Electroencephalogram (EEG) Biofeedback for Reading Disability and Traumatic Brain Injury

Kirtley E. Thornton, PhDa,*, Dennis P. Carmody, PhDb
aCenter for Health Psychology, Suite 2A, 2509 Park Avenue, South Plainfield, NJ 07080, USA
bInstitute for the Study of Child Development, Department of Pediatrics,
Robert Wood Johnson Medical School, University of Medicine and Dentistry of New Jersey,
97 Paterson Street, New Brunswick, NJ 08903, USA

Reading disabilities

Prevalence and costs

Reading disabilities present major challenges to the educational system. The
estimated prevalence rate for learning disabilities is 15% of the student
population [1], with 6.5 million children requiring special education in 2002
[2]. Approximately 63% of these special education children have specific
learning disabilities or speech and language problems without a concomitant
physical disability. Between 28% and 43% of inmates in adult correctional
facilities require special education (versus 5% in normal population), and 82% of
prison inmates in the United States are school dropouts [3]. Large financial and
social costs are associated with programs to address learning disabilities. The
federal government spent $350 billion over a 20-year period on special education
programs [4], and New York City spends $55,300 per year for each incarcerated
youth [3].

Neuroscience of reading disability

The underlying physical basis of reading disability condition is confirmed in
studies that examined the activity of neurotransmitters, magnetic fields, blood flow, and deviant response patterns on physical measures and neuropsychological instruments. Galaburda et al [5] conducted postmortem examinations on four
dyslexic subjects and observed abnormal neuronal development (dysplasias, extra
large neurons) along the left hemisphere superior temporal lobes (Peri-Sylvian regions) and frontal lobes.

Neuroimaging studies using positron emission tomography, magnetic electroencephalography,
and functional MRI have identified differences in the functional
organization between dyslexic and typical readers. Temple et al [6] and
McCandliss and Noble [7] reviewed the literature on functional neuroimaging
of dyslexia in adult and pediatric samples. A summary of the two reviews serves
to identify the brain regions associated with dyslexia.

Dyslexic adults show dysfunction in the left temporoparietal cortex during
phonologic processing of visual stimuli as evidenced by positron emission tomography
studies [8]. Specifically, the dysfunction is located in the superior
temporal gyrus and inferior parietal cortex, particularly in the left hemisphere
[9,10]. Functional MRI studies confirmed this finding in adults who showed
decreased activity in temporoparietal regions, including superior temporal gyrus
and angular gyrus, during phonologic processing of letters and pseudoword
rhyme [11]. Dyslexic children aged 8 to 12 years who underwent functional MRI
showed reduced temporoparietal activity during phonologic tasks, which
suggested that the disruption is fundamental to the disorder and is not a
compensation effect that occurs with maturation.

In addition to identifying the areas of activation, magnetic electroencephalography
provides data about the temporal course of activation. A regular
progression of activation was found for normal and dyslexic readers from
occipital, to basal temporal region, to the temporoparietal areas, which consists of
the posterior portions of superior and middle temporal gyri and the angular and
supramarginal gyri [12]. Dyslexic readers had onset latencies similar to normal
readers in the activation of all areas except left temporoparietal, an area known
to be involved in word recognition and phonologic analysis. These findings
indicated that the left temporoparietal area is slow to respond and responds
with less activation in dyslexic readers than in nonimpaired readers.

A magnetic electroencephalographic study was conducted on a sample of
45 children (5–7 years old) at the beginning of their reading experience who were
either at risk for reading difficulties or not [13]. The imaging scans showed that
children at risk had greater right hemisphere activity, whereas children not at risk
had greater activity in left posterior superior temporal gyrus. These findings
suggested that the dysfunction occurs early in development.

Several functional MRI neuroimaging studies have compared cortical
activation patterns under reading-related tasks in readers with dyslexia and
control groups of nonimpaired readers [11,14,15]. This series of studies showed
that nonimpaired adults increased their activation in posterior superior temporal
gyrus, angular gyrus, and supramarginal gyrus as the task demands increased
from orthographic comparisons to phonologic comparisons [11]. In contrast,
adults with dyslexia showed overactivation in response to increasing task
demands in anterior regions, including the inferior frontal gyrus. Whereas nonimpaired
readers showed activation of a widely distributed system for reading,
the readers with dyslexia had disrupted activity in the posterior cortex, which
involves traditional attentional, visual, and language areas.

The anatomic correlates of the dysfunction in left temporoparietal regions can
be visualized by diffusion tensor imaging, which identifies white matter tracts
[16]. Using diffusion tensor imaging, Klingberg et al [17] showed that reading
ability is directly related to the degree of anisotropy (water diffusion and the
direction of diffusion within each voxel) of white matter in left temporoparietal
regions for readers with dyslexia and nonimpaired readers. There are functional
equivalents of the structural connectivity. For example, in a positron emission
tomographic study of adults, the nonimpaired readers—but not the readers
with dyslexia—showed correlated activation between angular gyrus and lingual
and fusiform gyri and the left superior temporal gyrus and left inferior frontal
area [18].

In summary, the left temporoparietal region is disrupted in developmental
dyslexia. The magnitude of activation is low, and there is decreased coordination
of activity between the left superior temporal gyrus and left frontal areas. The
evidence indicates that the disruption is in place before children learn to read, is
related to difficulties with phonologic processing, and is related to underdevelopment
of white matter fibers in the region.

Efficacy research on intervention programs for students with learning disabilities

Despite the enormity of the social and educational problem, the interventions
currently used largely have been unsuccessful in obtaining significant and
meaningful results. In 1988, Lyon and Moats [19] concluded that ‘‘It is difficult,
if not impossible, to find any evidence beyond testimonials and anecdotal reports
that support the assumptions, treatment methods, and stated outcomes associated
with medical and psycho educational models . . . [T]here is overwhelming
empirical and clinical data indicating that medical and psycho educational
models, as they are presently conceived and used, are inadequate for determining
what and how to teach learning disabled students.’’ More recently, Birsh [20]
concluded that ‘‘despite the widespread inclusion of multisensory techniques in
remedial programs for dyslexic students and a strong belief among practitioners
using these techniques that they work, there was little empirical evidence to
support the techniques’ theoretical premises.’’

A comparison of the research results with current popular approaches indicates
an average improvement of +0.34 standard deviations (SD) (standardized testing,
350 intervention hours, n = 48, control group) for the Orton-Gillingham program
[21], an average improvement of 13% (standardized testing, 125 intervention
hours, n = 171, no control group) for the Lindamood-Bell program [22], and an
average +0.40 SD improvement (standardized testing, 100 intervention sessions,
n = 130, control group) for the Fast ForWord program [23]. The Orton-
Gillingham videotape obtained the same results as individual tutoring with
this method. Increases in reading abilities can be accompanied by magnetic
electroencephalographic images, however, showing that after an intervention of
80 hours of one-on-one instruction in phonologic structure, children with dyslexia
increased their activations in left posterior superior temporal gyrus, left
supramarginal gyrus, and angular gyrus [24].

