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Can Neurofeedback Training Enhance Performance? An Evaluation of the Evidence with Implications for Future Research



There have been many claims regarding the possibilities of performance enhancement training. The aim of such training is for an individual to complete a specific function or task with fewer errors and greater efficiency, resulting in a more positive outcome. The present review examined evidence from neurofeedback training studies to enhance performance in a particular area. Previous research has documented associations between specific cortical states and optimum levels of performance in a range of tasks. This information provides a plausible rationale for the use of neurofeedback to train individuals to enhance their performance. An examination of the literature revealed that neurofeedback training has been utilised to enhance performance from three main areas; sport, cognitive and artistic performance. The review examined evidence from neurofeedback training studies within each of these three areas. Some suggestive findings have been reported with regard to the use of neurofeedback training to enhance performance. However, due to a range of methodological limitations and a general failure to elicit unambiguous changes in baseline EEG activity, a clear association between neurofeedback training and enhanced performance has yet to be established. Throughout, the review highlights a number of recommendations to aid and stimulate future research.
Applied Psychophysiology and Biofeedback, Vol. 30, No. 4, December 2005 (
DOI: 10.1007/s10484-005-8421-4
Can Neurofeedback Training Enhance Performance?
An Evaluation of the Evidence with Implications
for Future Research
David J. Vernon
There have been many claims regarding the possibilities of performance enhancement
training. The aim of such training is for an individual to complete a specific function or task
with fewer errors and greater efficiency, resulting in a more positive outcome. The present
review examined evidence from neurofeedback training studies to enhance performance
in a particular area. Previous research has documented associations between specific
cortical states and optimum levels of performance in a range of tasks. This information
provides a plausible rationale for the use of neurofeedback to train individuals to enhance
their performance. An examination of the literature revealed that neurofeedback training
has been utilised to enhance performance from three main areas; sport, cognitive and
artistic performance. The review examined evidence from neurofeedback training studies
within each of these three areas. Some suggestive findings have been reported with regard
to the use of neurofeedback training to enhance performance. However, due to a range
of methodological limitations and a general failure to elicit unambiguous changes in
baseline EEG activity, a clear association between neurofeedback training and enhanced
performance has yet to be established. Throughout, the review highlights a number of
recommendations to aid and stimulate future research.
KEY WORDS: neurofeedback; performance enhancement; sport; attention; memory; music; dance.
Recent years have seen a dramatic increase in the number of claims made concerning
the possibility of improving some aspect of either physical or cognitive performance via the
use of feedback training (see e.g., Norris & Currieri, 1999), and the availability of venues
offering such opportunities. However, an important question remains to be addressed, and
that is whether such claims are supported by scientific evidence showing that it is possible
to enhance some aspect of performance using this technique.
Traditionally, performance has been characterised as operating along a continuum,
with dysfunctional performance positioned at one extreme and optimal performance po-
sitioned at the other (see e.g., Kirk, 2001). Changes in performance may either bring
those within a distressed population, which operate at or near the dysfunctional end of the
Department of Applied Social Sciences, Canterbury Christ Church University, Augustine House, Canterbury,
Kent, United Kingdom; e-mail:
2005 Springer Science+Business Media, Inc.
348 Vernon
spectrum, up to a normative baseline, or enhance the performance of those at the normative
baseline moving them closer to the optimal region. The focus of interest for this review is
the training of a non-distressed population of healthy individuals whose initial performance
falls within a normative baseline.
The aim of the review is to examine the evidence for using electroencephalographic
(EEG) biofeedback, or neurofeedback, to enhance performance, identifying a possible ra-
tionale for such training whilst highlighting some suggestions for future research. The
review will initially define neurofeedback and outline how such a technique may be used
to alter electro-cortical activity. A comprehensive examination of the scientific literature
was conducted (this was not restricted to any time frame and included literature searches of
databases such as BIDS, PubMed and Psychlit. In addition to this, various on-line resources
were utilised, such as the websites of relevant societies and any relevant conference pro-
ceedings, as well as websites of neurofeedback providers). This revealed that this technique
has been used in an effort to influence performance within three main areas: these include
sport, cognitive performance and artistic performance, see Table I for a summary. Hence,
the review focuses on evidence of enhanced performance using this technique from each
of these three areas.
EEG biofeedback, or neurofeedback, is a sophisticated form of biofeedback based
on specific aspects of cortical activity. It requires the individual to learn to modify some
aspect of his/her cortical activity. This may include learning to change the amplitude,
frequency and/or coherence of distinct electrophysiological components of one’s own
brain. The goal of neurofeedback training is to teach the individual what specific states
of cortical arousal feel like and how to activate such states voluntarily. For example, during
neurofeedback training the EEG is recorded and the relevant components are extracted and
fed back to the individual using an online feedback loop in the form of audio, visual or
combined audio-visual information. Such a format is able to represent each of the relevant
electrophysiological components separately: for example, as a bar with the amplitude of a
frequency represented by the size of the bar. The individual’s task may then be to increase
the size of the training-frequency bar and simultaneously decrease the size of the bars
representing inhibitory-frequencies. On meeting this goal, a tone may sound and a symbol
appear to indicate a point scored, with the aim to score as many points as possible.
It has been suggested that research aimed at enhancing performance has a number
of distinct aims, these include controlling the level of arousal, attention and motivation,
optimising the level of autonomic control, and the ability to shift states at will, as well as
developing rehabilitative interventions for athletes suffering injury (Landers, 1985; Norris
& Currieri, 1999; Wilson & Gunkelman, 2001).
The underlying rationale of using neurofeedback training to enhance performance
is one based on associations. By identifying associations between particular patterns of
cortical activity and specific states or aspects of behaviour that are classified as ‘optimal,
one can attempt to train an individual to enhance performance by mirroring the pattern of
cortical activity seen during such optimal states. An implicit assumption that permeates
the neurofeedback literature and underpins current practice is that the training process will
lead to changes in the EEG, which in turn produces changes in behaviour. However, it
Table I . Summary of Studies Using Neurofeedback Training to Enhance Sport, Cognitive and Artistic Performance
Number of changes in
Reference N Location Feedback protocol Feedback sessions Behaviour EEG Outcome
Landers et al.
T3 and T4 Regulation of slow
cortical potentials
Visual As many as
needed to
show shift
27 shots at target Both groups
increase in
power for
5–30 Hz for
both LH and
LH training
improved shots
RH training
resulted in
poorer shots
Beatty et al.
1.12 O1 and P3 1. Suppress theta
(3–7 Hz)
Auditory eyes
2 × 1-h Radar detection
1. Showed less
1. Better radar
2. 7 2. Enhance theta
(3–7 Hz)
2. Showed
more theta
2. Poorer radar
2. Poorer radar
Bauer (1976) 13 O1, O2,
P3, C3,
Cz, T3
Enhance alpha
(8.5–12.5 Hz)
Auditory eyes
4 × 1-h
Free recall digit
increase in
No change in recall
Rasey et al.
4 CPz–PCz Increase beta
(16–22 Hz)
Visual and
Mean of 20
IVA Limited
Faster RTs on IVA
Decrease high theta
and low alpha
(6–10 Hz)
WAIS-R No change on IVA
Boynton (2001) 30 Pz Increasing theta
(4–8 Hz) over alpha
(8–12 Hz)
Auditory 8 Creativity and
No No change in
Table I . Continued
Number of changes in
Reference N Location Feedback protocol Feedback sessions Behaviour EEG Outcome
Egner and
22 1. C3 1. Enhance beta1
(15–18 Hz)
Visual and
10 TOVA No
2. C4 Inhibit theta (4–7 Hz)
and high beta
(22–30 Hz)
P3b ERP Reduction in
errors, increase in
and P3b
2. Enhance SMR
(12–15 Hz)
Inhibit theta (4–7 Hz)
and high beta
(22–30 Hz)
Vernon et al.
