Abstract and Figures

Peripheral visual performance is an important ability for everyone, and a positive inter-individual correlation is found between the peripheral visual performance and the alpha amplitude during the performance test. This study investigated the effect of alpha neurofeedback training on the peripheral visual performance. A neurofeedback group of 13 subjects finished 20 sessions of alpha enhancement feedback within 20 days. The peripheral visual performance was assessed by a new dynamic peripheral visual test on the first and last training day. The results revealed that the neurofeedback group showed significant enhancement of the peripheral visual performance as well as the relative alpha amplitude during the peripheral visual test. It was not the case in the non-neurofeedback control group, which performed the tests within the same time frame as the neurofeedback group but without any training sessions. These findings suggest that alpha neurofeedback training was effective in improving peripheral visual performance. To the best of our knowledge, this is the first study to show evidence for performance improvement in peripheral vision via alpha neurofeedback training.
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Peripheral Visual Performance Enhancement by Neurofeedback
Wenya Nan
Feng Wan
Chin Ian Lou
Mang I Vai
Agostinho Rosa
Published online: 8 October 2013
Ó Springer Science+Business Media New York 2013
Abstract Peripheral visual performance is an important
ability for everyone, and a positive inter-individual corre-
lation is found between the peripheral visual performance
and the alpha amplitude during the performance test. This
study investigated the effect of alpha neurofeedback
training on the peripheral visual performance. A neuro-
feedback group of 13 subjects finished 20 sessions of alpha
enhancement feedback within 20 days. The peripheral
visual performance was assessed by a new dynamic
peripheral visual test on the first and last training day. The
results revealed that the neurofeedback group showed
significant enhancement of the peripheral visual perfor-
mance as well as the relative alpha amplitude during the
peripheral visual test. It was not the case in the non-neu-
rofeedback control group, which performed the tests within
the same time frame as the neurofeedback group but
without any training sessions. These findings suggest that
alpha neurofeedback training was effective in improving
peripheral visual performance. To the best of our knowl-
edge, this is the first study to show evidence for
performance improvement in peripheral vision via alpha
neurofeedback training.
Keywords Alpha EEG Peripheral vision
Neurofeedback training Performance enhancement
As an important part of the human sensory system, visual
system is composed of central vision and peripheral vision.
Central vision or fovea vision is an area in the center of the
visual field where the objects are focused allowing instant
recognition of objects. Peripheral vision occurs outside the
central field of view and is responsible for the peripheral
visual information collection. When an object exceeds the
central visual field, people have to make saccadic eye
movements to find the object, to bring parts of the object into
the central vision. The orientation and span of the eye
movements are based on the visual information from
peripheral vision. Thus, peripheral vision is important for
feature identification and object recognition because it
directs eye movements in neutral search tasks (Torralba et al.
2006) and provides the visual information as important
triggers for saccades (Luo et al. 2008). It is also a backup for
people who suffer central vision loss, since it provides a rich
source of visual information outside the central gaze.
High peripheral visual ability is very important to
everyone, especially to patients with central vision loss, team
sport practitioners and drivers who have higher requirement
of peripheral visual ability than the others. For instance,
when a driver drives a car on the road, he or she needs to pay
attention to the vehicle and traffic signs in the front of the
road and may not be free to notice the events in the sur-
roundings. If drivers could notice the non-compliance with
W. Nan F. Wan (&) C. I. Lou M. I. Vai
Department of Electrical and Computer Engineering, Faculty of
Science and Technology, University of Macau, Macau, China
e-mail: fwan@umac.mo
W. Nan
e-mail: nanwenya@gmail.com
C. I. Lou
e-mail: mb05439@umac.mo
M. I. Vai
e-mail: fstmiv@umac.mo
A. Rosa
Department of Bio Engineering, Systems and Robotics Institute,
Technical University of Lisbon, Lisbon, Portugal
e-mail: acrosa@laseeb.org
Appl Psychophysiol Biofeedback (2013) 38:285–291
DOI 10.1007/s10484-013-9233-6
traffic rules in their peripheral vision, many traffic accidents
would probably be avoided. For sports performance, e.g.
soccer players performance, it has been found related to the
peripheral visual ability (Hazel 1995; Marques Jr 2010).
Due to the importance of peripheral vision, it is desir-
able to improve its performance somehow. In recent
studies, several methods have been proposed to train and
improve the ability in peripheral vision. For instance, it is
reported that perceptual learning can improve the reading
speed in peripheral vision among normally-sighted young
adults (Chung et al. 2004), normally-sighted older adults
(Yu et al. 2010) and elderly with long-standing central
vision loss (Chung 2011). In addition, repetitive transor-
bital alternating current stimulation (rtACS) shows positive
effects on visual field recovery (Schmidt et al. 2013).
In recent years, neurofeedback (NF) training has attrac-
ted much attention as it has shown positive effects on the
improvement of cognition and successful treatment of many
neurological disorders. NF refers to an operant conditioning
paradigm in which the individual learns to self-regulate the
electrical activity of his/her brain. In EEG-based NF, EEG
is recorded from one or more electrodes placed on the scalp
and the relevant components are extracted and fed back
using an online feedback loop in the form of audio, visual or
combined audio-visual information (Vernon 2005). A
number of studies have demonstrated positive effects of NF
training on treatment of psychological disorders such as
attention deficit hyperactivity disorder (Moriyama et al.