Traumatic brain injury

Prevalence and costs

An estimated 5.3 million Americans (2% of the population) currently live with
disabilities that resulted from traumatic brain injury (TBI). Each year, 1.5 million
Americans sustain a TBI, with a new case added every 21 seconds, which leads to
80,000 new cases of long-term disability and 50,000 deaths. Although the causes
of TBI are many, the leading causes are car accidents (44%) and falls (26%),
which involve adolescent, young adult, and elderly populations [25]. The costs of
TBI in the United States are estimated at $48.3 billion a year, with hospitalization
costs of $31.7 billion and fatality costs of $16.6 billion.

Neuroscience of traumatic brain injury

Most studies on the biomechanical effects of closed head injury have
concluded that three force vectors contribute to the injury: a rotational vector, a
sheer vector, and a centripetal force vector, which is maximal at the outer cortex
with a gradient to the subcortex and brain stem. The geometrical summation of
these forces results in maximum injury in that part of the brain that is in contact
with the skull (eg, the gray matter of the frontal and temporal lobes), which
largely occurs independent of the direction of impact to the skull. Two other
invariant consequences of blunt force injuries to the skull are (1) sheer forces that
are maximal at the boundaries between different densities of tissue (eg, gray
versus white matter) and (2) a percussion shock wave that travels from the point
of impact and makes contact with the opposite side of the skull in less than
100 milliseconds, which results in a ‘‘coup-contra-coup’’ injury. All these forces
are capable of seriously disrupting the molecular integrity and function of cortical
neurons and glia [26].

The theoretical interpretations of biomechanical effects of TBI have found
support in modern neurodiagnostic testing and their correlates with cognitive
function. For example, patients with TBI have increased delta amplitudes and
increased white matter signal on T2 MRI indicating dysfunction, and there are
associations of decreased alpha and beta amplitudes with increased gray matter
T2 MRI relaxation times [27]. Although increases in both relaxation times were
associated with cognitive dysfunction, decreased alpha and beta amplitudes also
were associated with decreased cognitive function.

Commonly reported cognitive and psychological consequences of TBI include
difficulties with ‘‘orientation/concentration, overload-breakdown of comprehension,
reasoning and problem solving, organizational skills, rate of processing,
rate of performance, perseveration (a tendency to repeat a response or activity
after it has proven ineffective), staying on task/topic, initiation/motivation,
generalization, agitation, fatigue, stress and memory (possibly the most common
residual effect of brain injury and one that families generally find the most
troubling)’’ [25].

Efficacy research on intervention programs for traumatic brain injury

The research literature on memory improvement in patients with brain injury
generally has found minimal to mixed results for several intervention approaches.
One of the initial reviews in this area concluded that ‘‘findings regarding the
effectiveness of memory remediation interventions have been inconsistent,’’
adding that methodologic inadequacies have hindered the identification of
specific treatment effects [28]. Memory is not improved by simple, repetitive
practice [29] or by repetitive recall drills [30]. Specific techniques, such as
visualization, method of loci, and cognitive strategies, have shown different
degrees of effectiveness. Researchers generally agree that the subject does not
continue the use of the strategy after treatment ends [31]. Significant improvements
from internal memory aids, such as imagery instructions, are used less than
external memory aids, but patients on their own generally use neither.

More recent reviews of the literature report similar mixed to negative
conclusions on the efficacy of cognitive rehabilitation therapy for memory and
other areas of cognition and behavior [32–34]. In their exhaustive review, Carney
et al [32] concluded that ‘‘specific forms of cognitive rehabilitation reduce
memory failures (notebook training/electronic cueing devices—results didn’t
hold 6 months post treatment) and anxiety, and improve self-concept and
interpersonal relationships for persons with TBI.’’ A recent Defense and
Veteran’s Head Injury program study did not find any significant improvement
on their measures as a result of cognitive rehabilitation (compared with control
group) in patients with moderate to severe TBI [34]. In conclusion, no definitive
scientific evidence indicates that cognitive rehabilitation leads to sustained
improvements in memory.

Electroencephalogram and neurofeedback

What is the quantitative electroencephalogram?

The quantitative electroencephalogram (QEEG) is a digitization of the traditional
analog EEG signal. Instead of the EEG oscilloscope tracings being
printed directly onto paper, the computer obtains information on the waveform
being generated, displays the signal on a computer screen, and saves that
information. This process makes it possible to recreate the waveform at a later
time for computer display and statistical analysis. With this new capability for
storage and quantitative analysis, the EEG of an individual can be compared with
a database of individuals without any known neurologically based disorder,
which allows for the analysis of the background activity to reveal patterns not
apparent in the visual inspection of the routine EEG. For a review of the literature
in this area, see the article by Chabot et al elsewhere in this issue.

The waveforms generated by the 3-mm cortical gray matter just below the
scalp are measured based on the number of times per second that the waveform
goes from one peak to the next (cycles per second or Hz). The entire range of
EEG frequencies is conventionally divided into four standard frequency bands
and designated as follows: delta (0–4 Hz), theta (4–8 Hz), alpha (8–13 Hz),
beta (13 or more Hz), and gamma (40 Hz). Not all investigators use the
same frequency definitions, however, which leads to difficulties in interpreting
across studies.

The locations of the 19 electrodes follow the standardized 10-20 system.

There are two general classes of quantitative EEG (QEEG) measures. The first
class examines the type of activity at each of the 19 locations in reference to a
specific frequency. The value is usually correlated for a period of time or epoch
that can vary according to how the evaluator collects the data. Examples of such
quantitative measurements include the following:

* Magnitude: the average strength in absolute microvolts of the signal of a
band during an epoch
* Relative power: the microvolts of the particular band divided by the total
microvolts generated by all bands at a location
* Peak amplitude: the peak value in microvolts of a frequency band during
an epoch
* Peak frequency: the highest frequency obtained during an epoch within a
frequency range
* Symmetry: the peak amplitude symmetry between two locations (A and B)
in a particular bandwidth (ie, defined as (A  B)/(A + B). This measure
analyzes the amplitude relationships that do not necessarily depend
on connection activity but reflect differences in activity levels between different

* Spectral power: the square of the microvolts of a frequency during an epoch

The second major class of variables addresses the issue of the connectivity
patterns between locations. These variables are assumed to reflect the activity
that occurs in the long myelinated fibers that connect the different regions and
are known as the white matter of the brain. The variables are (1) coherence,
which is the average amplitude similarity between the waveforms of a particular
band in two locations over an epoch, and (2) phase, which is the time lag between
two locations of a particular band as defined by how soon after the beginning
of an epoch a particular waveform at location #1 is matched in amplitude at
location #2.

Relation between quantitative electroencephalographic variables and
cognition in reading disabilities and traumatic brain injury

Much of the original work on the relationship between the QEEG signal and
cognition collected EEG data under eyes-closed conditions and then correlated
those values with well-known cognitive measures, such as the IQ test. Different
investigators reported the results with terms such as level or activity. These
references can refer to magnitudes and relative power. These different measures
are empirically highly intercorrelated.

