ArticlePDF Available

Golf Performance Enhancement and Real-Life Neurofeedback Training Using Personalized Event-Locked EEG Profiles

Authors:

Abstract and Figures

Background. This study reports on a new method for golf performance enhancement employing personalized real-life neurofeedback during golf putting.Method. Participants (n = 6) received an assessment and three real-life neurofeedback training sessions. In the assessment, a personal event-locked electroencephalographic (EEG) profile at FPz was determined for successful versus unsuccessful putts. Target frequency bands and amplitudes marking optimal prefrontal brain state were derived from the profile by two raters. The training sessions consisted of four series of 80 putts in an ABAB design. The feedback in the second and fourth series was administered in the form of a continuous NoGo tone, whereas in the first and third series no feedback was provided. This tone was terminated only when the participants EEG met the assessment-defined criteria. In the feedback series, participants were instructed to perform the putt only after the NoGo tone had ceased.Results. From the personalized event-locked EEG profiles, individual training protocols were established. The interrater reliability was 91%. The overall percentage of successful putts was significantly larger in the second and fourth series (feedback) of training compared to the first and third series (no feedback). Furthermore, most participants improved their performance with feedback on their personalized EEG profile, with 25% on average.Conclusions. This study demonstrates that the “zone” or the optimal mental state for golf putting shows clear recognizable personalized patterns. The learning effects suggest that this real-life approach to neurofeedback improves learning speed, probably by tapping into learning associated with contextual conditioning rather than operant conditioning, indicating perspectives for clinical applications.
Content may be subject to copyright.
Reformatted author copy of:
Arns, M., Kleinnijenhuis, M., Fallahpour, K., & Breteler, R. (2008). Golf Performance Enhancement
and Real-Life Neurofeedback Training Using Personalized Event-Locked EEG Profiles. Journal of
Neurotherapy, 11(4), 1118. doi:10.1080/10874200802149656
Golf Performance Enhancement and
Real-Life Neurofeedback Training Using
Personalized Event-Locked EEG Pro-
files
Martijn Arns, Michiel Kleinnijenhuis, Kamran Fallahpour, Ri-
en Breteler
Background. This study reports on a new method for golf performance en-
hancement employing personalized real-life neurofeedback during golf putting.
Method. Participants (n 1⁄4 6) received an assessment and three real-life neu-
rofeedback training sessions. In the assessment, a personal event-locked elec-
troencephalographic (EEG) profile at FPz was determined for successful versus
unsuccessful putts. Target frequency bands and amplitudes marking optimal pre-
frontal brain state were derived from the profile by two raters. The training ses-
sions consisted of four series of 80 putts in an ABAB design. The feedback in the
second and fourth series was administered in the form of a continuous NoGo
tone, whereas in the first and third series no feedback was provided. This tone
was terminated only when the participants EEG met the assessment-defined cri-
teria. In the feedback series, participants were instructed to perform the putt only
after the NoGo tone had ceased.
Results. From the personalized event-locked EEG profiles, individual training
protocols were established. The interrater reliability was 91%. The overall per-
centage of successful putts was significantly larger in the second and fourth se-
ries (feedback) of training compared to the first and third series (no feedback).
Furthermore, most participants improved their performance with feedback on
their personalized EEG profile, with 25% on average.
Conclusions. This study demonstrates that the ‘‘zone’’ or the optimal mental
state for golf putting shows clear recognizable personalized patterns. The learn-
ing effects suggest that this real-life approach to neurofeedback improves learn-
ing speed, probably by tapping into learning associated with contextual condition-
ing rather than operant conditioning, indicating perspectives for clinical applica-
tions.
Keywords. neurofeedback, peak performance, golf, EEG, personalized, wireless
Reformatted author copy of:
Arns, M., Kleinnijenhuis, M., Fallahpour, K., & Breteler, R. (2008). Golf Performance Enhancement
and Real-Life Neurofeedback Training Using Personalized Event-Locked EEG Profiles. Journal of
Neurotherapy, 11(4), 1118. doi:10.1080/10874200802149656
Introduction
The majority of research studies exploring the utility of neurofeedback
in sports performance enhancement are noncontrolled group studies
or case studies (Landers et al., 1991). Nevertheless, these studies
indicate that neurofeedback is a promising method for sports perfor-
mance enhancement. Hammond (2007) reviewed some of the re-
search in this area and pointed to the potential for the use of neu-
rofeedback in performance enhancement in various sports. He also
described some of the limitations of approaches that do not account
for individual differences and the different demands of various sports.