1. 10 Cz 1. Enhance theta
(4–8 Hz)
Visual and
8 Attention CPT 1. No 1. No change in
2. 10 Inhibit delta (0–4 Hz)
and alpha
(8–12 Hz)
Semantic working
2. Increase in
2. Increased recall
in semantic
working memory
2 Enhance SMR
(12–15 Hz)
Inhibit theta (4–7 Hz)
and high beta
(18–22 Hz)
Egner and
1. 9 Cz 1. Enhance SMR
(12–15 Hz) and
inhibit theta
(4–7 Hz) and high
beta (22–30 Hz)
Visual and
10 1. TOVA, Divided
attention task
oddball task
No 1. Increased d’ and
reduction in
omission errors
2. 8
2. Enhance beta1
(15–18 Hz) and
inhibit theta
(4–7 Hz) and high
beta (22–30 Hz)
2. TOVA, Divided
attention task
oddball task
2. Reduction in RT
and increased P3b
Egner and
1. 22 1. C4, C3,
Pz (com-
1. Enhance SMR
(12–15 Hz), beta1
(15–18 Hz), theta
(5–8 Hz) over alpha
(8–11 Hz)
1. Combina-
tion of
visual and
and auditory
1.10 1. Music
1. No 1. Marginal
improvement in
music ratings
associated with
theta over alpha
training only
Experiment 1
2.1.9 2.1. C4 2.1. Enhance SMR
(12–15 Hz) and
inhibit theta
(4–7 Hz) and high
beta (22–30 Hz)
2.1. Visual and
2.1.10 2.1. Music
2.1. No 2.1. No
Experiment 2
2.2. 9 2.2. C3 2.2. Enhance beta1
(15–18 Hz) and
inhibit theta
(4–7 Hz) and high
beta (22–30 Hz)
2.2. Visual and
2.2.10 2.2. Music
2.2. No 2.2. No
2.3.8 2.3. Pz 2.3. Enhance theta
(5–8 Hz) over alpha
(8–11 Hz)
2.3. Auditory
2.3. 10 2.3. Music
2.3. No 2.3. Improvement in
Raymond et al.
6 Pz Enhance theta
(4.5–7.7 Hz) over
alpha (8.5–11.5 Hz)
Auditory alone 10 Dance
No Some improvement
in dance
Note. Showing, reference, sample size, location of neurofeedback training, parameters trained, type of feedback (i.e., auditory/visual), number of sessions, behaviour
measured/examined, evidence of learning to alter the EEG and any reported changes in behaviour.
352 Vernon
should be noted that the link between these components is not well established in either
performance enhancement (see e.g., Egner, Zech, & Gruzelier, 2004) or the clinical literature
(see e.g., Vernon, Frick, & Gruzelier, 2004), especially with respect to predictable changes
and correlations between changes and outcome variables. For instance, research has shown
that healthy participants learning to enhance low beta (11.7–14.6 Hz) at Cz exhibited a post-
training decrease in low beta in the prefrontal region, and a decrease in alpha (7.8–11.7 Hz) at
left-frontal regions (Egner et al., 2004). Whilst learning to raise theta (3.9–7.8 Hz) over alpha
(7.8–11.7 Hz) at Pz was associated with a post-training reduction of beta1 (14.6–17.5 Hz)
at prefrontal scalp sites. Furthermore, additional examination of these effects only managed
to partially replicate these findings, showing that raising theta over alpha was associated
with a reduction in prefrontal beta1. This shows that learning to temporarily enhance
the amplitude of a specific frequency component during neurofeedback training does not
necessarily translate to increased activity in that component whilst at rest. The reason for
this pattern of effects is, as yet, unclear. It is possible that the difficulty in producing a clear
and consistent effect on baseline EEG levels is due to the underlying complexity of the
neural dynamics of the EEG. Nevertheless, further systematic investigation is imperative
to examine and produce empirical validation of predictable neurophysiological outcomes
as a result of neurofeedback training.
Previous research has examined the EEG of expert sportsmen and found that they
exhibit a distinct pattern of cortical activity from that seen in the amateur (see e.g., Bird,
1987; Collins, Powell, & Davies, 1990; Crews & Landers, 1993; Hatfield, Landers, & Ray,
1984; Radlo, Steinberg, Singer, Barba, & Melinkov, 2002; Salazar et al., 1990; Wilson,
Ainsworth, & Bird, 1985). Identification of the EEG state of sporting experts prior to
and during their performance provides a plausible rationale for the use of neurofeedback
to create, or mimic, such patterns in the non-expert in an attempt to enhance sporting
Research examining t he psychophysiology of sports performers has reported hemi-
spheric asymmetries in the EEG prior to the execution of a skill. For instance, Hatfield et al.
(1984) found that during the preparatory period of a skill, left-temporal EEG alpha activity
(8–12 Hz) significantly increased. A similar finding was reported by Salazar et al. (1990)
who found a significant increase in spectral power at 10 and 12 Hz in the left-temporal
region prior to the performance of a skill. This increase in EEG activity within the alpha
frequency range has been interpreted as representing a reduction of cortical activation in the
left-temporal region, reducing the covert verbalisations of the left-brain and allowing the
visual-spatial processes of the right hemisphere to become more dominant (Salazar et al.,
1990). These findings led researchers to examine whether a reduction in left-temporal
activity prior to skill execution can be augmented via neurofeedback, and whether such
training would benefit performance (Landers et al., 1991). Landers et al. (1991) examined
three groups of pre-elite archers, two of which received neurofeedback training whilst the
third acted as a non-contingent control group. The neurofeedback paradigm was modelled
after a slow potential feedback paradigm, whereby those receiving neurofeedback training
were encouraged to shift their level of cortical activity towards more negativity in either the
left-temporal region or the right-temporal region. The number of neurofeedback training
Enhance Performance Using Neurofeedback 353
sessions varied, continuing until each participant reached a pre-set criterion with regard to
EEG amplitude. Pre- and post training, all groups were assessed by completing a total of
27 shots at a target positioned 45 m distant, with the level of performance measured as the
distance between the arrow and the centre of the target. They found that those trained to
shift the level of cortical activity towards more negativity in the left-temporal region showed
a significant improvement in performance, and those trained to shift the level of cortical
activity towards more negativity in the right-temporal region showed significantly poorer
There was no change in performance of the control group. However, examination
of participants’ EEG spectra from pre- to post training failed to reveal a clear pattern
of change as a result of neurofeedback training. For instance, all groups, including non-
feedback controls, showed an increase in power between the ranges of 5–11 and 13–30 Hz
in the left hemisphere, and between 5 and 11 Hz in the right hemisphere. Nevertheless, the
group given right-temporal feedback did exhibit a greater increase in power in the 13–30 Hz
range in the right hemisphere compared with both the left-temporal feedback group and the
control group. These findings led Landers et al. (1991) to conclude that the results provided
some support for the use of neurofeedback as a method of enhancing the performance of
pre-elite archers.