Thatcher et al [35] sought to discriminate between normal subjects and
subjects with TBI under the eyes-closed condition and obtained discriminate
values at or above 0.90 across three independent samples. The predominant
finding was decreased posterior alpha and increased posterior beta activity, frontal
connection abnormalities, and some long cortico-cortico connection deviations
in the group with TBI compared with the controls.

Additional studies generally have obtained consistent findings. Randolph
and Miller [36] found variability of the EEG to be a critical component in
discriminating patients with head injury from normals. Tabano et al [37] found
higher mean power values in the lower alpha range (8–10 Hz), less power in fast
alpha range (10.5–13.5 Hz), and lower mean alpha frequency in subjects with
TBI compared with normal controls. They also reported a reduction in fast beta
(20.5–26 Hz) activity. Trudeau et al [38] demonstrated high discriminant accuracy
of qEEG for the evaluation of combat veterans with a history of blast injury.

Summarizing this body of research, Thatcher [26] concluded that ‘‘EEG
coherence has been shown to be the most sensitive EEG measure of TBI.’’ He
also concluded that ‘‘the standard or routine EEG and conventional MRI are
essentially useless for the detection of TBI because of their low sensitivity and
low reliability in detecting mild to moderate TBI (eg, b20% accuracy in routine
visual EEG and visual MRI).’’

These studies have focused on frequency ranges below the 32-Hz range and
have not investigated EEG activity under task conditions. Collectively, the
studies have indicated elevated beta levels after the trauma and decreased alpha in
posterior locations, connection abnormalities, decreased alpha and beta amplitudes
in frontal location, increased variance, and nonspecific generalized slowing.
Some of the studies seem to have conflicting results (ie, increased posterior beta,
reduction in fast beta) possibly because of definitions of the frequency ranges
studied or differences in length of time since injury. Hughes and John [39]
concluded that ‘‘there is a broad consensus that increased focal or diffuse theta,
decreased alpha, decreased coherence and increased asymmetry are common
EEG indicators of the post concussion syndrome.’’

High-frequency electroencephalographic activity in the patient with
traumatic brain injury and learning disability

The 40-Hz rhythm (gamma band) in animals has been found to be associated

with the acquisition of learning. Basar-Eroglu et al [40] indicated that the 40-Hz
rhythm exists spontaneously and can be evoked in the human brain, and they
suggested that it may have multiple functions in sensory and cognitive
processing. Forty-Hertz activity also has been found during problem solving in
children [41] and adults [42]. Miltner et al [43] found increases in gamma band
activity and gamma coherence between areas of the brain that undergo an
associative learning procedure. Although more research is needed to clarify the
role of 40-Hz activity in brain function, these early findings suggest the possibility
that this frequency may be an important missing element in the understanding
of patients with TBI and learning disabilities.

Activation conditions and the patient with traumatic brain injury

McEvoy et al [44] demonstrated that the test-retest reliability of the qEEG
signal is greatly enhanced under task or activation conditions, because it requires
the subject to focus on specific tasks, whereas the subject’s state during the eyesclosed
condition may be expected to differ (because of vigilance, anxiety,
cognitive processing variations). Seven-day test-retest reliabilities were higher for
the activation condition (mean of 0.93) versus the eyes-closed condition (mean of
0.84). Even within a single EEG acquisition session, reliability varied more
during the resting condition (0.74–0.97) than the activation condition (0.92–0.99)
when analyzing particular frequency bands (eg, theta, alpha).

In two studies, Thornton [45,46] compared subjects with TBI (n = 32) and
normal controls (n = 52) under eyes-closed resting and activation conditions. The
activation conditions were an auditory attention task, a visual attention task, and a
listening-to-paragraphs task. In addition to measuring the traditional brain
frequencies (1–32 Hz), this study measured higher frequencies in the 32- to
64-Hz range. An analysis of the EEG data collected in the eyes-closed condition
led to correct classification of 100% of subjects as belonging to the TBI or normal
control group (for accidents that occurred within 1 year of evaluation) and 93%
(for all subjects regardless of time since accident). Separate analysis based on
each of the activation measures yielded respective percent correct hit rates of 95%
(auditory attention task: 79 of 84 subjects completing the tasks), 91% (visual
attention task: 79 of 84 subjects completing the task), and 88% (listening to
paragraphs: n = 84).

The listening-to-paragraphs task analysis required the least number of
variables to discriminate. The variables that were involved most often in
successful discrimination were high frequency (32–64 Hz) connectivity variables
that emanated from the frontal lobes, which supported Thatcher’s emphasis on
the effect on the frontal lobes in TBI cases. A separate analysis indicated that the
length of time that had elapsed since the accident did not correlate positively with
these connectivity values, which indicated that time does not result in improvement
in these values.

The coherence and phase relationships between locations can be conceptualized
in terms of a generator emanating from a particular location. This generator
can be visualized as a ‘‘flashlight’’ effect, in which the origin of the beam comes
from one location and sends the beam to all other 18 locations in a particular

A correlational analysis was conducted to determine the EEG parameters that
correlate with successful auditory recall for patients with TBI and normal controls
[47]. The TBI group had significantly lower values than the control group for the
beta 2 frequency (32–64 Hz) coherence and phase values involving frontal lobe
locations. These values were significantly negatively related to the total memory
score. This pattern was not observed when analyzing the reading task and
reading memory scores (K.E. Thornton, PhD, unpublished data).

Different QEEG variables are associated with success on the memory task in
the two groups. In a normal adult group, auditory memory performance correlates
positively with coherence alpha ‘‘flashlight’’ projections from predominantly left
hemisphere locations (eg, T3, F7) (K.E. Thornton, PhD, unpublished data) [48].
As the value of coherence alpha increases, there are increases in the memory
score. Within the TBI group, the positive correlates of successful recall include
‘‘flashlight’’ effects involving higher phase values from the right temporal
location (T4 in the beta 1 frequency range) and left frontal location (F7 in the beta
2 frequency range) [46]. It seems that patients with TBI compensate by shifting
the response pattern from the left temporal to the right temporal location and
engaging the higher frequencies to complete the task successfully.
Critical review of quantitative electroencephalographic studies of traumatic
brain injury