2012), substance use disorder (Sokhadze et al. 2008), epi-
lepsy (Egner and Sterman 2006), autistic spectrum disorder
(Coben et al. 2010), and schizophrenia (Nan et al. 2012a;
Bolea 2010; Surmeli et al. 2012). Besides clinical applica-
tions, benefits of NF training have also been reported in
optimizing performance such as semantic working memory
(Vernon et al. 2003), short term memory (Nan et al. 2012b),
mental rotation ability (Doppelmayr and Weber 2011;
Zoefel et al. 2011; Hanslmayr et al. 2005), cognitive pro-
cessing speed (Angelakis et al. 2007), and microsurgical
skills (Ros et al. 2009). Taken together, the evidence
demonstrates that people indeed obtain benefits through
altering the specific EEG activity by NF.
Our previous work has investigated peripheral visual
performance by a new dynamic peripheral visual test where
the appearance of stimuli objects presented in peripheral
field has drastic changes (Rodrigues et al. 2012). The result
indicated a significant positive inter-individual correlation
between the peripheral visual performance and the relative
alpha amplitude at Cz during the performance test. Follow-
ing such a finding (submitted for publication), in this study
we aimed to examine the effect of alpha NF training on the
peripheral visual performance. Our hypotheses were (a) an
increase of the relative alpha amplitude in the peripheral
visual test, and (b) it is associated with the enhancement of
peripheral visual performance. In order to ascertain that the
enhancement of peripheral visual performance is due to the
NF training instead of the task practice effect, a non-NF
control group was included for comparison.
A total of 35 volunteers (24 males and 11 females, aged
19–33 years, all right-handed) recruited without monetary
reward at the University of Macau participated in the study.
The subjects were selected according to the following
criteria: no history of psychiatric or neurological disorders,
no psychotropic medications or addiction drugs, and with
normal or corrected-to normal vision. Informed written
consent was obtained from all participants after explaining
the nature, possible consequences and privacy issues of the
study to them. They were randomly assigned to one of two
groups: NF group (6 females and 12 males) and non-NF
control group (5 females and 12 males). Five subjects in
the NF group dropped out due to the timing of university
holidays or course examinations. The final sample con-
sisted of 13 subjects in the NF group (4 females and 9
males) and 17 subjects in the control group (5 females and
12 males). The two groups did not have any significant
difference in age (t(28) = 0.579, p = 0.567). The protocol
was in accordance with the Declaration of Helsinki and
approved by the Research Ethics Committee (University of
Signal Recordings
Based on the relationship between the peripheral visual
performance and the alpha at Cz, EEG was recorded from
Cz during the performance test and NF training. To detect
eye movements in the peripheral visual test, horizontal and
vertical electrooculogram (EOG) was recorded at the outer
canthi of eyes from two electrodes, one above an eye and
one below another eye. The reference electrodes were
placed on the left and right mastoids, and the ground was
located at the forehead. The EOG signal and EEG from Cz
were amplified by an amplifier (Vertex 823 from Meditron
Electomedicina Ltda, SP, Brazil) and recorded by Somni-
um software platform (Cognitron, SP, Brazil). The sam-
pling frequency was 256 Hz. Circuit impedance was
maintained below 10 kX for all electrodes.
Each subject in the NF group completed 5 training exer-
cises within 20 days and each training exercise was
286 Appl Psychophysiol Biofeedback (2013) 38:285–291
composed of 4 sessions with an interval of 3–4 days
between two successive exercises. In each session, there
were 10 trials with 20 s each and an interval of 5 s between
two successive trials. On the first training day, the resting
baseline was recorded first and then the peripheral visual
test was performed (as pre-test), after that the training
session started. The resting baseline consisted of two 30-s
epochs with eyes open and another two 30-s epochs with
eyes closed, which enabled to determine the individual
alpha band through the amplitude band crossings (Klim-
esch 1999). On the last training day, the participants
repeated the baseline recording and then the peripheral
visual test (as post-test) after completing all NF sessions.
To ensure that the enhancement of peripheral visual per-
formance was due to the NF training rather than the
practice effect, the non-NF control group was measured
with the same design and setup, but without any training
Neurofeedback Training
Considering the hypotheses in the Introduction section,
the training parameter was the relative alpha amplitude at
Cz, which was calculated by Eq. (1) where the Band
Amplitude was the amplitude of the individual alpha band
and the EEG Amplitude was the amplitude from 0.5 to
30 Hz. Fast Fourier transformation (FFT) was used to
calculate the amplitudes every 0.125 s using the latest 512
samples, and the frequency resolution was 0.5 Hz. The
subjects received this feedback information in visual
Relative Amplitude ¼
EEG Amplitude
The feedback display contained two tridimensional
objects: a sphere and a cube. The sphere radius reflected
the relative alpha amplitude in real time and if this value
reached the threshold (Goal 1), the sphere color changed.
This sphere was constituted by several slices and the more
slices it had, the smoother it appeared. The cube height was
related to the period of time for which Goal 1 kept being
achieved continuously. If Goal 1 was being achieved
continuously for more than a predefined period of time
(2 s), Goal 2 was accomplished and the cube rose up until
Goal 1 stopped being achieved. Then the cube started
falling slowly until it reached the bottom or Goal 2 was
achieved again. Therefore, the participant’s task was to
make the cube as high as possible (Nan et al. 2012b;
Rodrigues et al. 2010).
The feedback threshold was set to 1 in the first session,
and it could be adjusted according to the session report
which showed the percentage of time for which the feed-
back parameter was above the threshold in each session. If
this percentage exceeded 60 %, the threshold would be
increased by 0.1 in the next session. In contrast, if the
percentage was below 20 %, the threshold would be
decreased by 0.1 in the next session.