Whilst the findings from the Landers et al. (1991) study are encouraging they are by no
means conclusive and a number of issues remain that need to be directly addressed by future
researchers in order to fully elucidate t he role that neurofeedback may play in enhancing
the performance of athletes. First, training participants to shift their cortical level towards
an increase in negativity within a particular hemisphere may be a less efficient method to
enhance performance than training them to alter a particular frequency component of the
EEG. For instance, previous research has shown significant increases in left-hemisphere al-
pha activity (8–12 Hz) during shot preparation of skilled marksmen (Hatfield et al., 1984),
and between the best and worst shots of elite archers (Salazar et al., 1990). An overall
increase in alpha power during the time that karate experts break wooden boards (Collins
et al., 1990) and an increase in right-hemisphere alpha associated with decreased errors
for highly skilled golfers has also been demonstrated (Crews & Landers, 1993). However,
such changes are not consistent in location, or direction, which may be accounted for in
terms of differences in sporting requirements. Nevertheless, a more systematic examination
of the effect of neurofeedback training for athletes needs to include an examination of the
enhancement and/or inhibition of each of the main frequency components associated with
peak athletic performance. Furthermore, directly training a specific frequency component
may make it easier to identify possible changes in the EEG associated with enhancing
athletic performance. Finally, Landers et al. (1991) provided only visual feedback in the
form of moving horizontal bars, where the position of the bar operated as a function of
the slow potential shift during that trial. A recent review of clinical studies utilising neu-
rofeedback for attention-deficit/hyperactivity disorder (ADHD) found that the majority
of researchers use a combination of both visual and auditory feedback (Vernon, Frick,
et al., 2004). Vernon, Frick, et al. (2004) suggest t hat providing both auditory and visual
feedback, as opposed to a single modality, may be a more efficacious way of informing
the participant of his/her psychophysiological state. Future research could directly ad-
dress this issue by comparing visual feedback alone, auditory feedback alone or combined
visual-auditory feedback to ascertain which method provides the most efficient training
354 Vernon
There is a great deal of research examining the association between particular EEG
frequency components and performance on specific cognitive tasks. It is beyond the scope
of this article to provide a comprehensive review of what is known about such associations
(for a useful review see, Klimesch, 1999) and the focus of this review is on the use of
neurofeedback to enhance performance. Nevertheless, a brief synopsis of some of the main
findings will serve to provide a rationale for the use of neurofeedback to enhance cognitive
It is known from previous research that theta activity (4–7 Hz) has an influence on
the cellular mechanisms of memory through its role in facilitating long-term potentiation
(LTP) (e.g., Pavlides, Greenstein, Grudman, & Winson, 1988), and more recent studies
have documented a link between recognition memory processes and theta activity recorded
from the scalp (Burgess & Gruzelier, 1997). Convincing evidence of the direct relationship
between theta and working memory stems from data showing that, during the encoding
phase of a recognition task, only words that were later correctly recognised elicited a
significant increase in theta activity (Klimesch, Doppelmayr, Schimke, & Ripper, 1997). In
addition, during the latter recognition phase, greater theta activity was found for correctly
recognised words but not distractors.
Research focusing on alpha (8–12 Hz) activity has shown that verbal thoughts are
associated with a decrease in alpha in the left hemisphere and visual thoughts are associated
with a decrease in alpha in the right hemisphere (Schwartz, Davidson, & Pugash, 1976).
More recently, it has been suggested that the alpha frequency range should be separated
into lower alpha (7–9.5 Hz) and upper alpha (9.5–12 Hz), based on the findings showing
that the lower alpha band is predominantly associated with attentional processes, whereas
upper alpha is primarily associated with semantic memory processes (Klimesch et al., 1997;
Klimesch, Schimke, Ladurner, & Pfurtscheller, 1990; Klimesch, Schimke, & Schwaiger,
1994). Examination of the interplay between working memory and long-term memory has
suggested that theta activity (4–8 Hz) reflects the processes of working memory, whilst
upper alpha (9.5–12 Hz) reflects retrieval from long-term memory (Klimesch et al., 1997;
Sarnthein, Petsche, Rappelsberger, Shaw, & von Stein, 1998; Sauseng et al., 2002).
In addition to the findings outlined previously, which highlight associations between
specific frequencies of the EEG and particular cognitive processes, researchers have also
attempted to document differences in patterns of cortical activity associated with good and
poor cognitive performance. For instance, individuals classified as highly creative exhibit a
lower mean alpha index (i.e., percentage of time in alpha [7–13 Hz]), recorded from the right
occipital-parietal region, than low creative individuals (Martindale & Armstrong, 1974).
Furthermore, highly creative individuals also take less time to increase their alpha activity
and are more efficient at alpha suppression than less creative individuals. In addition,
examination of memory performance has shown that the alpha frequency of good memory
performers is higher than that of bad performers (Klimesch et al., 1990; Klimesch, Schimke,
& Pfurtscheller, 1993), whilst others have reported strong positive correlations between
alpha power and intelligence (Doppelmayr, Klimesch, Stadler, Polhuber, & Heine, 2002).
The associations between specific frequency components of the EEG and different
aspects of cognitive processing outlined previously provide a plausible rationale for the use
of neurofeedback to enhance specific cognitive processes. For instance, if performance on a
specific cognitive task is associated with a particular EEG frequency, or better performance
Enhance Performance Using Neurofeedback 355
with a particular aspect of the EEG, the aim would be to train individuals to enhance that
frequency component.
In reviewing the literature on the use of neurofeedback to enhance cognitive per-
formance it became apparent that researchers had focused on four main neurofeedback
training protocols, each aimed at influencing a specific aspect of the EEG. These included
neurofeedback training to influence theta, alpha, the alpha/theta ratio and beta training. To
maintain a clear and coherent structure, the review will focus on evidence from research
focusing on each of these frequency components.
Theta Training
Research has shown that suppression of slow theta activity (as measured by the
ratio between the number of waves in the theta frequency band and the total number of
waves from 3 to 30 Hz) in the left parietal-occipital region (O
and P
) is associated
with a concomitant increase in attentional performance when completing a simulated radar
monitoring task (Beatty, Greenberg, Diebler, & O’Hanlon, 1974). Based on the notion
that a decrease in arousal may be associated with an increase in theta (3–7 Hz), Beatty
et al. (1974) assigned 19 participants to two groups. One group underwent neurofeedback
training to suppress theta, whilst the remaining group trained to enhance theta. The training
for both groups consisted of two 1-h EEG conditioning sessions. Following the training
participants from each group then completed a 120 min radar detection task whilst their
EEG was either recorded (EEG-unregulated) or whilst they concurrently attempted to
alter their theta activity using neurofeedback (EEG-regulated). Beatty et al. (1974) found
that neurofeedback made it possible for participants to selectively alter their theta ratio.
For instance, the theta suppress group showed less theta activity whilst simultaneously
performing both neurofeedback and the radar monitoring task compared to the monitoring
task alone. In contrast, the theta augment group produced more theta in the neurofeedback
and the radar monitoring task compared to the monitoring task alone. This led them to
suggest that neurofeedback is effective in inducing discriminate control of EEG activity.
Furthermore, they found that the theta-augment group performed significantly worse at
the radar detection task, whilst the theta-suppression group performed significantly better.
There was no difference in radar monitoring performance of the two groups when their
EEG was monitored but not fed back to them.
At first glance it would seem that these findings show that suppression of theta can
enhance attentional performance. However, closer inspection suggests that before such a
conclusion can be accepted a number of additional issues need to be addressed. First,
whilst changes in attentional performance were reported to accompany changes in the theta
ratio, it is not made clear that absolute levels of theta changed over time. For instance, a
decrease in the theta ratio could well result from an increase in either alpha (8–12 Hz), beta
(13–30 Hz) or a combination of the two. This can be readily addressed in future research
that reports changes in EEG spectra ratios by also reporting absolute values and showing
unambiguously what is increasing and what is decreasing. Secondly, it suggests that for
neurofeedback training to have an influence on performance one needs to complete both
neurofeedback training and the cognitive task simultaneously. This limits the applicability
of the training beyond the laboratory. Although, given that no difference was found in
radar monitoring performance between the groups when their EEG was monitored but not
356 Vernon
fed back may imply that the two 1-h neurofeedback training sessions were insufficient
to produce long-term changes in theta activity. Future research could directly address
this issue by monitoring the effect of neurofeedback training on cognitive performance
following different training intervals.
Alpha Training
Research focusing on the alpha (8.5–12.5 Hz) frequency has examined whether en-
hancing alpha activity can influence short-term memory performance (Bauer, 1976). Bauer
trained 13 participants to enhance their level of alpha activity over the left parietal-occipital
region of the cortex in four 1-h sessions. Feedback took the form of a 400-Hz tone to
indicate the presence of alpha. Following this participants were subsequently tested using
a verbal free recall task and digit span memory task whilst simultaneously attempting to
produce alpha. Despite showing significant increases in the percentage of alpha activity
participants exhibited no change in their level of recall for either task. This led Bauer (1976)
to conclude that changes in alpha had no apparent functional significance for the learning
process itself.