Several difficulties limit the degree to which firm conclusions can be drawn
from the literature in this area. (1) Variations exist among studies in the use of
specific frequency ranges and locations. (2) The eyes-closed condition does not
directly investigate brain function during specific tasks. (3) Most studies do not
include the frequency range above 32 Hz. (4) The implicit concept behind many
of these studies is that a particular set of locations is sufficient to understand how
the brain functions. This view is akin to a previous popular concept of a modular
functional model of brain activity. Lloyd’s [49] review of 36 functional neuroimaging
studies suggests that functions are distributed over multiple regions
and most brain regions are multifunctional. (5) The age groups under consideration
also differ across studies.
To address these limitations, future research should (1) use standard band
definitions across different studies and tasks, (2) study the relationship between
task performance and the qEEG variable during the task, (3) use higher frequencies
(above 32 Hz), (4) study all locations, all available variables, and under
different tasks, and (5) use separate databases for adult and children for the
activation approach.
What is electroencephalographic biofeedback?
Neurotherapy (or EEG biofeedback) is the operant conditioning of the EEG.
Electrodes are placed on the scalp of a subject, and the electrical information is
sent to a recording unit. The unit uses a software interface to present the status of
selected EEG variables to the subject in visual or auditory modality. When the
subject’s EEG signal meets the desired goal, the subject is presented with a
reward in the form of selected sounds and displays. When the subject’s EEG
signal produces a value that is not desired, a different sound or visual image is
presented to the subject to inhibit that particular signal. Because the brain is an
adaptive organ, it attempts to satisfy the demands made on it by the software and
changes its activity to meet these requests. The exact mechanism is unknown.
Treatment effects of neurotherapy with reading disability
No outcome research published to date has addressed the efficacy of
neurofeedback specifically for reading disability. Several studies of the effect
of neurofeedback on attention deficit hyperactivity disorder (ADHD), however,
have provided suggestive preliminary evidence that this intervention modality
can result in improved cognitive function in general.
A case study of a 13-year-old child with ADHD demonstrates the effectiveness
of 45 EEG biofeedback sessions [50]. The cortical sites that were
monitored were C3 (designed to increase 15–18 Hz and decrease 2–10 Hz)
and C4 (designed to increase 12–15 Hz and decrease 2–7 Hz). There was
marked improvement (tested at preintervention and at the twentieth and fortieth
sessions) in processing speed and processing speed variability, a 19-point IQ
increase (Kaufman Brief Intelligence Test), a 7.5 grade level increase in reading
scores (Kaufman test of Educational Achievement–Brief Form), and significant
behavioral improvements, as indicated by report of parents and patient.
Follow-up at 17 months demonstrated that the behavioral and QEEG changes
were maintained.
With samples of learning disabled subjects and subjects with and ADD and
ADHD (total sample size n = 155), four independent researchers have
demonstrated significant increases in IQ averaging 15 points (one SD) as a
result of EEG biofeedback [51–54]. Only one study [51] used a control group,
which did not demonstrate improvements on the IQ measures. A deficit in 40-Hz
activity has been reported in children with learning difficulties [55,56] and can
be enhanced through EEG biofeedback [57,58].
Efficacy of electroencephalographic biofeedback with traumatic brain injury
Frequency interventions
In a single case study, Byers [59] found that 31 sessions of EEG biofeedback
increased the magnitude of EEG in the 12- to 18-Hz range and suppressed EEG
magnitude in the 4- to 7-Hz range. The patient who had mild TBI improved
cognitive flexibility and executive function. Hoffman et al [60] used EEG
biofeedback techniques on 14 patients with TBI and reported that approximately
60% of the patients with mild (M)TBI showed improvement in self-reported
symptoms or cognitive performance as measured by the MicroCog assessment
battery after 40 sessions. The degree of improvement noted ranged from 23%
to 62%. The authors also noted significant normalization of the EEG in subjects
who showed clinical improvement. There were no controls in this study. A subsequent
open trial case series (n = 14) showed significant improvement after
five to ten sessions in self-report symptom checklists [61,62].
Keller [63] demonstrated with a group of patients with TBI (n = 12) that ten
sessions of EEG biofeedback (13–20 Hz, increase mean amplitudes) improved
attentional abilities (in 8 patients) and was superior to ten 30-minute sessions
using two standard software computerized attention training programs [64,65].
The EEG biofeedback subjects showed significant improvement on the
cancellation task (improvement more than 3 SD) and nonsignificant improvements
in other error measures (eg, choice reaction, sustained attention), whereas
subjects in the computer-based training improvements showed no improvements
on any measure. Significant improvements (more than 2 SD) for the EEG
biofeedback group also were noted on number of errors and crossed out stimuli
on the cancellation task, choice reaction time speed (milliseconds), and reaction
time (milliseconds) on the sustained attention task. The computerized intervention
program also showed significant improvements (more than 2 SD) on the
number of crossed out stimuli in the cancellation tasks (2 SD) and choice
reaction time (milliseconds) ( 2 SD), however.

Coherence interventions
Walker et al [66] studied 26 patients with MTBI within 3 to 70 days of injury
with an eyes-closed QEEG. EEG biofeedback treatment protocols (average of
19 sessions) that addressed the deviations from the normative database for the
abnormal coherence values were then implemented. Five sessions were directed
toward each coherence problem until the patient reported significant improvement
or until 40 sessions were completed. No controls were used. Significant and
substantial improvements (N50%) on a global improvement self-rating scale were
reported by 88% of the patients. All patients were able to return to work.
Coherence and magnitude interventions
Tinius and Tinius [67] performed 20 EEG biofeedback sessions and cognitive
retraining with a group of patients with TBI (n = 16) and ADHD (n = 13).
Progress was assessed with neuropsychological measures of attention and
problem solving and compared with a control group (negative history of
neurologic or neuropsychological problems, not matched for age or education)
that received only the cognitive retraining intervention. The QEEG studies were
conducted for all subjects. Intervention parameters were determined by reference
to the qEEG database comparison [26]; EEG biofeedback training targets
included coherence and magnitude abnormalities. Both groups were treated
with visual and auditory cognitive training exercises [68,69]. The subjects with
MTBI and ADHD in the EEG biofeedback treatment groups improved significantly
(+.5 to +1 SD) in comparison to the control group on the attention tasks
(intermediate visual and auditory attention) [70]. The MTBI group showed
significant improvement compared with controls on the Wisconsin card-sorting
problem-solving task in terms of a decrease in the number of trials and perseverative
Schoenberger et al [71] developed an alternative EEG biofeedback approach
with patients with TBI that involved conventional EEG biofeedback
and subthreshold photic stimulation. The clients wore glasses that had lightemitting
diodes embedded in the lenses. The EEG sensors were moved to
different locations on the head during the treatment. The client’s momentary
dominant or peak EEG frequency was measured and used to reset the frequency
at which the light-emitting diodes pulse, which in turn affected the EEG. The goal
of the intervention was to reduce slow-wave activity (4–8 Hz) and increase
activity in the 12- to 18-Hz range. Ochs [72] previously reported positive effects
in clinical cases (with a wait-list control group) using this approach with
patients with TBI. The Schoenberger study examined 12 subjects who had
experienced mild to moderately severe TBI and were 36 months to 21 years post
trauma. Neuropsychological measures of memory, attention, information
processing, verbal fluency, and integrated functions were administered, as was
the Beck Depression Inventory and the Multidimensional Fatigue Inventory. The
researchers used a wait-list control group (who subsequently received the
treatment) and random assignment to the treatment and control groups. The
subjects received 25 sessions, with session length varying between 5 seconds and
15 minutes, over a 5- to 8-week period. The dominant frequency that was
stimulated varied between 5 and 20 Hz. Significant improvements were reported
on the emotional (Beck Depression Inventory, Multidimensional Fatigue
Inventory) and the neuropsychological measures, and 7 subjects reported
returning to a productive work life. Additional benefits included a reduction of
medication usage for 2 of the 8 subjects taking medications, with cessation in
3 subjects. Potential problems of practice effects were addressed with alternate
measures when available. Three subjects did not respond positively to
the treatment.
In summary, qEEG biofeedback interventions have proved to be a useful
approach to remediation of cognitive difficulties in patients with TBI, whether the
approach was directed toward coherence or magnitude measures. Limitations of
these studies include a lack of specificity between the cognitive task and its
relationship to the qEEG variables, failure to obtain or indicate that the cognitive
improvements were concomitant with changes in the qEEG measures, lack of
high frequency analysis, and long-term follow-up.
An alternate electroencephalographic biofeedback approach: development
and clinical application of an activation database
Whereas the eyes-closed condition provides clinically relevant information
regarding the nature of state of the brain, it does not provide information on the
brain’s active functioning. A logical next step in the development of this field is
the use of a qEEG activation database in the rehabilitation process. Thornton
developed such a database with normal child and adult subjects (K.E. Thornton,
PhD, US patent #6309361 B1) [45,46,73,74]. The criteria for inclusion in the
database were no self-report of neurologic or psychiatric problems or history of
learning disabilities/ADD or seizure activity. The database includes 30 child
subjects between the ages of 10 and 14 and 60 adult subjects over the age of 14.
The age cut-off for the adult group was derived from Piaget’s concept of formal
operations beginning at approximately age 13.
The EEG was recorded in a resting or baseline condition with eyes closed and
eyes open and during 24 different cognitive activation tasks. These tasks focused
on auditory and visual attention, auditory memory (eg, paragraphs, word lists),
visual-verbal memory (eg, names of faces, reading) and visual information. All
memory tasks involved collection of data during the input stage and during
immediate and delayed recall periods. Additional cognitive tasks included
problem solving (Raven’s Matrices), pronunciation of nonsense words, spelling,
mathematics (internal spatial addition and multiplication tables), autobiographical
memory, and visualization (K.E. Thornton, PhD, US patent #6309361 B1).