Peripheral Visual Performance Measurement
An LED screen with 102.5 cm diagonal length was used to
display the stimuli objects. Five objects were presented at
the central and four corners of the screen. Each object was
framed in a square with a diagonal length of 5.8 cm. The
sequence of the stimuli objects was determined by a script
file programmed and loaded into the system before the
experiment started.
Two types of image sets were presented in this experi-
ment: targets and non-targets. For an image set to be
considered as a target, three of the five objects must be the
same in both color and shape, otherwise it was a non-target.
The test consisted of two types of test sessions and each
type was performed twice. At the beginning of each test
session, images lasted for 4 s on screen and this exposure
time progressively decreased with the test until it reached
500 ms. In the first type of test, the objects had the same
shape but different colors. There were 13 target image sets
and 43 non-target image sets. Regarding another type, the
objects had different shapes and colors, and there were 14
target image sets and 42 non-target image sets. More
details were given in Rodrigues et al. (2012).
The subjects were seated on a comfortable chair with
adjustable height to keep their eyes centered with the screen
and 84 cm away from the screen. The distance of 84 cm
ensured that the horizontal vision angle and vertical vision
angle were 60° and 33.75° respectively. To start each test
session, the start button in the upper left corner was clicked
by the subjects using eye scanning. Then the subjects looked
back to the central object. The test began at 2 s after the click.
This eye movement exercise was used as a calibration event
for the EOG eyes movement detection. During the test, the
subjects were required to track a moving object in the center
of the screen binocularly with a mouse pointer and keep their
sight on the central object all the time so that they could
capture the corner objects with their peripheral vision. They
were not allowed to use eye scanning, although most of the
subjects did use it voluntarily or involuntarily. Once the
subjects perceived that any three objects were the same, they
needed to click on the central object as fast as possible to
identify the test image set as a target.
For each test session, the following events were taken
into account: True Positive (TP) stood for clicking a target;
True Negative (TN) meant ignoring a non-target; False
Positive (FP) was accounted whenever a non-target was
clicked; False Negative (FN) stood for ignoring a target.
These events were used to calculate the accuracy shown in
Appl Psychophysiol Biofeedback (2013) 38:285–291 287
Eq. (2), where T was the total number of targets and NT
was the total number of non-targets. If the subject did not
click on any target or clicked on all (true and false targets),
the accuracy was 0 %. If the subject only clicked on correct
targets and did not miss any one, the accuracy was 100 %.
If the subject clicked on every false target and did not click
on any correct one, the accuracy was -100 %. To detect
whether the subject recognized the image sets using
peripheral vision and without any eye scanning, an EOG
detection algorithm was developed (Rodrigues et al. 2012).
By this way, we can calculate the peripheral visual accu-
racy for each test session.
accuracy ¼
100 %
Data Analyses
For each subject, the relative amplitude in all training
sessions and in the peripheral vision test was calculated
offline in each of the following bands: delta (0.5–4 Hz),
theta (4–8 Hz), standard alpha (8–12 Hz), individual alpha,
and sigma (12–16 Hz). The mean peripheral visual accu-
racy in all the four test sessions was regarded as the sub-
ject’s peripheral visual performance.
In the control group, the relative alpha amplitudes of
two subjects were found outliers and resulted in an unequal
variance across groups. To ensure reliable statistical ana-
lysis results, these two subjects were excluded from further
statistical analyses. Thus, the final sample of the control
group consisted of fifteen subjects.
For all statistical analyses, the significance level was set
as p \ 0.05. Pearson correlation was performed on the
aforementioned frequency bands separately to examine the
EEG changes over training sessions. Independent t test was
utilized to investigate the EEG difference and the visual
performance difference in the pre-test between the two
groups. For the peripheral visual performance and the
relative alpha amplitude in the performance test, we con-
ducted a repeated-measures analysis of variance (ANOVA)
with TIME (pre vs. post) as the within-subjects factor and
GROUP (NF vs. Control) as the between-subjects factor. If
significant Group by Time interaction was found, one-
tailed paired t test was followed up.
EEG Results
The correlation analysis demonstrated significant correla-
tions between session number and the relative amplitude in
the individual alpha, standard alpha, and delta frequency
bands. As shown in Fig. 1, the relative amplitude in the
individual alpha band (r = 0.834, p \ 0.001) and in the
standard alpha band (r = 0.850, p \ 0.001) increased over
sessions while the relative amplitude in the delta band
decreased over sessions (r =-0.560, p \ 0.02). In the
other bands no significant correlation was found.
There were no significant pre-test differences between
the two groups in any EEG frequency bands. Regarding the
relative amplitude of the standard alpha band, the results of
repeated-measures ANOVA showed significant main
effects of Time (F(1, 26) = 5.596, p = 0.026, partial
Fig. 1 The average EEG relative amplitude of all subjects over the
training sessions. The horizontal axis represents the session number
and the vertical axis represents the relative amplitude
288 Appl Psychophysiol Biofeedback (2013) 38:285–291
= 0.177) and Time by Group interaction (F(1,
26) = 5.066, p = 0.033, partial g
= 0.163). There was no
significant main effect of Group. Paired t test revealed that
only the NF group enhanced the relative amplitude in the
standard alpha band (t(12) = 3.674, p \ 0.01). For the
individual alpha band, repeated-measures ANOVA did not
yield significant main effects of either Group or Time, or
Time by Group interaction. Figure 2 presents the relative
amplitude of the standard alpha band in the pre- and post-
Peripheral Visual Performance
Figure 3 demonstrates the peripheral visual performance for
both groups. No significant difference was found in the pre-
test between the two groups. Repeated-measures ANOVA
revealed a main effect of Time (F(1, 26) = 18.220,
p \ 0.001, partial g
= 0.412), a main effect of Time by
Group interaction (F(1, 26) = 9.583, p = 0.005, partial
= 0.269), and a main effect of Group (F(1, 26) = 4.528,
p = 0.043, partial g
= 0.148). Paired t test confirmed that
only the NF group significantly improved peripheral visual
performance (t(12) = 4.376, p \ 0.001).