The fact that training to increase alpha had no discernable effect on short-term memory
performance may have been due to the limited number of neurofeedback training sessions.
However, given that participants did exhibit an i ncrease in alpha and that previous research
has shown an association between alpha and memory performance (e.g., Klimesch et al.,
1990, 1993) this interpretation is problematic. There remain a number of possibilities why
training overall alpha may have had no effect on short-term memory. The first and most
obvious is that whilst neurofeedback training may be able to influence gross EEG activity
it simply cannot influence cognitive performance. However, before such a possibility can
be accepted a more rigorous and systematic examination of the effects of neurofeedback
training on cognitive performance needs to be undertaken. Another possibility stems from
the traditional assumption t hat peak alpha frequency is the same for everyone. Such an
assumption is questionable, particularly when research has shown that peak alpha frequency
can vary to a considerable extent in normal age-matched participants (Klimesch et al.,
1990, 1993). Future research could directly address this by defining the frequency range of
alpha individually for each participant (see Klimesch et al., 1990 for a description of this
method). A further possibility stems from findings suggesting that alpha may be separated
into a number of distinct components, including alpha 1 (6–8 Hz), alpha 2 (8–10 Hz)
and upper alpha (10–12 Hz) (see e.g., Doppelmayr et al., 2002; Klimesch, Doppelmayr,
Russegger, Pachinger, & Schwaiger, 1998; Klimesch et al., 1994). Here the suggestion is
that lower alpha frequencies are associated with attentional demands, whilst the upper alpha
frequency is related to semantic memory. As such, the type of task used to assess cognitive
performance when training to enhance a particular aspect of alpha activity may need to be
more closely matched to the suggested cognitive processes associated with that particular
frequency range. It should be noted that the alternative interpretations offered here are
purely speculative; nevertheless, they do provide some direction for future research.
Alpha/Theta Training
Alpha/theta neurofeedback training represents a modification of a traditional neuro-
feedback training paradigm, whereby the individual attempts to increase his or her level
Enhance Performance Using Neurofeedback 357
of theta activity over that of alpha. Research has utilised this approach in an attempt to
enhance creativity and well-being (Boynton, 2001). For instance, Boynton (2001) had two
groups of participants complete an 8-week training regime. One of the groups received
neurofeedback training to enhance theta (4–8 Hz) over alpha (8–12 Hz) (alpha/theta train-
ing) and the other acted as a non-contingent control group. Both pre- and post training
groups completed measures of creativity, examining aspects of cognitive fluency, flexibility
and originality, as well as a measure of behavioural well-being. Boynton (2001) found no
change in the EEG of those completing the neurofeedback training and no pre- versus post
training differences in creativity or well-being between the two groups.
This suggests that alpha/theta training has no effect on creativity or well-being. How-
ever, Boynton (2001) suggests that the lack of any clear effects may have resulted from a
confound in the training program, which included several different components, making it
impossible to effectively evaluate the contribution of each component. In addition to this,
Boynton (2001) highlights the length of the training schedule and the ‘small’ sample size
as being additional limiting factors. Future research could directly address these points
by conducting a comparison of different training schedules to see if a more concentrated
training regime would in fact have a more beneficial effect on an individual’s ability to alter
their EEG, as well as its possible influence on creativity. The issue of sample size could
more easily be dealt with by conducting a-priori power analyses.
Beta Training
Research has also examined whether neurofeedback training to alter the faster beta
frequencies can influence attentional processing in healthy participants (Rasey, Lubar,
McIntyre, Zoffuto, & Abbott, 1996). Rasey et al. ( 1996) reported on four participants who
completed an average of 20 training sessions to enhance beta (16–22 Hz) and inhibit high
theta and low alpha (6–10 Hz) at the central-posterior region (e.g., CPz–PCz). Participants
completed a combination of ‘ feedback only’ sessions, during which they were provided with
both auditory and visual feedback, and reading and listening conditions. These involved the
presentation of audio feedback whilst the participants either read or listened to someone else
read. Cognitive performance was examined pre- and post training using the Intermediate
Visual and Auditory (IVA) attention test and the Wechsler Adult Intelligence Scale-Revised
(WAIS-R). The IVA is a computerised continuous performance test that provides measures
of visual and auditory attention. The WAIS-R measures global or general intelligence and
is divided into two parts: the verbal scale and the performance scale. Each of these two
parts is further divided into subtests, each of which taps a specific verbal or nonverbal skill.
Post training analysis of participants’ EEG revealed that two of the four participants
exhibited some change in their EEG in the direction of the training, which although not
conclusive was suggestive of learning. Analysis of behavioural data showed that none of the
participants exhibited any improvement in their IVA scores; however, the two participants
that exhibited changes in their EEG completed the IVA task in less time. Furthermore,
Rasey et al. (1996) classified two of the participants as ‘learners’ on the basis of changes
in their EEG and IVA scores. However, when WAIS-R performance for the two learners
was compared to that of the non-learners they found that the two learners showed poorer
performance on the vocabulary subtest that was at a level that suggested impairment (Rasey
et al., 1996).
358 Vernon
Rasey et al. (1996) suggest that the data highlights ‘improvements in attentional
performance among learners’ and that ‘some college students can learn to increase EEG
activity’ (p. 19). Such an optimistic interpretation of the data is difficult to understand
given that there was no clear evidence of a change in the EEG, no improvement either in
IVA or WAIS-R performance, and that those classified as ‘learners’ actually showed poorer
performance on the vocabulary subtest of the WAIS-R. A plausible explanation for the
weak effects found by Rasey and colleagues may be based on the lack of power of their
study, resulting from a sample size of only four participants.
More recent research has extended this work to investigate the influence of training
low beta frequencies (i.e., 12–15 and 15–18 Hz) on healthy individuals (Egner & Gruzelier,
2001). Egner and Gruzelier (2001) trained 22 participants to enhance low beta both in the
12–15 and the 15–18 Hz range, whilst simultaneously inhibiting theta (4–7 Hz) and high
beta (22–30 Hz). The 12–15 Hz training was conducted at the right central region (i.e., C4)
and the 15–18 Hz training was conducted at the left central region (i.e., C3). All participants
completed a total of 10 neurofeedback training sessions whilst pre- and post measures of
attention were examined using the Tests of Variable of Attention (TOVA) (Greenberg, 1987)
and an auditory oddball task that also elicited P3b event related potentials. They found
that comparing pre- versus post training performance on the TOVA showed a significant
reduction in commission error rate and a marginal improvement in participants’ ability
to discriminate potential targets. Furthermore, participants exhibited a positive correlation
between enhancing 12–15 Hz and changes in error rates and discrimination, and a negative
correlation between enhancing 15–18 Hz and changes in error rates and discrimination. Data
from the auditory oddball task showed no reported change in attentional behaviour, although
they did find a generalised increase in P3b amplitudes, which was positively correlated with
enhancing both 12–15 and 15–18 Hz. They conclude that this pattern of results represents
a ‘successful enhancement of attentional performance in healthy volunteers through EEG
operant conditioning techniques’ (p. 9).