Treatment protocols and intervention methods using the activation database
The treatment consists of subjects either listening to audiotapes or reading
while the appropriate protocols are being used. The purpose of this approach is to
train the brain under the appropriate and relevant task conditions. The initial
evaluation provides four baseline measures of auditory memory. During
treatment, the subject’s progress is tested with novel stories that contain
approximately 20 to 25 pieces of information. The subject listens to the story
at the beginning of the session and recalls the story immediately to the clinician to
obtain an immediate memory score. At the end of the session, the subject is asked
to recall the story to obtain a delayed memory score. The scores are compared
with the baseline to assess improvement in functioning. Treatment for children
with learning disability or ADHD typically involves 40 sessions, although the
program can last longer for patients with TBI.
Clinical case examples
Learning disabled case reports
Case examples previously have been reported in peer-reviewed journals
[73,74]. This report provides additional information and includes additional
subjects. A control group used in the previously reported research did not
demonstrate any significant gains as a result of practice effects or the passage of
time between first and second testing. Outcomes are reported for all variables that
were available for analysis.
Case 1 involves an 8-year-old boy who was diagnosed with ADHD (no
official diagnosis of reading disability) and underwent 25 hours (50 sessions) of
qEEG biofeedback. The focus of the treatments, based on findings from his
qEEG activation study, was on decreasing relative power of delta and theta and
increasing relative power of beta1 (13–32 Hz) under auditory memory conditions
in central and posterior locations. The data reflect the changes in the
qEEG variables during a reading task in the left posterior region (T5-P3-O1) after
the treatment and the resultant improvements (gain of percentile rank of 50%)
on the reading subtests of the Terra Nova test.
The improvement in the values during the auditory input condition generalized
to the reading condition. His auditory memory improved 205%. The SD value
represents the standard deviation in the normative reference group of the relative
power figures during a reading task.
Case 2 involves a 9-year-old girl whose parents reported a history of learning
problems. Neither academic records nor formal educational or neuropsychological
testing completed were examined to verify the presence of learning
disability, however. Her absolute levels for theta were more than 0.50 SD below
the norm, and her values for relative power of beta 2 (frequency range 32–64 Hz)
were 2 to 3 SD above the norm, which indicated that she did not fit into the
high theta/low beta pattern seen in many children with learning disability. The
coherence alpha projections during the input stage and the coherence and phase
alpha during the immediate recall period were significantly below the norm,
however, and became the main focus of the treatment. She improved approximately
1069% in auditory memory (total memory score from 1.8–19.25) and
400% in reading memory (total score from 2.5–14) during the 40 sessions. Table 3
presents her improvements toward the end of the treatment.
Two subjects have been treated at different clinics using the activation
approach, which reflects generalizability of the approach.
Case 3 represents another example of a child with significant history of
reading problems who did not demonstrate any problems in theta, delta, or beta
values but showed significant problems in connectivity issues. The parents had
spent approximately US$25,000 in alternate standard treatment programs to
improve his reading ability, which resulted in no significant gains. Fig. 4 presents
some of the coherence abnormalities found in the beta1 frequency.
Many additional deviations from database averages also were observed with
this child. The treatment was directed toward the posterior connection problem
from the occipital positions under reading and auditory memory conditions. His
auditory memory functioning increased 589% from baseline by the end of the
twenty-fifth session. He also improved on the standardized reading inventory
(SRI) from the previous year’s testing (a standardized reading inventory measure
administered by the school system) from a Lexile score of 360 before treatment to
753 after approximately 40 sessions, which is a much larger change than the
typical improvement of 75 to 100 Lexiles per year.