The present study investigated the effect of alpha NF
training on the peripheral visual performance. This effect
was compared to a non-NF control group which only tested
twice the peripheral visual performance. We hypothesized
that only the NF group showed an enhancement of the
peripheral visual performance as well as the alpha activity
in the performance test.
For the EEG changes over training sessions, the relative
amplitude in the individual alpha and the standard alpha
bands showed a significantly positive trend. Alpha NF
training has been used in the improvement of cognitive
performance, and the participants were able to increase the
alpha amplitude over multiple training sessions (Nan et al.
2012b; Zoefel et al. 2011; Angelakis et al. 2007). Fur-
thermore, the relative amplitude in the delta band presented
a decreased trend over sessions, which can be explained by
the increased alertness during the training.
In line with our hypotheses, the NF group significantly
enhanced the relative alpha amplitude in the performance
test, indicating that NF training can elevate alpha not only in
the training sessions but also in the performance test. This
was not however found in the non-NF control group, further
supporting the effectiveness of NF training on the alpha
enhancement in the peripheral vision test. Additionally, only
the NF group revealed a significant enhancement in the
peripheral visual performance, suggesting that the increase
in the peripheral visual performance was attributable to the
NF training rather than the test–retest effect.
In summary, the NF group revealed a significant increase
not only in the alpha amplitude during the peripheral visual
test but also in the peripheral visual performance, which
answered our hypotheses that an increase of the relative
alpha amplitude in the performance test was associated with
the enhancement of the peripheral visual performance.
These results are in agreement with our previous study,
which suggests that the peripheral visual performance is
positively related to the relative alpha amplitude in the
peripheral visual test. Moreover, our results are consistent to
some degree with the work from Schmidt et al. (2013),
which demonstrated the enhancement of alpha power
and visual field recovery in the patients with visual field
impairments after 10 days of rtACS stimulation.
Fig. 2 The average relative alpha amplitude (8–12 Hz) in the pre-test
(blue bar) and the post-test (red bar) for both groups. The error bars
present the standard deviation of relative alpha amplitude (Color
figure online)
Fig. 3 The average peripheral visual accuracy in the pre-test (blue
bar) and the post-test (red bar) for both groups. The error bars
present the standard deviation of peripheral visual accuracy (Color
figure online)
Appl Psychophysiol Biofeedback (2013) 38:285–291 289
In addition, the brain plasticity changes with NF have
been demonstrated by some functional magnetic resonance
imaging (fMRI) studies. Ros et al. (2013) using fMRI had
shown the NF effect on the brain network dynamics. In
their study, the subjects’ task was to decrease alpha
amplitude by instruction of audio feedback in a 30-min NF
session. The results showed that NF induced a statistically
significant up-regulation of functional connectivity within
the salience network and the attentional task performance
had positive change, indicating that adult cortex was suf-
ficiently plastic that half-hour of NF was capable of
intrinsically reconfiguring the brain’s functional activity. In
our case, about 67-min of NF in total was sufficient to
modulate brain function and peripheral visual performance.
Finally, some evidence supports that visual cortex is
possible to be enhanced by NF. Shibata et al. (2011) has
employed a decoded fMRI NF to validate that the adult
primate early visual cortex is sufficiently plastic to cause
visual ability changes. Another study from Scharnowski
et al. (2012) reported that perceptual sensitivity was sig-
nificantly enhanced in the subjects who had previously
learned control over ongoing spontaneous activity in visual
cortex using real-time fMRI NF. Here, instead of fMRI, we
utilized EEG-based NF to enhance peripheral visual per-
formance successfully. EEG-based NF is low cost com-
pared to fMRI, thus it holds realistic clinical promise as a
treatment option (Hammond 2011).
In conclusion, this study revealed that alpha NF can
increase alpha activity not only in the NF sessions, but also
in the peripheral vision test. Furthermore, the enhancement
of alpha activity was associated with the improvement of
peripheral visual performance. Future studies will employ
double-blind design and a larger sample size to replicate
the presented findings. To the best of our knowledge, this is
the first study to show evidence for performance
improvement in peripheral vision by means of alpha NF
Acknowledgments The authors would like to thank all the subjects
for their participation and Xiaoting Qu for experiment arrangement,
the Editor-in-Chief and the anonymous reviewers for their construc-
tive and detailed comments that helped improve very much the
quality of this paper. This work is supported in part by FCT SFRH/
BSAB/1101/2010 and PEst-OE/EEI/LA0009/2013 Grants and the
Macau Science and Technology Development Fund under Grant
FDCT 036/2009/A and the University of Macau Research Committee
under Grants MYRG139(Y1-L2)-FST11-WF, MYRG079(Y1-L2)-
FST12-VMI and MYRG069(Y1-L2)-FST13-WF.
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Appl Psychophysiol Biofeedback (2013) 38:285–291 291
... The human visual system is composed of central vision, an area in the centre of the visual field that offers clear recognition of objects, and peripheral vision, which occurs around the central field of view and is responsible for the collection of peripheral visual information ( Figure 2) [45,53]. Sports scientists and neurologists have studied peripheral vision in relation to changes in neural activity using Electroencephalogram (EEG), revealing that peripheral awareness maps to the higher part of the alpha range 10-12Hz [44,45]. ...