The study by Egner and Gruzelier (2001) represents a reasonable attempt at examining
whether neurofeedback training can positively influence cognitive performance. However,
the fact that no control group was included limits the findings, and suggests that the
changes in attentional processing were nothing more than the result of practice effects. To
their credit, Egner and Gruzelier (2001) acknowledge this point (see p. 10) but attempt to
counter the argument by suggesting that the tests used are ‘free of any practice effects’
(p. 11). Even if this were true, which is something that could be disputed as the author is
unaware of any research directly addressing this issue, the problem still remains that no
significant changes in the EEG were found in those undergoing neurofeedback training. This
limits the conclusion that operant conditioning of participants’ EEG enhanced attentional
performance. Furthermore, changes in the amplitude of an event-related component linked
to attentional processing would be important if such changes coincided with relevant
behavioural changes. However, no behavioural data on auditory oddball performance were
reported making it difficult to identify what, if any, change in the amplitude of such a
component represents. That participants exhibited some improvement in task performance
is reasonably clear. What remains ambiguous is whether or not neurofeedback training had
anything to do with this.
Others have attempted to extend this work and suggest that neurofeedback training to
enhance low beta activity may influence semantic working memory performance (Vernon,
Egner et al., 2004; Vernon et al., 2003). Vernon et al. (2003) used neurofeedback to train
Enhance Performance Using Neurofeedback 359
participants to enhance a range of different EEG frequencies. One group trained to enhance
theta (4–8 Hz) whilst simultaneously inhibiting delta (0–4 Hz) and alpha (8–12 Hz). A
second group underwent training to enhance low beta (12–15 Hz) whilst simultaneously in-
hibiting theta (4–8 Hz) and high beta (18–22 Hz), and a final group acted as a non-contingent
control group. Those participants receiving the neurofeedback training completed eight
15-min training sessions, with all training conducted at Cz. All three groups completed
pre- and post measures of attention and semantic working memory. Attention was mea-
sured using a computerised continuous performance task (CPT) and semantic memory was
examined using a computerised conceptual span task (see, Haarmann, Davelaar, & Usher,
Contrary to expectation they found that those training to enhance theta showed a
marginal within session reduction of theta. However, participants training to enhance low
beta (12–15 Hz) showed significant within session increases in low beta amplitude and
decreases in both theta (4–8 Hz) and high beta (18–22 Hz) amplitudes. This shows that
the low beta group exhibited learning of the neurofeedback parameters. With regards to
behavioural performance there was no clear pattern of effects when comparing pre- and
post CPT performance, with the low beta group exhibiting some improvement in the two-
sequence task and all groups, including controls, exhibiting some improvement in the
three-sequence task. Nevertheless, comparing pre- and post semantic working memory
performance revealed that only the group training to enhance low beta showed a significant
improvement. They account for this improvement by suggesting that enhancing 12–15 Hz
aids the maintenance of the working memory representation utilised in semantic working
This research represents a reasonable attempt to identify whether neurofeedback can
enhance cognitive performance. However, there are a number of points that suggest that
these results should be interpreted with caution. For instance, whilst participants exhibited
changes in 12–15 Hz in the desired direction during the neurofeedback training sessions,
no changes were found across the training sessions. Despite this lack of any change in
baseline EEG levels it is suggested that the pre- versus post differences found in semantic
working memory are attributable to neurofeedback training. The authors are aware of this
point and are rightly cautious in their interpretation, suggesting that the increase in low
beta is merely associated with improved recall in the semantic working memory task as
opposed to offering it as a causal factor. Nevertheless, further doubt is cast on the use of
neurofeedback to enhance semantic memory performance from more recent research that
has attempted to replicate these findings and met with little success ( Vernon, Ahmed, &
Gruzelier, 2004).
More recently, Egner and Gruzelier (2004) examined whether enhancing low beta
(12–15 Hz) and beta1 (15–18 Hz) may have differential effects on attentional processing.
They randomly allocated participants to one of three groups, with each group focusing on
a different neurofeedback protocol. The first group underwent neurofeedback training to
enhance SMR (12–15 Hz) activity and inhibit theta (4–7 Hz) and high beta (22–30 Hz),
the second group underwent neurofeedback training to enhance low beta (15–18 Hz) and
inhibit theta (4–7 Hz) and high beta (22–30 Hz), and the third group engaged in a non-
neurofeedback training regime utilising the Alexander technique. Participants undergoing
neurofeedback training completed 10 weekly sessions, each lasting approximately 15-min,
with recording conducted at Cz. The non-neurofeedback group completed a similar num-
ber of weekly sessions focusing on the use of the Alexander technique. Pre- and post
360 Vernon
training all three groups were tested using the TOVA, a divided attention test and for the
two neurofeedback groups only, an auditory oddball task that elicited P300 event related
potentials. Examination of TOVA data showed that the SMR group exhibited a marginal
improvement in their ability to discriminate targets, whilst the beta1 group exhibited a
significant decrease in response times. Data from the divided attention task showed that the
SMR group exhibited a significant improvement in their ability to discriminate targets as
well as a reduction in omission errors and response time variability. However, all groups
exhibited a reduction in response times. Finally, analysis of performance on the auditory
oddball task showed no evidence of a change in behaviour; however, the beta1 group did
exhibit a significant increase in P3b amplitude. These findings led them to conclude that
neurofeedback training to enhance amplitude in SMR and beta1 can lead to significant
and ‘specific effects on cognitive-behavioural and electrocortical measures of attentional
processing’ (Egner & Gruzelier, 2004, p. 137), and that training to enhance SMR may
improve perceptual sensitivity, whilst beta1 training may increase cortical arousal.
This represents one of the more strictly controlled and useful studies showing possible
improvements in attentional processing as a function of specific neurofeedback training.
Nevertheless, it is not without its limitations. For instance, no pre- versus post changes
in EEG were reported for those undergoing neurofeedback training. This makes it very
difficult to attribute any changes in behaviour to possible changes in the EEG. Secondly,
data showing that the beta1 group exhibited a significant reduction in response times and a
slight but non-significant increase in commission errors when completing the TOVA may
be more parsimoniously interpreted as the result of a speed–accuracy trade-off (see Egner
& Gruzelier, 2004, Table I, p. 135). Finally, despite increases in P3b amplitude for the beta1
group, they exhibited no change in their ability to complete the task. Egner and Gruzelier
(2004) acknowledge this point and interpret the pattern as one indicating an increase in
overall cortical arousal only (see p. 138).
These results, combined with those reviewed previously, are suggestive in the sense
that individuals do seem to be able to alter some aspects of their EEG using neurofeed-
back training. However, at present, the evidence that neurofeedback training can result in
enhanced cognitive performance is ambiguous at best.
Early r esearch has shown t hat when passively listening to music, musicians produce
more alpha wave activity than non-musicians do (Wagner, 1975b). Others have shown
differences in the levels of coherence of the EEG between musically educated and non-
musical participants (Petsche, Lindner, Rappelsberger, & Gruber, 1988). One suggestion is
that such differences in alpha activity may reflect the training or education that musicians
receive (Wagner, 1975a). More recently, it has been shown that musical imagination and
composing elicit greater levels of coherence in particular EEG frequencies, and that beta
activity plays a major role in the processing of music (Petsche, Richter, von Stein, Etlinger,
& Filz, 1993).
These findings are not exhaustive and serve merely to identify that artistic performance
may be associated with specific changes in particular frequency components of the EEG,
and as such they provide a rationale for the use of neurofeedback to enhance artistic
performance. This in mind, recent research has reported on two successive experiments
aimed at using neurofeedback to enhance music performance (Egner & Gruzelier, 2003).
Enhance Performance Using Neurofeedback 361
In the first of t hese, Egner and Gruzelier (2003) report on a group of music students who
completed 10 sessions of neurofeedback training, each lasting 45 min, aimed at enhancing
three distinct aspects of the EEG. The first 15 min were spent attempting to enhance the
SMR rhythm (12–15 Hz) at C4, the second 15 min focused on enhancing beta1 (15–18 Hz)
at C3, and the final 15 min on producing high theta (5–8 Hz) to alpha (8–11 Hz) ratios at Pz.