His mother reported her impression of increased self-confidence, greater
reading fluency, and ability to present information orally in school. There were
no grades available for comparison from the resource room to which he
was assigned.
Case 4 involved a 17-year-old subject with reading disability. After 20 sessions
he increased his comprehension score (on the Burns Roe Reading Passages)
from 45% to 90% (on alternate versions –eighth grade level) and from 20% to
70% (on tenth grade level). His performance on alternate versions of the
Cognisys Story Recall Test had increased by 3 SD. On the Wechsler Individual
Achievement Test reading comprehension subtest he attained a standard score of
99 for age and grade level.
Neurotherapy for the patient with traumatic brain injury
Case 5 involves that of a 37-year-old woman (with a PhD) who experienced a
mild TBI during an auto accident. She was particularly concerned that she
recover her auditory memory ability to return to work as a psychotherapist.
The predominant problems were with coherence
beta 2 (32–64 Hertz) that emanated from the Fz location
and projecting its beam to the rest of the head. Interventions were directed toward
increasing coherence beta 2. The subject was involved in neurofeedback on a
weekly basis for more than a year. The training was targeted at normalizing these
beta 2 coherence abnormalities. Significant improvements (3.7 SD increase) in
the EEG were seen in the areas targeted and in other connectivity variables.
Improvements in cognitive functioning at 13 months after initial testing
were as follows (Table 5): Shipley verbal IQ score improved from 101 to 123
(approximately 7 SD improvements in continuous performance test errors);
Wechsler Logical Memory improvements (35% to 84% overall ranking) for
immediate and delayed recall; raw score increase from 40.5 to 62.5, or a 54%
improvement. On the California Verbal Learning Test the patient improved 3 SD
on long delay free recall and recognition hits and 2 SD on several measures (short
delay, free and cued; long delay cued recall). She also increased total memory
score from 47 to 61, while the rest of the measures changed in a positive direction
(except for perseverations, which increased significantly—5 SD). The Wisconsin
Card sorting performance showed an increased correct score (from 61–71),
whereas the other measures remained approximately the same and within the
average range. Changes in a negative direction included an increase in errors on
the category test (69–81) and an increase in the number of trials to complete
category 1 (11–18) on the Wisconsin Card sorting test.
Case 6  involved a female patient with mild TBI with deficits in the
high frequency range that emanated from the F4 location. She entered
neurofeedback 3 years after the accident. Interventions were directed toward
the F4-Fz, F4-C3, and F4-Fp2 relationships. The subject’s neurofeedback
treatment notes showed 2 SD in improvements in the beta 2 coherence at F4-
Fz. Her auditory memory score improved 110%. Although the interventions were
not directed at the normal positive correlates of memory (eg, T3 coherence alpha
values) there were substantial improvements in this skill.
Case 7 involved a 69-year-old woman who was hit by a car at a shopping mall
and remained unconscious for 3 months. An MRI evaluation revealed a left
frontal hematoma. Her QEEG study revealed abnormalities in left frontal
connectivity (particularly FP1-F3 PB2, F3-T3 PA). Neurofeedback began
24 months after the accident. The treatment protocols were directed toward
these connection problems. After 54 sessions, the values of Fp1-F3 PB2 (phase
beta 2) increased from 36 to 70 (4.9 SD) and for F3-T3 PA (phase alpha) from
62 to 68 (1.25 SD). Her auditory memory improved from 10 to 34 (340%) pieces
of information. The case is of particular importance because it involved structural
damage to the brain in an elderly patient, factors that would intuitively be thought
to be negative treatment indicators.
Comparisons of effectiveness of interventions
Comparisons of the outcomes of neurotherapy with traditional interventions
demonstrate their relative effectiveness in treating reading disability and TBI.
Fig. 7 shows the outcomes of treatments in standard deviation units for several
programs in current use and for the different forms of EEG biofeedback: standard
EEG biofeedback (increased beta/decrease theta at central locations) and
activation qEEG-guided biofeedback. For reading disability, the current programs
show improvements that range from 0 to +0.40 SD on verbal skill measures,
+0.60 to +1 SD for ‘‘standard’’ EEG biofeedback on attention and IQ measures,
compared with the +3 to +3.3 SD changes for the hi-frequency activation
database-guided qEEG biofeedback (reading and auditory memory). For the TBI
cases, attention is improved +0.45 SD by cognitive exercises and +1.63 SD by
‘‘standard’’ EEG biofeedback. Problem solving is not improved by cognitive
exercises but is improved by +0.60 to +0.71 SD with ‘‘standard’’ and hifrequency
activation database-guided QEEG biofeedback. Finally, memory for
paragraphs is improved by cognitive exercises +0.57 SD (short-term assessment),
whereas hi-frequency activation database-guided QEEG biofeedback showed
average increases of +3 SD (up to 1 year follow-up assessment in one case).
These results offer encouragement to the continued application of QEEG-guided
interventions for cognitive improvement in these groups.

Our society has spent billions of dollars on efforts to remediate the cognitive
and behavioral dysfunction in individuals with learning disabilities and
TBI through various cognitive-based strategies. The evidence accumulated to
date indicates that few of these intervention efforts demonstrate efficacy.
When change is measured for the more traditional approaches, the change
scores typically result in improvements in the +0.00 SD to +0.50 SD range,
often after lengthy intervention periods. Research completed to date and clinical
reports show greater improvements with EEG biofeedback with these
two groups.
The application of neurofeedback with reading disability and TBI is relatively
recent. Although no published studies have assessed the efficacy of neurofeedback
for subjects specifically diagnosed with reading disability, many studies
have assessed the effectiveness of qEEG with the ADHD population, which is
known to have a high rate of comorbidity for learning disabilities. These findings
suggest the possibility that neurofeedback specifically aimed at remediating
reading disability would be effective. Clinical experience, as evidenced by the
case examples, provides strong initial support for this suggestion. In particular,
there is reason to believe that assessment and training under task conditions are
likely to be fruitful. Further research is required to confirm these initial findings.
Given the significance of the problems and the absence of proven alternatives for
remediating reading disability, efforts to complete the needed research seem
warranted. Given the absence of proven alternatives, clinical use of this intervention
also seems to be warranted with informed consent acknowledging the
absence of empirical efficacy data.
More work has been reported on the assessment of the efficacy of neurofeedback
for TBI. The results of these studies indicate that neurofeedback shows
promise in this area. There is reason to believe that assessment and training under
task conditions are likely to be fruitful. Further research replicating these findings
with larger numbers of subjects and better controls is needed before strong claims
can be made. Clinical work using neurofeedback with patients with TBI has
been consistent with the indications of efficacy found in the research. Given
the significance of the problems and the absence of proven alternatives for
remediating the cognitive and behavioral effects of TBI, efforts to complete the
needed research for clinical use seem warranted.
The authors would like to express their appreciation to Alena Appelbaum,
Dale Paterson, and Roger Riss, PhD, for their continued support in the
development and clinical application of the QEEG activation approach and their
editorial and subject contributions for this article.