... The human visual system is composed of central vision, an area in the centre of the visual field that offers clear recognition of objects, and peripheral vision, which occurs around the central field of view and is responsible for the collection of peripheral visual information ( Figure 2) [45,53]. Sports scientists and neurologists have studied peripheral vision in relation to changes in neural activity using Electroencephalogram (EEG), revealing that peripheral awareness maps to the higher part of the alpha range 10-12Hz [44,45]. Further work has shown that a relaxed and open mood can be conducive to widening our perceptual field to reach peripheral awareness [63]. ...
... Moreover, the challenge with eBikes worldwide is that riders often misuse the eBike's acceleration and this has resulted in accidents and fatalities [50,61]. As such, we consider this an interesting societal challenge for a human-computer integration designintervention, where the system offers engine support only when the user is in a state of peripheral awareness, which affords them greater awareness of their surroundings [44,45]. In order to use peripheral awareness directly from neurological activity we rely on indirect physiological signals as follows. ...
... In alphaband NFB research, there are several studies, which have reported progressive session-to-session increases in alpha-band responsiveness to the NFB protocol. In the alpha-amplitude upregulation protocol, progressive session-to-session increases in alpha amplitude during the NFB sessions were reported in several studies (Zoefel et al., 2011;López-Larraz et al., 2012;Nan et al., 2013;Wan et al., 2014;Bobby and Prakash, 2017;Naas et al., 2019). Progressive increases in the ability to manipulate the alpha-band dynamics relative to the NFB protocol have been also observed in alpha-downregulation protocols (Wan et al., 2016;Nan et al., 2018Nan et al., , 2020. ...
... In other words, all multi-session tACS were focused on the offline alpha activity (Schmidt et al., 2013;Clancy et al., 2018;Ahn et al., 2019;He et al., 2019). On the other hand, NFB research, which seems to have a longer history of studying the dynamics of alpha activity, involves a considerable number of multi-session studies focusing on studying the offline alpha (Kerson et al., 2009;Breteler et al., 2010;Alexeeva et al., 2012) as well as the online dynamics of alpha activity (Bobby and Prakash, 2017) or both (Plotkin, 1978;Dempster and Vernon, 2009;Escolano et al., 2011;López-Larraz et al., 2012;Nan et al., 2013Nan et al., , 2020Dekker et al., 2014;Hsueh et al., 2016;Wan et al., 2016). With regard to the NFB studies devoted to showing the alpha behavior during both online and offline periods of the NFB session, it is worth mentioning that some studies reported an increase of alpha-band responsiveness relative to NFB modulation only during online periods and with no changes during offline periods (Nan et al., 2012(Nan et al., , 2020Hsueh et al., 2016;Wan et al., 2016). ...
... The findings also agree with NFB studies, which managed to reduce anxiety by enhancing alpha amplitude (Hardt and Kamiya, 1978;Dadashi et al., 2015). Apart from a reduction in anxiety, several other promising clinical outcomes, such as an improvement in spelling in dyslectic patients (Breteler et al., 2010), clinical improvement in post-stroke patients (Mottaz et al., 2015), working memory improvement (Zoefel et al., 2011), and the improvement of visual performance, have been reported (Nan et al., 2013). ...
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Transcranial alternating current stimulation (tACS) and neurofeedback (NFB) are two different types of non-invasive neuromodulation techniques, which can modulate brain activity and improve brain functioning. In this review, we compared the current state of knowledge related to the mechanisms of tACS and NFB and their effects on electroencephalogram (EEG) activity (online period/stimulation period) and on aftereffects (offline period/post/stimulation period), including the duration of their persistence and potential behavioral benefits. Since alpha bandwidth has been broadly studied in NFB and in tACS research, the studies of NFB and tACS in modulating alpha bandwidth were selected for comparing the online and offline effects of these two neuromodulation techniques. The factors responsible for variability in the responsiveness of the modulated EEG activity by tACS and NFB were analyzed and compared too. Based on the current literature related to tACS and NFB, it can be concluded that tACS and NFB differ a lot in the mechanisms responsible for their effects on an online EEG activity but they possibly share the common universal mechanisms responsible for the induction of aftereffects in the targeted stimulated EEG band, namely Hebbian and homeostatic plasticity. Many studies of both neuromodulation techniques report the aftereffects connected to the behavioral benefits. The duration of persistence of aftereffects for NFB and tACS is comparable. In relation to the factors influencing responsiveness to tACS and NFB, significantly more types of factors were analyzed in the NFB studies compared to the tACS studies. Several common factors for both tACS and NFB have been already investigated. Based on these outcomes, we propose several new research directions regarding tACS and NFB.
... If the percentage was in the range of 40-60%, the reward threshold would remain unadjusted. The above principle for threshold setting was adopted from previous NFT work (Nan et al., 2012(Nan et al., , 2013. ...
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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.
... This result confirms the results of previous studies on the improvement of performance in football players (Wenya et al., 2013;Wenya et al., 2014) or sportsmen in general (see Gong et al., 2021). The major element in this study is that performance is increased with only one session of Alpha EEG-NFB. ...