In addition to this, a subset of this group also completed a regime of physical exercise and
mental skills training. Pre- and post training, all participants were examined on a 15 min
rendition of a piece of music of their choice. Their performance was assessed internally
by a panel of judges from the Royal College of Music and externally by expert judges
examining video recordings of each performance. They found that participants receiving
neurofeedback training exhibited a marginal improvement in musical performance. In
addition, they also found that participants that completed the alpha/theta training, a regime
of weekly physical exercises and mental skills training exhibited a correlation between
success on the alpha/theta protocol and improved musical performance.
In the second experiment, which utilised a similar design, Egner and Gruzelier (2003)
report on a separate group of participants completing ten 15-min training sessions aimed
only at enhancing theta over alpha at Pz. Post training examination of participants music
performance revealed significant improvements in overall quality, musical understanding,
stylistic accuracy and interpretive imagination. This led them to conclude that slow wave
neurofeedback training can benefit the musical performance of a non-clinical group.
Whilst the results from both experiments are suggestive there are a number of limi-
tations that restrict the interpretation that neurofeedback as an intervention can positively
influence musical performance. In Experiment 1 it is unclear whether the success in learn-
ing to alter levels of alpha and theta is confounded by the order of training. For instance, all
participants undergoing neurofeedback training initially completed 10 training sessions of
SMR training, followed by beta1 training. This was then followed by a further 10 sessions
of alpha/theta training. Thus, participants’ ability to alter their alpha/theta levels may be
nothing more than the result of these additional neurofeedback training sessions. In ad-
dition to this, it also remains unclear whether changes in the alpha/theta ratios were the
result of alpha decreasing, theta increasing, or a combination of the two. Furthermore, the
correlation reported between changes in alpha/theta and improved musical performance
is confounded by including those who also completed the physical exercise regime and
mental skills training. The implication here is that, at best, any improvement in musical
performance is the result of a combination of alpha/theta training, physical exercise and
mental skills training. Finally, in the second experiment changes in musical performance
are again documented for those completing alpha/theta neurofeedback training. However,
no changes in amplitude of theta, alpha or the theta-to-alpha ratio are reported. As such, a
clear case cannot be made for suggesting that neurofeedback training alone can positively
influence music performance.
In an attempt to gain further understanding of the potential use of neurofeedback to
enhance artistic performance, researchers have compared alpha/theta neurofeedback train-
ing to traditional biofeedback training on dance performance (Raymond, Sajid, Parkinson,
& Gruzelier, 2005). They allocated participants to one of three groups; Group 1 completed
10 sessions of neurofeedback training aimed at enhancing theta (4.5–7.7 Hz) over alpha
(8.5–11.5 Hz) at Pz. Group 2 completed 10 sessions of biofeedback training aimed at con-
trolling heart r ate variability (HRV) and the final group acted as a non-contingent control
group. Pre- and post training participants’ dance performance from all three groups was
362 Vernon
rated by two qualified judges. These ratings showed that dance performance for all three
groups improved. However, when differences in dance performance were divided by the
number of practice sessions they found that only those completing some form of feed-
back training (i.e., neurofeedback or biofeedback) showed improved dance performance.
This led them to suggest that both alpha/theta training and HRV biofeedback training can
improve dance performance.
Whilst representing an interesting possibility this study is again limited by the fact
that there were no reported changes in the EEG for those undertaking neurofeedback
training, and no changes in HRV for those completing the biofeedback training. Further-
more, the improved dance performance shown by both feedback groups may be noth-
ing more than a confound of participant–experimenter interaction. The failure to ensure
that the control group maintained an equal level of experimenter contact gives rise to
the possibility that both feedback interventions show an improvement in performance
merely as a result of taking part in a training scheme, irrespective of what that scheme
Given the reported association between specific patterns of cortical activity and par-
ticular levels of performance it seems plausible to utilise neurofeedback as a tool to train
individuals to re-create patterns of cortical activity in an attempt to enhance performance.
However, it seems the plethora of claims regarding the use of neurofeedback training to
enhance performance is matched only by the paucity of research showing a clear effect.
For instance, attempts to increase low frequency EEG oscillations in archers has been as-
sociated with improved accuracy, despite no clear pattern of changes in the EEG (Landers
et al., 1991). Suppression of theta activity has been associated with increased attentional
performance, but again there was no reported change in baseline levels of the EEG (Beatty
et al., 1974). Meanwhile, attempts to increase alpha had no discernible effect on memory
performance (Bauer, 1976), and alpha/theta training had no effect on creativity (Boynton,
2001). Research examining the effects of low beta neurofeedback training on cognitive
performance has met with some intriguing results (Egner & Gruzelier, 2001, 2004; Rasey
et al., 1996; Vernon, Ahmed, et al., 2004; Vernon, Egner, et al., 2004; Vernon et al., 2003).
Nevertheless, a range of methodological flaws and a failure to document clear changes in
the EEG following neurofeedback training limit such findings. A similar picture emerges
for research utilising neurofeedback to enhance artistic performance (Egner & Gruzelier,
2003; Raymond et al., 2005). As such, whilst the findings outlined previously are sugges-
tive, a clear connection between neurofeedback training and enhanced performance has yet
to be established.
It should be stressed t hat it is not the aim of this review to suggest that neurofeedback
training cannot enhance performance, merely that the evidence to date is equivocal. With
this in mind, an additional aim of the present review was to identify possible shortcomings
throughout and highlight a number of recommendations that future researchers may wish to
adopt. These recommendations included: measuring pre- and post EEG baselines to monitor
changes resulting from neurofeedback training. Where possible, utilising a moving baseline
measurement of the EEG to control for possible changes in arousal. To always include a
non-contingent control group, ideally one that receives everything the experimental group
Enhance Performance Using Neurofeedback 363
does, including equal contact time and ideally pre- and post QEEG measures. To obtain
clear pre- and post training measures of behaviour, and to correlate changes in EEG to
changes in behaviour.
As mentioned previously, some of the findings from the neurofeedback training studies
are intriguing and suggestive. As such, it remains the domain of f uture researchers to
elucidate precisely what effect neurofeedback training may have on performance.
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... Among these approaches, EEG neurofeedback training (NFT) is of particular relevance because of its direct impact on brain function (Cooke et al., 2018;Hung & Cheng, 2018). The basic principle of EEG NFT is to change the EEG trace, which in turn results in a change in behavior (Vernon, 2005). EEG NFT encourages individuals to learn self-regulation at the level of brain activity (Cooke et al., 2018), which has been associated with the optimal psychological state for sports performance (Vernon, 2005). ...
... The basic principle of EEG NFT is to change the EEG trace, which in turn results in a change in behavior (Vernon, 2005). EEG NFT encourages individuals to learn self-regulation at the level of brain activity (Cooke et al., 2018), which has been associated with the optimal psychological state for sports performance (Vernon, 2005). Despite efforts to apply NFT to elevate performance in precision sports (Arns et al., 2008;Berka et al., 2010;Cheng et al., 2015;Kao et al., 2014;Landers et al., 1991;Ring et al., 2015;Rostami et al., 2012;Sherlin et al., 2015), a recent meta-analysis has shown that existing evidence supporting the effectiveness of EEG NFT protocols in changing the EEG and improving sports performance is inconsistent (Xiang et al., 2018). ...
... Fourth, some unexpected changes in other EEG frequency bands have been observed (Landers et al., 1991). However, these potential problems may stem from methodological limitations rather than from NFT efficacy (Vernon, 2005;Xiang et al., 2018). ...