[1] Learning Disability Association of America. Available at: www.ldanatl.org. Accessed January
[2] Individuals with Disabilities Education Act—Data; Data Tables for OSEP State Reported Data
IDEA Part B Child Count (2002). Available at: http://www.ideadata.org/arc_toc4.asp#partbCC.
Accessed September 3, 2004.
[3] Winter C. Learning disabilities, crime, delinquency, and special education placement. Adolescence
1997;32:451– 62.
[4] Wood R. The trillion-dollar sham in federal remedial education, NRRF Director of Statistical
Research, The National Right to Read Foundation. Available at: http://www.nrrf.org/essay_
Trillion_ 116.html. Accessed September 3, 2004.
[5] Galaburda AM, Geschwind N, Sherman GF, Rosen GD, Aboitiz F. Developmental dyslexia: four
consecutive patients with cortical anomalies. Ann Neurol 1985;18:222–33.
[6] Temple E, Deutsch GK, Poldrak RA, Miller SL, Tallal P, Merzenich MM, et al. Neural deficits in
children with dyslexia ameliorated by behavioral remediation: evidence from functional MRI.
Proc Natl Acad Sci U S A 2003;100(5):2860– 5.
[7] McCandliss BD, Noble KG. The development of reading impairment: a cognitive neuroscience
model. Ment Retard Dev Disabil Res Rev 2003;9:196 – 205.
[8] Paulesu E, Frith U, Snowling M, Gallagher A, Morton J, Frackowiak RSJ, et al. Is developmental
dyslexia a disconnection syndrome? Evidence from PET scanning. Brain 1996;119:
143– 57.
[9] Rumsey JM, Horwitz B, Donohue BC, Nace K, Maisog JM, Andreason P. Phonological and
orthographic components of word recognition: a PET-rCBF study. Brain 1997;120:739–59.
[10] Rumsey JM, Nace K, Donohue B, Wise D, Maisog JM, Andreason P. A positron emission
tomographic study of impaired word recognition and phonological processing in dyslexic men.
Arch Neurol 1997;54:562– 73.
[11] Shaywitz SE, Shaywitz BA, Pugh KR, Fulbright RK, Constable RT, Mencl WE, et al. Functional
disruption in the organization of the brain for reading in dyslexia. Proc Natl Acad Sci U S A
1998;95:2636– 41.
[12] Simos PG, Breier JI, Fletcher JM, Foorman BR, Bergman E, Fishbeck K, et al. Brain activation
profiles in dyslexic children during non-word reading: a magnetic source imaging study.
Neurosci Lett 2000;290:61–5.
[13] Simos PG, Fletcher JM, Foorman BR, Francis DJ, Castillo EM, Davis RN, et al. Brain activation
profiles during the early stages of reading acquisition. J Child Neurol 2002;17:159– 63.
[14] Shaywitz BA, Shaywitz SE, Pugh KR, Mencl WE, Fulbright RK, Skudlarski P, et al. Disruption
of posterior brain systems for reading in children with developmental dyslexia. Biol Psychiatry
2002;52:101– 10.
[15] Shaywitz SE, Shaywitz BA, Fulbright RK, Skudlarski P, Mencl WE, Constable RT, et al. Neural
systems for compensation and persistence: young adult outcome of childhood reading disability.
Biol Psychiatry 2003;54:25 – 33.
[16] Watts R, Liston C, Niogi S, Ulug˘ AM. Fiber tracking using magnetic resonance diffusion tensor
imaging and its applications to human brain development. Ment Retard Dev Disabil Res Rev
2003;9:168– 77.
[17] Klingberg T, Hedehus M, Temple E, Salz T, Gabrieli JD, Moseley ME, et al. Microstructure of
temporo-parietal white matter as a basis for reading ability: evidence from diffusion tensor
magnetic resonance imaging. Neuron 2000;25:493– 500.
[18] Horwitz B, Rumsey JM, Donohue BC. Functional connectivity of the angular gyrus in normal
reading and dyslexia. Proc Natl Acad Sci U S A 1998;95:8939–44.
[19] Lyon GR, Moats LC. Critical issues in the instruction of learning disabled. J Consult Clin
Psychol 1988;56:830– 5.
[20] Birsh J. Multisensory teaching of basic language skills. Baltimore7 Brookes; 1999.
[21] Oakland T, Black JL, Stanford G, Nussbaum NL, Balise RR. An evaluation of the dyslexia
training program: a multisensory method for promoting reading in students with reading
disabilities. J Learn Disabil 1998;31(2):140– 50.
[22] Lindamood-Bell. Available at: www.Lindamood-Bell.com. Accessed October 12, 2004.
[23] FastForWord. Available at: www.Scientificlearning.com. Accessed October 12, 2004.
[24] Simos PG, Breier JI. Age-related changes in regional brain activation during phonological
decoding and printed word recognition. Dev Neuropsychol 2001;19(2):191 –210.
[25] Brain Injury Association of America. Available at: www.biausa.org. Accessed.
[26] Thatcher RW. EEG operant conditioning (biofeedback) and traumatic brain injury. Clin Electroencephalogr
2000;31:38– 44.
[27] Thatcher RW, Biver C, McAlaster R, Camacho M, Salazar A. Biophysical linkage between MRI
and EEG amplitude in closed head injury. Neuroimage 1998;7:352–67.
[28] Benedict RHG. The effectiveness of cognitive remediation strategies for victims of traumatic
head injury: a review of the literature. Clin Psychol Rev 1989;9:605 – 26.
[29] Glisky EL, Schacter D. Remediation of organic memory disorders: current status and future
prospects. J Head Trauma Rehabil 1986;1(3):54–63.
[30] McKinlay WW. Achieving generalization of memory training. Brain Inj 1992;6(2):107 – 12.
[31] Freeman MR, Mittenberg W, Dicowden M, Bat-Ami M. Executive and compensatory memory
retraining in traumatic brain injury. Brain Inj 1992;6(1):65–70.
[32] Carney N, Chesnut RM, Maynard H, Mann NC, Patterson P, Helfand M. Effect of cognitive
rehabilitation on outcomes for persons with traumatic brain injury: a systematic review. J Head
Trauma Rehabil 1999;14(3):277 –307.
[33] Cicerone K, Dahlberg C, Kalmar K, Langenbahn D, Malec J, Bergquist F, et al. Evidence-based
cognitive rehabilitation: recommendations for clinical practice. Arch Phys Med Rehabil
[34] Salazar AM, Warden DL, Schwab K, Spector J, Braverman S, Walter J, et al. Cognitive rehabilitation
for traumatic brain injury: a randomized trial for the Defense and Veterans Head
Injury Program (DVHIP) Study Group. JAMA 2000;283(23):3075– 81.
[35] Thatcher RW, Cantor DS, McAlaster R, Geisler F, Krause P. Comprehensive predictions of
outcome in closed head-injured patients: the development of prognostic equations. Ann N Y
Acad Sci 1991;620:82–101.
[36] Randolph C, Miller MH. EEG and cognitive performance following closed head injury.
Neuropsychobiology 1988;20:43 – 50.
[37] Tabano MT, Cameroni M, Gallozzi G, Loizzoa A, Palazzinob G, Pezzinib G. EEG Spectral
analysis after minor head injury in man. Electroencephalography and Clinical Neurophysiology
[38] Trudeau DL, Anderson J, Hansen L M, Shagalov DN, Schmoller J, Nugent S, et al. Findings of