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The effectiveness of EEG-neurofeedback (EEG-NFB) in modulating cognition has been the subject of much research for several years, particularly in relation to attentional functions in healthy subjects and those with attentional deficits. However, its effectiveness on sports performance remains poorly studied and its use is not widely practised among athletes, notably because of its accessibility and questionable effectiveness. The aim of this study is to show that this technology can be accessible, and that Alpha EEG-NFB is immediately effective. Fifteen professional soccer players took part in this study. Using a novel EEG headset that can be installed in less than one minute, and new processing software, the players performed two peripherical attentional tasks before and after, immediately and one month, a single Alpha EEG-NFB training session. The results showed a significant effect on both tasks immediately after EEG-NFB training, with a benefit of more than 30% and this performance continued after one month (20%). This study, the first to use this headset and software, shows that the improvement in sports performance can be related to cognitive performance, especially peripherical visual attentional functions. Furthermore, it demonstrates that the use of the EEG-NFB is accessible and effective for high-level athletes.
... If this percentage exceeded 60%, the threshold would be increased by 0.1 in the next session. In contrast, if the percentage was below 20%, the threshold would be decreased by 0.1 in the next session [22]. ...
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Considering that athletes constantly practice and compete in noisy environments, the aim was to investigate if performing neurofeedback training in these conditions would yield better results in performance than in silent ones. A total of forty-five student athletes aged from 18 to 35 years old and divided equally into three groups participated in the experiment (mean ± SD for age: 22.02 ± 3.05 years). The total neurofeedback session time for each subject was 300 min and were performed twice a week. The environment in which the neurofeedback sessions were conducted did not seem to have a significant impact on the training’s success in terms of alpha relative amplitude changes (0.04 ± 0.08 for silent room versus 0.07 ± 0.28 for noisy room, p = 0.740). However, the group exposed to intermittent noise appears to have favourable results in all performance assessments (p = 0.005 for working memory and p = 0.003 for reaction time). The results of the study suggested that performing neurofeedback training in an environment with intermittent noise can be interesting to athletes. Nevertheless, it is imperative to perform a replicated crossover design.
... If this percentage exceeded 60%, the threshold would be increased by 0.1 in the next session. In contrast, if the percentage was below 20%, the threshold would be decreased by 0.1 in the next session [52]. ...
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Neurofeedback training is a technique which has seen a widespread use in clinical applications, but has only given its first steps in the sport environment. Therefore, there is still little information about the effects that this technique might have on parameters, which are relevant for athletes' health and performance, such as heart rate variability, which has been linked to physiological recovery. In the sport domain, no studies have tried to understand the effects of neurofeedback training on heart rate variability, even though some studies have compared the effects of doing neurofeedback or heart rate biofeedback training on performance. The main goal of the present study was to understand if alpha-band neurofeedback training could lead to increases in heart rate variability. 30 male student-athletes, divided into two groups, (21.2 ± 2.62 year 2/week protocol and 22.6 ± 1.1 year 3/week protocol) participated in the study, of which three subjects were excluded. Both groups performed a pre-test, a trial session and 12 neurofeedback sessions, which consisted of 25 trials of 60 s of a neurofeedback task, with 5 s rest in-between trials. The total neurofeedback session time for each subject was 300 min in both groups. Throughout the experiment, electroencephalography and heart rate variability signals were recorded. Only the three sessions/week group revealed significant improvements in mean heart rate variability at the end of the 12 neurofeedback sessions (p = 0.05); however, significant interaction was not found when compared with both groups. It is possible to conclude that neurofeedback training of individual alpha band may induce changes in heart rate variability in physically active athletes.
... Neurofeedback protocols have been used to improve performance on SSVEP-based BCIs, but these protocols have targeted SSVEPs indirectly, basing feedback on endogenous a oscillations (Wan et al., 2016), or algorithm classification accuracy (Zhang et al., 2010). In the past decade, several fMRI neurofeedback studies have endeavored to train complex spatiotemporal patterns of stimulus-evoked activity, allowing researchers to directly target neural representations of visual stimuli (Shibata et al., 2011Scharnowski et al., 2012;Nan et al., 2013;Robineau et al., 2014;deBettencourt et al., 2015;Amano et al., 2016;Cortese et al., 2016;Habes et al., 2016). Applying this approach to SSVEPs allowed us to access a near real-time readout of visual processing, and provide feedback at a much higher temporal resolution than fMRI typically allows. ...
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Complex perceptual decisions, in which information must be integrated across multiple sources of evidence, are ubiquitous but are not well understood. Such decisions rely on sensory processing of each individual source of evidence, and are therefore vulnerable to bias if sensory processing resources are disproportionately allocated among visual inputs. To investigate this, we developed an implicit neurofeedback protocol embedded within a complex decision-making task to bias sensory processing in favor of one source of evidence over another. Human participants of both sexes (N = 30) were asked to report the average motion direction across two fields of oriented moving bars. Bars of different orientations flickered at different frequencies, thus inducing steady-state visual evoked potentials. Unbeknownst to participants, neurofeedback was implemented to implicitly reward attention to a specific "trained"orientation (rather than any particular motion direction). As attentional selectivity for this orientation increased, the motion coherence of both fields of bars increased, making the task easier without altering the relative reliability of the two sources of evidence. Critically, these neurofeedback trials were alternated with "test"trials in which motion coherence was not contingent on attentional selectivity, allowing us to assess the training efficacy. The protocol successfully biased sensory processing, resulting in earlier and stronger encoding of the trained evidence source. In turn, this evidence was weighted more heavily in behavioral and neural representations of the integrated average, although the two sources of evidence were always matched in reliability. These results demonstrate how biases in sensory processing can impact integrative decision-making processes.
... If this percentage exceeded 60%, the threshold's ratio would be increased by 0.1 in the next session. In contrast, if the percentage was below 20%, the threshold's ratio would be decreased by 0.1 in the next session (Nan et al. 2013). ...