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A recent meta-analysis has shown inconclusive results on the effectiveness of traditional electroencephalography (EEG) neurofeedback training (NFT) protocols in changing EEG activity and improving sports performance. To enhance the effectiveness of EEG NFT protocols, we explored a new approach to EEG NFT, namely the function-specific instruction (FSI) approach. The basic tenet underpinning effective verbal instruction is to induce mental states as the verbal instructions consider the meaning of the brainwave function in the target region and the EEG power magnitude. This study aimed to test whether a single session of FSI is efficacious in improving frontal midline theta (FMT) activity and putting performance. Method: Thirty-six skilled golfers with a handicap of 14.05 ± 9.43 were recruited. A consecutive sampling method was used to form three groups: an FSI group (n = 12), a traditional instruction (TI) group (n = 12), and a sham control (SC) group (n = 12). In the pre- and post-tests, each participant performed 40 putts from a distance of 3 m, and the number of holed putts was recorded. The participants were asked to perform 50 trials in a single session of NFT. Putting performance improved significantly from before to after NFT in the FSI group. Moreover, the FSI group demonstrated a significant decrease in FMT power, whereas the SC group demonstrated a significant increase in FMT power from before to after NFT. These findings suggest that the FSI approach is more effective in enhancing sustained attention and putting performance in skilled golfers than TI.
... Neurofeedback training (NFT) is a specific form of biofeedback training in which real-time parameters of neural activity are measured and presented to the participants through visual, auditory or combined feedback, that aims to help participants learn how to consciously control certain aspects of their brain activity (Vernon, 2005). Learning control specific neural substrates has been shown to be related to the changes in specific behaviors (Gruzelier, 2014). ...
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Attention plays an important role in children’s development and learning, and neurofeedback training (NFT) has been proposed as a promising method to improve attention, mainly in population with attention problems such as attention deficit hyperactivity disorder. However, whether this approach has a positive effect on attention in normal developing children has been rarely investigated. This pilot study conducted ten sessions of alpha/theta ratio (ATR) NFT on eight primary students in school environment, with two to three sessions per week. The results showed inter-individual difference in NFT learning efficacy that was assessed by the slope of ATR over training sessions. In addition, the attention performance was significantly improved after NFT. Importantly, the improvement of attention performance was positively correlated with the NFT learning efficacy. It thus highlighted the need for optimizing ATR NFT protocol for the benefits on attention at the individual level. Future work can employ a double-blind placebo-controlled design with larger sample size to validate the benefits of ATR NFT for attention in normal developing children.
... Neurofeedback is an operant conditioning technique that could help subjects self-regulate targeted brain activity patterns (Wood et al., 2014;Sitaram et al., 2017). Today, neurofeedback training (NFT) has been widely investigated for its possible applications in cognitive enhancement and psychiatric amelioration (Vernon, 2005;Gruzelier, 2014;Micoulaud-Franchi et al., 2015). Numerous studies have demonstrated that alpha NFT can significantly and independently improve resting-state alpha band power (Wan et al., 2014;Ossadtchi et al., 2017). ...
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Significant variation in performance in motor imagery (MI) tasks impedes their wide adoption for brain-computer interface (BCI) applications. Previous researchers have found that resting-state alpha-band power is positively correlated with MI-BCI performance. In this study, we designed a neurofeedback training (NFT) protocol based on the up-regulation of the alpha band relative power (RP) to investigate its effect on MI-BCI performance. The principal finding of this study is that alpha NFT could successfully help subjects increase alpha-rhythm power and improve their MI-BCI performance. An individual difference was also found in this study in that subjects who increased alpha power more had a better performance improvement. Additionally, the functional connectivity (FC) of the frontal-parietal (FP) network was found to be enhanced after alpha NFT. However, the enhancement failed to reach a significant level after multiple comparisons correction. These findings contribute to a better understanding of the neurophysiological mechanism of cognitive control through alpha regulation.
... (For a comprehensive review of the relationship between cortical frequency and sport, see Cheron et al., 2016.) During NFT, relevant components of the athlete's EEG are extracted and fed back in the form of audio and/or visual cues that indicate when they have met the predetermined threshold (Vernon, 2005). This feedback loop (generally considered operant conditioning) allows athletes to see their brainwaves visually, and based on reward contingent feedback, gives them the ability to progressively alter their brainwaves (Hammond, 2011;Schwartz & Andrasik, 2017). ...
In nearly all studies within the domain of neurofeedback, a threshold has been defined for each training feature in a way that subjects' status can be evaluated during training according to the given value. In this study, a hard boundary-based neurofeedback training (HBNFT) method based on the determination of decision boundary using support vector machine (SVM) classifier was proposed in which subjects' status were clarified considering a decision boundary and they could also be encouraged once entering a target area. In this method, a scoring index (SI) was similarly defined whose value was determined in accordance with subject performance during training. The results revealed that employing a classifier and determining a decision boundary instead of using a threshold could prove more successful in accurately guiding them towards a target area and also meet no needs to choose a basis for determining a threshold. Moreover, it was likely that the proposed method could be more efficient in controlling features and preventing extreme changes compared to those using variable thresholds.
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فعالیت های بدنی و شناختی اثرهای مثبتی بر عملکردهای شناختی دارند، اما به ندرت در ترکیب با يکديگر استفاده شده اند؛ ازاين رو، مطالعۀ حاضر با هدف بررسی اثر فعالیت بدنی با سطوح متفاوت بار شناختی بر امواج مغزی قشر سینگولیت انجام شد. در اين پژوهش نیمه تجربی، 30 دانشجوی دختر کم تجرک با متوسط سنی 1/92 ± 22/63 سال به روش نمونه گیری دردسترس انتخاب شدند و براساس جايگزينی تصادفی در سه گروه 10 نفره (فعالیت بدنی بدون بار شناختی، فعالیت بدنی با بار شناختی و کنترل) قرار گرفتند. نمونه های دو گروه تجربی به مدت 16 جلسه در برنامۀ تمرينی مخصوص به گروه خود قرار گرفتند، اما گروه کنترل به فعالیت روزانۀ خود پرداختند. قبل و بعد از برنامۀ تمرينی، امواج مغزی با استفاده از دستگاه EEG در حالت استراحت و با چشمان باز ثبت شد و داده های نواحی Fz، Cz و Pz در امواج مغزی دلتا، تتا، آلفا و بتا با استفاده از روش آماری تحلیل کوواريانس چندمتغیره و آزمون تعقیبی بونفرونی در سطح معناداری 0/05 تحلیل شد. نتايج نشان داد که موج مغزی دلتا در ناحیە Fz کاهش بافت و موج مغزی آلفا در ناحیه Pz از نظر آماری افزايش معناداری در شرکت کنندگان هر دو گروه آزمايش داشت، اما تفاوت معناداری بین اثرگذاری فعالیت بدنی با و بدون بار شناختی بر امواج مغزی قشر سینگولیت مشاهده نشد. براساس يافته های اين مطالعه، بهره گیری از هر نوع فعالیت بدنی )با و بدون بار شناختی(، احتماالً بتواند شرايط تحريک نورون ها را در سطح قشر سینگولیت مغز فراهم آورد و باعث ايجاد سازگاری هايی در دستگاه عصبی شود
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Autism is a pervasive neurodevelopmental disorder of multifactorial causation and phenotypical variation. The nature of the disorder together with the difficulty in planning and implementing highly effective treatment models, have directed the scientific research towards discovering and implementing new intervention or/and therapeutic models, of different type and philosophy. Brain – computer interface systems as intervention tools in autism comprise an approach consistent with the demands of the new era. The dissertation aims at examining applied, non-invasive research protocols of this kind, placing emphasis on the way they were implemented and their effectiveness. The review was conducted on published research in the last decade, concerning ages 4-21.
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ADHD is a neurodevelopmental disorder that affects children. ADHD can often persist in adulthood too. Children diagnosed with ADHD have significantly increased across the globe and range between 3-10% of the population. The cardinal features of ADHD are inattention, hyperactivity, and impulsivity. Clinically significant impairment affects bio-psychosocial functioning. Theoretical understanding reveals the central role of genetics, environmental factors, and cognition in ADHD symptoms. The gold standard for ADHD diagnosis relies on clinical history, mental status examination, and diagnostic tools. Pharmacological intervention is the first-line evidence-based treatment for ADHD. However, studies also report that children don't respond to or can't tolerate medications and suffered from adverse side effects. There are also evidence-based treatments such as neurofeedback training that uses technology to regulate brain activity through modifying brain waves. Hence, developing devices for assessment and intervention using technology that targets the cognitive deficits are the need of the hour.