mild traumatic brain injury in combat veterans with PTSD and a history of blast concussion.
J Neuropsychiatry Clin Neurosci 1998;10(3):308– 13.
[39] Hughes JR, John ER. Conventional and quantitative electroencephalography in psychiatry.
J Neuropsychiatry Clin Neurosci 1999;11(2):190– 208.
[40] Basar-Eroglu C, Struber D, Schurmann M, Stadler M, Basar E. Gamma-band responses in the
brain: a short review of the psychophysiological correlates and functional significance. Int J
Psychophysiol 1996;24(1–2):110– 2.
[41] Sheer DE. Electrophysiological studies in learning disabilities. In: Eichenwald H, Talbot A,
editors. The learning disabled child. Austin7 University of Texas Press; 1974.
[42] DeFrance J, Sheer DE. Focused arousal, 40 Hz EEG and motor programming. In: Giannitrapani
D, Murri L, editors. The EEG of mental activities. Basel7 Karger; 1988. p. 153–69.
[43] Miltner WHR, Braun C, Arhnold M, Witte H, Taub E. Coherences of gamma-band EEG activity
as a basis for associative learning. Nature 1999;397:434– 6.
[44] McEvoy LK, Smith ME, Gevins A. Test-retest reliability of cognitive EEG. Clin Neurophysiol
2000;111:457– 63.
[45] Thornton K. Exploratory investigation into mild brain injury and discriminant analysis with high
frequency bands (32–64 Hz). Brain Inj 1999;13(7):477– 88.
[46] Thornton K. Exploratory analysis: mild head injury, discriminant analysis with high frequency
bands (32–64 Hz) under attentional activation conditions and does time heal? Journal of
Neurotherapy 1999;3(3):1– 10.
[47] Thornton K. Electrophysiology of the reasons the brain-damaged subject can’t recall what they
hear. Arch Clin Neuropsychol 2002;17:1– 17.
[48] Thornton K. Electrophysiology of auditory memory of paragraphs. Journal of Neurotherapy
[49] Lloyd D. Virtual lesions and the not-so-modular brain. Journal of the International Neurospychological
Society 2000;6:627–35.
[50] Rossiter R. Neurofeedback for AD/HD: a ratio feedback case study and tutorial. Journal of
Neurotherapy 2002;6(3):9– 37.
[51] Linden M, Habib T, Radojevic V. A controlled study of the effects of EEG biofeedback on
cognition and behavior of children with attention deficit disorder and learning disabilities.
Biofeedback Self Regul 1996;21(1):35 –49.
[52] Tansey M. Wechsler (WISC-R) changes following treatment of learning disabilities via EEG
biofeedback training in a private practice setting. Aust J Psychol 1991;43:147– 53.
[53] Othmer S, Othmer SF. EEG biofeedback training for hyperactivity, attention deficit disorder,
specific learning disability, and other disorders. Encino (CA)7 EEG Spectrum; 1992.
[54] Thompson L, Thompson M. Neurofeedback combined with training in metacognitive strategies:
effectiveness in students with ADD. Appl Psychophysiol Biofeedback 1998;23(4):243– 63.
[55] Sheer DE. Focused arousal and 40 Hz EEG. In: Knights RM, Bakker DJ, editors. The
neurophysiology of learning disorders. Baltimore7 University Park Press; 1976.
[56] Sheer DE. Focused arousal, 40 Hz EEG and dysfunction. In: Elbert J, Rockstrogh B,
Lutzenberger W, Birbaumer N, editors. Self regulation of the brain and behavior. Berlin7
Springer; 1984.
[57] Sheer DE. Biofeedback training of 40 Hz EEG, and behavior. In: Kamiya J, Barber TX, Miller
NE, Shapiro D, Stoya J, editors. Biofeedback and self-control, 1976/1977: an annual review.
Chicago7 Aldine; 1977. p. 435.
[58] Bird BL, Newton FA, Sheer DE, Ford MR. Biofeedback training of 40 Hz EEG in humans.
Biofeedback Self Regul 1978;3(1):1– 12.
[59] Byers AP. Neurofeedback therapy for a mild head injury. Journal of Neurotherapy 1995;1(1):
[60] Hoffman DA, Stockdale S, Hicks LL. Diagnosis and treatment of head injury. Journal of
Neurotherapy 1995;1(1):14– 21.
[61] Hoffman DA, Stockdale S, Van Egren L. Symptom changes in the treatment of mild traumatic
brain injury. Clin Electroencephalogr 1996;27(3):164.
[62] Hoffman DA, Stockdale S, Van Egren L. EEG neurofeedback in the treatment of mild traumatic
brain injury. Clin Electroencephalogr 1996;27(2):6.
[63] Keller I. Neurofeedback therapy of attention deficits in patients with traumatic brain injury.
Journal of Neurotherapy 2001;5(1/2):19– 33.
[64] Marker T. COGPACK: Programmpaket fqr neuropsychologicschen rehabilitation. Ladenburg,
Germany: 1996.
[65] Siegmund K. Neurosoft: ein integriertes therapliesystem. Burladingen, Germany: 1999.
[66] Walker JE, Norman CA, Weber RK. Impact of qEEG-guided coherence training for patients with
a mild closed head injury. Journal of Neurotherapy 2002;6(2):31– 45.
[67] Tinius TP, Tinius KA. Changes after EEG biofeedback and cognitive retraining in adults with
mild traumatic brain injury and attention deficit hyperactivity disorder. Journal of Neurotherapy
[68] Sandford JA, Turner A, Browne RJ. Captain’s log cognitive training system (computer program).
Richmond (VA)7 Braintrain, Inc.; 1993.
[69] Sandford JA, Turner A. Alphabet bingo (computer program). Richmond (VA)7 Braintrain,
Inc.; 1996.
[70] Sandford JA, Turner A. Intermediate visual and auditory continuous performance test. Richmond
(VA)7 Braintrain, Inc.; 1995.
[71] Schoenberger NE, Shif SC, Esty ML, Ochs L, Matheis RJ. Flexyx neurotherapy system in the
treatment of traumatic brain injury: an initial evaluation. J Head Trauma Rehabil 2001;16(3):
260– 74.
[72] Ochs L. EEG-driven stimulation and heterogeneous head injured patients: extended findings.
Presented at the Berrol Head Injury Conference. Las Vegas, 1994.
[73] Thornton K. Rehabilitation of memory functioning with EEG biofeedback. NeuroRehabilitation
2002;17(1):69– 81.
[74] Thornton K. Rehabilitation of memory functioning in brain injured subjects with EEG biofeedback.
J Head Trauma Rehabil 2000;15(6):1285–96.
[75] Orlando PC, Rivera RO. EEG: biofeedback for elementary students with identified learning
problems. Journal of Neurotherapy, in press.
[76] Kaiser D, Othmer S. Effect of neurofeedback on variables of attention in a large multi-center
trial. Journal of Neurotherapy 2000;4(1):5– 17.
[77] Cicerone K, Smith LC, Ellmo W, Mangel HR, Nelson P, Chase RF, et al. Neuropsychological
rehabilitation of mild traumatic brain injury. Brain Injury 1996;10(4):277 –86.


* Corresponding author.
E-mail address: ket@chp-neurotherapy.com (K.E. Thornton).

1056-4993/05/$ – see front matter D 2004 Elsevier Inc. All rights reserved.


To read this stud in pdf format with charts and graphs, click here.

Sierra EEG
Neurofeedback Therapy
142 East McKnight Way
Grass Valley, CA  95949
(530) 263-1413