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Neurofeedback training has been an increasingly used technique and is taking its first steps in sport. Being at an embryonic stage, it is difficult to find consensus regarding the applied methodology to achieve the best results. This study focused on understanding one of the major methodological issues—the training session frequency. The aim of the investigation was to understand if there are differences between performing two sessions or three sessions per week in enhancement of alpha activity and improvement of cognition; and in case there are differences, infer the best protocol. Forty-five athletes were randomly assigned to the three-session-training-per-week group, the two-session-training-per-week group and a control group. The results showed that neurofeedback training with three sessions per week was more effective in increase of alpha amplitude during neurofeedback training than two sessions per week. Furthermore, only the three-session-per-week group showed significant enhancement in N-back and oddball performance after training. The findings suggested more condensed training protocol lead to better outcomes, providing guidance on neurofeedback protocol design in order to optimize training efficacy.
... If this percentage exceeded 60 %, the threshold would be increased by 0.1 in the next session. In contrast, if the percentage was below 20 %, the threshold would be decreased by 0.1 in the next session (Nan, Wan, Lou, Vai, & Rosa, 2013). ...
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Introduction: Neurofeedback training has been an increasingly used technique in sport; however, most of the protocols used in athletes are based in the results obtained in nonathletic population. Purpose: Understand if a specific neurofeedback training protocol implemented in a nonathletic population can improve short-term memory and reaction time in athletes. Methods: A total of 45 subjects participated in the experiment (mean ± SD for age: 23.31 ± 4.20 years). For athletes, 12 neurofeedback training sessions were performed; for the nonathletes, 15 neurofeedback training were performed. Each session had 25 min of effective neurofeedback training. Results: Despite the nonathletes group’s increased standard alpha band (SAB) relative amplitude and individual alpha band (IAB) relative amplitude after 12 sessions of neurofeedback training (p < .005), only the athletes intervention group had positive results in reaction time (p < .001 in oddball test). Not only was the null hypothesis rejected by the differences of IAB and SAB relative amplitudes between and within protocols but also by the performance tests. Conclusion: Neurofeedback training increases the relative amplitude of the bands in the nonathletes group; however, only the athletes have shown to improve performances tests after 12 neurofeedback training sessions.
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Introduction Findings of recent studies have proposed that it is possible to enhance cognitive capacities of healthy individuals by means of individual upper alpha (around 10 to 13.5 Hz) neurofeedback training. Although these results are promising, most of this research was conducted based on high-priced EEG systems developed for clinical and research purposes only. This study addresses the question whether such effects can also be shown with an easy to use and comparably low priced Emotiv Epoc EEG headset available for the average consumer. In addition, critical voices were raised regarding the control group designs of studies addressing the link between neurofeedback training and cognitive performance. Based on an extensive literature review revealing considerable methodological issues in an important part of the existing research, the present study addressed the question whether individual upper alpha neurofeedback has a positive effect on alpha amplitudes (i.e. increases alpha amplitudes) and short-term memory performance focussing on a methodologically sound, single-blinded, sham controlled design. Method Participants (N = 33) took part in four test sessions over four consecutive days of either neurofeedback training or sham feedback (control group). In the experimental group, five three-minute periods of visual neurofeedback training were administered each day whereas in the control group, the same amount of sham feedback was presented. Performance on eight digit-span tests as well as participants' affective states were assessed before and after each of the daily training sessions. Results Participants in the neurofeedback training (NFT) group showed faster and greater alpha enhancement compared to the control group. Contrary to the authors' expectations, alpha enhancement was also observed in the control group. Surprisingly, exploratory analyses showed a significant correlation between the initial alpha level and the alpha improvement during the course of the study. This finding suggests that participants with high initial alpha levels profit more from alpha NFT interventions. digit-span performance increased in both groups over the course of time. However, the increase in individual upper relative alpha did not explain significant variance of digit-span improvement. In the discussion, the authors explore the appearance of the alpha enhancement in the control group and possible reasons for the absence of a connection between NFT and short-term memory.
Conference Paper
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The paper presents a case report of the therapy by neurofeedback training of a chronic female schizophrenia outpatient. The purpose of the neurofeedback training was to enhance cognition, memory and behavioral performance. We used an intensive approach. All the training sessions were performed during 4 consecutive days. The results showed that the patient learnt to increase individual alpha amplitude and simultaneously decrease beta2 (20-30 Hz) amplitude at P4 site during active state over sessions. The individual alpha increased 74.73% and beta2 decreased 13.73%. The short term memory was enhanced after training. Her mood had positive change and her speech was much clearer than before. She started to associate the meaning of her life and illness. These results support that neurofeedback is not only a feasible therapy for schizophrenia but also positive results could be obtained using a much more intensive training sessions than the one reported in the literature.
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Written to educate both professionals and the general public, this article provides an update and overview of the field of neurofeedback (EEG biofeedback). The process of assessment and neurofeedback training is explained. Then, areas in which neurofeedback is being used as a treatment are identified and a survey of research findings is presented. Potential risks, side effects, and adverse reactions are cited and guidelines provided for selecting a legitimately qualified practitioner.