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This study aimed to determine the effect of bio-neural feedback exercises on female rugby players' performance. Methods: The design was quasi-experimental with experimental and control groups. Twenty-four female rugby players aged 16 to 25 in Alborz province were randomly divided into two equal groups of 12. Bio-neural feedback or neurofeedback exercises were performed for 15 sessions, three times a week, including the alpha protocol at point Pz and increasing the sensory-motor wave at point C3, each for 20 minutes. Data collected in pre-test and post-test were used to measure rugby performance, including pass accuracy and shot accuracy. The Mann-Whitney U test was used to analyze the data. Results: The results showed that the left and right passes' accuracy increased significantly after bio-neural feedback exercises. However, no significant improvement in shooting accuracy was observed. Conclusions: Therefore, bio-neural feedback training can be used as an effective way to improve athletes' optimal performance in sports such as rugby that require accurate passing.
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This study was performed to test the usefulness of the EEG as a research instrument for music psychology in individuals. Measuring the degree of functional interrelatedness of brain areas by coherence estimates has turned out to be more efficient than amplitude mapping. Therefore, the method, based on the analysis of EEG periods of at least 1 min, has been expanded to estimate all possible coherence values between the 19 electrodes (i.e., 171 values) and to observe any significant changes in those values caused by different musical tasks. This report concerns observations in a total of 49 healthy subjects (29 male and 20 female). The main goal of this study was to determine the degree of engagement of either hemisphere in the processing of music. Two items were shown to indicate hemispheric involvement: (1) the topographic distribution of "focal points of coherence" (brain areas participating in coherence changes with respect to a great number of other brain areas) and (2) the number of intrahemispheric coherence increases. In most cases, both items seem to focus on the same hemisphere. Taking these as parameters for hemispheric engagement, the following principal observations were made: the beta bands (and particularly their uppermost ranges) seem to play a major role in the processing of music; the hemispheric engagement, however, need not be the same for each frequency band. No hemisphere seems to be preferred. When listening to music is shifted between different styles, laterality may change. When the same tasks are repeated at several weeks' intervals, a fairly large degree of consistency is found. Imagining music and composing clearly differs from listening by activating many more coherence increases in the beta band and by an increasing percentage of hemispheric interaction. This kind of analysis may also provide some clues as to how a piece of music is processed by an individual. The coherence changes observed may represent events taking place in a system of differential attention that selects and orders the sensory inputs before the musical material is further processed at higher order hierarchical levels.
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Attention deficit/hyperactivity disorder (ADHD) represents one of the most common psychiatric disorders in childhood, resulting in serious impairment across a variety of domains. Research showing that a high proportion of children with ADHD exhibit a dysfunctional electroencephalogram (EEG), relative to aged matched peers, provides a rationale for the use of neurofeedback as an intervention. The aim of neurofeedback training is to redress any EEG abnormality, resulting in a concomitant improvement in the behaviour and/or cognitive performance of these children. This review focused on studies using neurofeedback to treat children with ADHD, with particular emphasis on the methodological aspects of neurofeedback training. Specifically, the review examined the modality of feedback provided, the different training parameters and their underlying rationale, and the particular montages used. In addition, the review also focused on the duration, frequency and total number of training sessions required to obtain a positive effect in terms of a change in the individual's EEG, behaviour and/or cognitive performance. Finally, the long-term effects of neurofeedback and the potential negative side effects were reviewed. Throughout, the review provides a number of directions for future research.
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College students received EEG biofeedback to enhance beta (16-22 hertz) activity while simultaneously inhibiting high theta and low alpha (6-10 hertz) activity in order to evaluate improvements in attentional measures. Following short-term treatment (mean = 20 sessions), subjects were evaluated as either learners or nonlearners based upon standard pre- versus posttreatment neurofeedback measures. Attention quotients taken from pre- and posttreatment measurements using the Intermediate Visual and Auditory Continuous Performance Test identified significant improvements in attentional measures in learners, while nonlearners showed no significant improvements. Results suggest that some "normal" young adults can learn to increase EEG activity associated with improved attention.
Electroencephalographic recordings were taken from eight high-level male karate performers as they performed an easy and a hard karate training task, namely the breaking of one and three inch-thick boards of wood. Examination of activity in the alpha waveband provided only partial support for a differential pattern of engagement between the left and right hemispheres. There was stronger evidence for an overall increase in alpha waveband activity immediately prior to performance that was most noticeable during the hard task. Some alternative explanations for these observations were eliminated by the design and electrode montage used. The results and implications were discussed in light of other sport related studies and work in the field of hemispheric specialization.
One of the major problems of interest to sport psychologists has been the study of stress responses of athletes to competition. Over the years, many theories and hypotheses have been advanced to explain this relationship. For example, the popular inverted-U hypothesis between arousal and performance predicts that performance will be best when arousal for a given individual on a particular task is at a moderate level. Unfortunately, this and other theories/hypotheses have been tested primarily by examining performance outcome measures (such as an average performance score or win/loss record). Few research studies have been designed to measure physiological arousal reactions continually while subjects are performing. If an attempt has been made to measure arousal at all, it usually consisted of a discrete physiological measure (i.e., a stethoscope measure of heart rate or Palmar Sweat Index) or a global paper—pencil measure of arousal (e.g., STAI or SCAT). Without some measure of an intervening process to describe the physiological and behavioral manifestations of arousal that ultimately affects performance, limited understanding would be gained upon which to base biofeedback or other cognitive—behavioral strategies.
Prompted by a new memory theory, the 'connectivity model', the hypothesis was tested whether Alpha frequency is related to memory performance. In Experiment 1, a sample of Alzheimer demented subjects and a sample of normals with comparable age were tested with the Wechsler Memory scale. EEG-signals were recorded during a resting period. The results show that EEG-frequency in the range of the Alpha band is related to memory performance. Compared to subjects with bad memory performance, mean EEG-frequency in the Alpha band is significantly higher for subjects with good memory performance. Experiment 2 replicates the findings of Experiment 1 but shows further that compared to a resting period, Alpha frequency does not increase when subjects are actually retrieving information from memory. This result suggests that Alpha frequency may be a permanent factor determining the speed with which information can be retrieved from memory.
Whether or not processing of music by the brain can be reflected in an ongoing EEG was studied in a group of 75 healthy students. The parameters considered were location, power, frequency, and coherence. By means of the Fisher permutation test, significant changes of these parameters with respect to control EEG periods were computed and represented as probability maps of brain electrical activity. In spite of the great individual differences in musical ability, education, and interest of the subjects, a number of specific group differences of the EEG parameters could be elucidated for different musical tasks. Significant differences were also seen between the groups of musically trained and untrained subjects, both during listening and even in their EEGs at rest. In addition, considerable sex differences were observed. Part of these differences, particularly those of the parameter power, are most likely caused by the different degrees of attention elicited by these tasks. The greater part of the observed changes, however, concerns coherence and thus conceivably reflects different degrees of the functional cooperation of two adjacent brain regions or the two hemispheres in several musical perception tasks.
This study investigated the effect of two music stimuli and silence on alpha rhythm production in the temporal lobes of 30 musicians and 30 nonmusicians. The experiment consisted of a no-feedback and an alpha-feedback presentation. Analysis of time spent in alpha showed significantly more alpha rhythm content in electroencephalograms of musicians than in electroencephalograms of nonmusicians. Comparisons of results evidenced no significant differences among aural conditions used nor between the no-feedback and alpha-feedback presentation. Analysis of verbal reports of attentiveness indicated that subjects ascribed a significantly lower attentiveness rating to silence conditions than to either music condition. Correlational analysis between time spent in alpha and verbal reports of attentiveness showed that an extremely low correlation existed between the two dependent measures.