: effectiveness in students with ADD. Appl Psychophysiol Biofeedback.

disorientation induced by delta band down-training over the temporal brain regions by neurofeedback treatment. Journal of Neurotherapy. 2010;14:153–155. Toplak ME, Connors L, Shuster J, Knezevic B, Parks S. Review of cognitive, cognitive-

behavioral, and neural-based interventions for Attention-Deficit/Hyperactivity

evidence with implications for future research. Appl Psychophysiol Biofeedback,

Review with Implications for Future Research. Journal of Neu-rotherapy.

training distinct neurofeedback protocols on aspects of cognitive performance. Int.

for children with Attention-Deficit/ Hyperactivity Disorder. In S. O. Lilienfield, S. Jay Lynn, & J. M. Lohr (Eds.), Science and pseudoscience in clinical psychology.

alimentary behavior in the cat. Ann N Y Acad Sci. 1969;157:723–39.

EEG operant conditioning. Clin Electroencephalogr. 2000;31:45–55.

therapy. Pediatric Child Health, 2000; 5, 12\_23.

substrates. Brain Res. 1967;6:143– 63.

disorder. Pediatrics. 2006;118(5):1530-40.

1998; 2(4):8–39.

1998;23(4):243-63.

2005;30(4):347-364.

2004;8(2):53-82..

J. Psychophysiol. 2003;47:75-85.

New York: Guilford Press, 2003; 333-362.

in the waking cat. Physiol Behav. 1968;3:703–7.

hyperkinesis. Biofeedback Self Regul. 1979;4(4):299-312.

deficit/hyperactivity disorder: Prevalence of diagnosis and stimulant medication


Lubar JF. Neurofeedback for the management of attention- deficit/hyperactivity disorders.

Molina BS, Hinshaw SP, Swanson JM, Arnold LE, Vitiello B, Jensen PS, Epstien JN, Hoza B,

Monastra VJ, Monastra DM, George S. The effects of stimulant therapy, EEG biofeedback,

Monastra VJ. Electroencephalographic biofeedback (neurotherapy) as a treatment for

Nazari MA, Querne L, de Broca A, Berquin P. Effectiveness of EEG biofeedback as

disorder: A clinical outcome study. Neuroscience & Medicine. 2011;2:78-86. Nazari MA, Wallois F, Aarabi A, Berquin P. Dynamic changes in quantitative

Nazari MA. Electroencephalographic characteristic of children with attention-

Niedermeyer E, da Silva FL. Electroencephalography: Basic Principles, Clinical Applications, and Related Fields. Lippincot Williams & Wilkins. 2004. Nuwer M. Assessment of digital EEG, quantitative EEG and EEG brain mapping: report of

Pasqual-Marqui RD, Michel CM, Lehmann D. Low resolution electromagnetic tomography:

Rossiter T. The effectiveness of neurofeedback and stimulant drugs in treating AD/HD: Part

Rossiter T. The effectiveness of neurofeedback and stimulant drugs in treating AD/HD: part

Rossiter TR, La Vaque TJ. A comparison of EEG biofeedback and psychostimulants in

Roth SR, Sterman MB, Clemente CC. Comparison of EEG correlates of reinforcement,

II. Replication. Appl Psychophysiol Biofeedback. 2004;29(4):233-43.

disorder. Appl Psychophysiol Biofeedback. 2002;27(4):231-49.

Adolesc Psychiatr Clin N Am. 2005;14(1):55-82.

Guilford Press. 1995.

2009;48(5):461-462.

ahead of print]

France. 2008.

1994;18:49-65.

1995;1:48−59.

112.

20.

Society. Neurology. 1997;49:277-292.

In M. S. Schwartz (Ed.), biofeedback: A practitioner's guide. 2nd edition, New York:

Hechtman L, Abikoff HB, et al. Prospective follow-up of children treated for combined type ADHD in a multisite study. J Am Child Adolesc Psych.

and parenting style on the primary symptoms of attention-deficit/hyperactivity

attention deficit hyperactivity disorder: rationale and empirical foundation. Child

compared with Methylphenidate in the treatment of attention-deficit/hyperactivity

electroencephalogram during continuous performance test in children with attention-deficit/hyperactivity disorder. Int. J. Psychophysiol. 2011 Jul 14. [Epub

deficit/hyperactivity disorder and evaluation of the effects of neurofeedback on attentional fonctions.Ph.D. dissertation, University of Picardie Jules Verne, Amiens,

the American Academy of Neurology and the American Clinical Neurophysiology

a new method for localizing electrical activity in the brain. Int J Psychophysiol.

I. Review of methodological issues. Appl Psychophysiol Biofeedback. 2004;29(2):95-

treating attention deficit/hyperactivity disorders. Journal of Neurotherapy.

internal inhibition, and sleep. Electroencephalogr Clin Neurophysiol .1967;23:509–


**15** 

**The Effect of Psycho-Educational Therapy** 

**Attention Deficit Hyperactivity Disorder** 

Attention deficit/Hyperactivity (ADHD) disorder, a neuropsychiatric disorder characterized by inattention, hyperactivity and impulsivity, has been precisely described by the American Psychiatric Association (APA, DSM-IV, 2000). According to the diagnostic criteria these main symptoms must appear before the age of 7 years, be present for at least 6 months, be evident in at least two settings, and the impairment must contribute to social, academic, or occupational dysfunction. An increasing number of patients, mostly children, are diagnosed with ADHD. Recent studies focusing on the epidemiology of the disorder, reported variable prevalence of ADHD in childhood that reached in some studies the percentage of 14% (Barbaresi et al, 2004; Froehlich et al, 2007; Pliszka, 2007; Merinkagas et al, 2010; CDC, 2010). It is estimated that 30-70% of children with ADHD continue to experience ADHD symptoms

into adulthood (Vollmer, 1998; Wender, 1997; Mannuzza et al, 1991; Elia et al, 1999).

The therapeutic approach of ADHD includes several treatment modalities (pharmacotherapy, behavioral, psychological and educational interventions). The choice of treatment depends mainly on the patient's characteristics such as age at diagnosis, the presence of co-morbid conditions and the therapeutic goals. Behavioral interventions alone have not been effective in ameliorating the core symptoms of ADHD to a significant extent (The Multimodal Treatment study of Children with ADHD [MTA], 1999; Brown et al, 2005). Their main contribution is the improvement of the behavioral problems that frequently accompany ADHD (Kolko et al, 1999; Pelham et al, 1998). Pharmacotherapy is one of the most efficacious treatment approaches both in children and adolescents. Stimulants have been for many years the first-line treatment for ADHD with a response rate of approximately 70% or more when patients are strictly complying with the treatment (MTA, 1999; Miller et al, 1998; Schachter et al, 2001). Atomoxetine seems to be about equally effective to stimulants, even though in a recent meta-analysis it was reported that stimulants (especially the long-lasting ones) demonstrated greater efficacy than atomoxetine during the short duration of

**1. Introduction** 

**on Electroencephalographic** 

Georgia Kleidaria2 and Antigone Papavasiliou1 *1Department of Neurology, Pendeli Children's Hospital, Athens* 

**Biofeedback Scores in** 

Irene Nikaina1, Aspa Paspali2,

*2DIKEPSY, Athens* 

*Greece* 