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Neurofeedback training (NFT) has been demonstrated to be a useful, inexpensive, nonpharmacological tool in the treatment of attention deficit hyperactivity disorder and epilepsy in humans. Different neurofeedback training protocols have been associated with positive effects on performance in sports, creativity, memory, and simple reaction time tasks. During NFT, individuals receive visual or acoustic feedback of their brain oscillations, which are recorded by electroencephalogram (EEG). Through operant conditioning that employs this feedback, the individuals subsequently may be able to modulate the respective oscillations. The most widely used training protocols focus on either the enhancement of the sensorimotor rhythm (SMR; 12–15 Hz) or modulation of the theta/beta ratio (TBR; theta: 4.5–7.5 Hz, beta: 17–21 Hz). We investigated whether healthy individuals are able to learn, within 30 NFT sessions, how to modulate either the SMR (n = 13) or the TBR (n = 14), and whether such modulation can lead to an enhancement in different cognitive or creative tasks. A control group (n = 14) that received NFT with daily changing frequency bands and instructions was included for comparison. Although neither the TBR group nor the control group was able to modulate the EEG in the trained frequency bands, the SMR group was successful in doing so. In addition, only the SMR group was able to attain significantly better results in simple and choice reaction time tasks and a spatial rotation task after training as compared to the two other groups. No effects of NFT were found for the other attention-related tasks or for creative tasks. A series of 30 SMR training sessions can increase the ability to increase SMR amplitudes and therefore may have a future application in settings where the cultivation of fast reactions and good visuospatial abilities are relevant (e.g., in sports).
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Neurofeedback (NFB) involves a brain–computer interface that allows users to learn to voluntarily control their cortical oscillations, reflected in the electroencephalogram (EEG). Although NFB is being pioneered as a noninvasive tool for treating brain disorders, there is insufficient evidence on the mechanism of its impact on brain function. Furthermore, the dominant rhythm of the human brain is the alpha oscillation (8–12 Hz), yet its behavioral significance remains multifaceted and largely correlative. In this study with 34 healthy participants, we examined whether during the performance of an attentional task, the functional connectivity of distinct fMRI networks would be plastically altered after a 30-min session of voluntary reduction of alpha rhythm (n = 17) versus a sham-feedback condition (n = 17). We reveal that compared to sham-feedback, NFB induced an increase of connectivity within regions of the salience network involved in intrinsic alertness (dorsal anterior cingulate), which was detectable 30 min after termination of training. The increase in salience network (default-mode network) connectivity was negatively (positively) correlated with changes in ‘on task’ mind-wandering as well as resting state alpha rhythm. Crucially, we observed a causal dependence between alpha rhythm synchronization during NFB and its subsequent change at resting state, not exhibited by the SHAM group. Our findings provide neurobehavioral evidence for the brain's exquisite functional plasticity, and for a temporally direct impact of NFB on a key cognitive control network, suggesting a promising basis for its use to treat cognitive disorders under physiological conditions.
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Neurofeedback (NF) is a training to enhance self-regulatory capacity over brain activity patterns and consequently over brain mental states. Recent findings suggest that NF is a promising alternative for the treatment of attention-deficit/hyperactivity disorder (ADHD). We comprehensively reviewed literature searching for studies on the effectiveness and specificity of NF for the treatment of ADHD. In addition, clinically informative evidence-based data are discussed. We found 3 systematic review on the use of NF for ADHD and 6 randomized controlled trials that have not been included in these reviews. Most nonrandomized controlled trials found positive results with medium-to-large effect sizes, but the evidence for effectiveness are less robust when only randomized controlled studies are considered. The direct comparison of NF and sham-NF in 3 published studies have found no group differences, nevertheless methodological caveats, such as the quality of the training protocol used, sample size, and sample selection may have contributed to the negative results. Further data on specificity comes from electrophysiological studies reporting that NF effectively changes brain activity patterns. No safety issues have emerged from clinical trials and NF seems to be well tolerated and accepted. Follow-up studies support long-term effects of NF. Currently there is no available data to guide clinicians on the predictors of response to NF and on optimal treatment protocol. In conclusion, NF is a valid option for the treatment for ADHD, but further evidence is required to guide its use.
This study used a peripheral vision test that evaluates how well visual information captured in two different areas of the retina is used and tries to establish a relation with the performance of the test subjects in other fields. Automatic detection of ocular movement is used to distinguish between responses based on information from different retinal sites. Test subjects were athletes and were evaluated on how well they can recognize and relate objects in their peripheral and foveal field while focused on some different task. The correctness of their decisions based on this visual information is then compared to objective and subjective data on their athletic performance and other attributes.
Perception depends on the interplay of ongoing spontaneous activity and stimulus-evoked activity in sensory cortices. This raises the possibility that training ongoing spontaneous activity alone might be sufficient for enhancing perceptual sensitivity. To test this, we trained human participants to control ongoing spontaneous activity in circumscribed regions of retinotopic visual cortex using real-time functional MRI-based neurofeedback. After training, we tested participants using a new and previously untrained visual detection task that was presented at the visual field location corresponding to the trained region of visual cortex. Perceptual sensitivity was significantly enhanced only when participants who had previously learned control over ongoing activity were now exercising control and only for that region of visual cortex. Our new approach allows us to non-invasively and non-pharmacologically manipulate regionally specific brain activity and thus provide "brain training" to deliver particular perceptual enhancements.
This is a study on the effect of neurofeedback on chronic inpatient complex paranoid schizophrenics. The purpose of this research was twofold: first, to determine the effects of the application of neurofeedback to very chronic cases of schizophrenia that had been resistant to years of inpatient medical and psychological treatment and second, to propose a connection paradigm of schizophrenia. The author obtained progress using neurofeedback with more than 70 hospital inpatients with chronic schizophrenia. Improvements were seen in the EEG patterns and in cognitive, affective and behavioral patterns that often resulted in successful release from the hospital to live in the community. A 2-year follow up found that positive changes were sustained. It is the author's impression that reinforcement of right parietal alpha and inhibiting frontal delta and fast beta activity obtained the best results.