Haufler, Spalding, Maria, and Hatfield (2000) reported that marksmen
showed less activation when shooting a target as demonstrated by a
decrease in fast activity and an increase in synchronization in the al-
pha band but with a focus in the left central-temporalparietal areas.
Other research for archery (Hatfield, Landers, & Ray, 1984; Salazar
et al., 1990) and before golf putting (Crews & Landers, 1993) showed
an increase in alpha power (corresponding to a decrease in activa-
tion) in the aiming and focusing period, known in the literature as the
preparatory period. More important, the relationship between sports
performance and EEG measures found increased left-temporal alpha
is associated with decreased performance in marksman (Hatfield et
al., 1984) and archers (Salazar et al., 1990), but increased right-
temporal alpha is associated with increased performance in golfers
(Crews & Landers, 1993). In an early study, (Landers et al., 1991) re-
ported that right cerebral hemisphere slow cortical potential (SCP) or
Bereitschaftspotential training (suggested to correspond to increased
activation) in archery led to a decline in performance in contrast to the
group who showed an increase in performance with left hemisphere
SCP training, indicating the power to either improve or impair perfor-
mance via neurofeedback training.
However, different sports and even different tasks within the same
sport are likely to require a totally different pattern of activation in the
brain and the autonomic nervous system. Furthermore, assessment
and training for performance enhancement various electroencephalo-
graphic (EEG) frequencies can have a functional significance that is
highly variable across individuals. For example, consider the implica-
Reformatted author copy of:
Arns, M., Kleinnijenhuis, M., Fallahpour, K., & Breteler, R. (2008). Golf Performance Enhancement
and Real-Life Neurofeedback Training Using Personalized Event-Locked EEG Profiles. Journal of
Neurotherapy, 11(4), 1118. doi:10.1080/10874200802149656
tion of the alpha activity related to optimal response preparation.
Based on the work of Klimesch (1999), the individual alpha peak can
be defined as the frequency showing maximum power density peak
within a large frequency range lasting from 4 to 16 Hz, and therefore
the alpha band may or may not fall within the 8 to 13Hz range as de-
scribed in some of the EEG and neurofeedback literature. Consider-
ing this important factor, the assessment and training of alpha may
require a totally different frequency range, which is again personal-
ized and unique to that individual.
We agree with the conclusions made by Hammond (2007) as he
suggested that different brains demand different approaches. Simplis-
tic one-size-fits-all approaches to neurofeedback in sports are likely
to be ineffective across various tasks and sports. This is also in line
with new approaches to clinical treatment such as personalized med-
icine and the development of the Diagnostic and Statistical Manual of
Mental Disorders (5th ed.; American Psychiatric Association, in
press) focusing more on individual differences (genotype and neuro-
biological phenotype) and personalized treatments rather than behav-
ior-based diagnosis and treatment (Gordon, 2007). In addition to the
use of personalized approaches, a taskrelated to real-lifetraining will
probably facilitate learning, as new skills are acquired in the context
where they need to be exercised.
In the study presented here, we investigated the existence and dis-
criminative power of personal success profiles in the EEG, using a
within-subject design comparing successful versus unsuccessful golf
putts. To explore whether these personal success profiles were func-
tionally associated with putting skills, we provided participants with
real-lifeneurofeedback to see if they were able to improve their putting
skills.
Methods
Participants
Six participants participated in the experiment (3 female, 3 male).
Participants were all amateur golf players. Their average handicap
was 12.3 (SD = 5.6).
Reformatted author copy of:
Arns, M., Kleinnijenhuis, M., Fallahpour, K., & Breteler, R. (2008). Golf Performance Enhancement
and Real-Life Neurofeedback Training Using Personalized Event-Locked EEG Profiles. Journal of
Neurotherapy, 11(4), 1118. doi:10.1080/10874200802149656
Apparatus
The assessment took place on the putting range of a golf course (An-
derstein, The Netherlands). A table was set up near the putting hole
on which recording PCs were placed. The experimenters were seated
behind the table. Because weather conditions made it impossible to
continue the training outside, not all training sessions were held out-
doors. The majority of training sessions were held indoors on artificial
grass measuring 145 × 400 cm. A putting cup was placed on the arti-
ficial grass. A table holding the equipment was placed next to the
grass, on the side of the putting cup. The experimenter was seated
behind the putting cup. A marker was placed at the 50% successful
putting distance.
All EEG recordings and feedback sessions were recorded using the
wireless BraInquiry 2-channel PET EEG with active electrodes and
BioExplorer software. The PET EEG was attached on the partici-
pants’ back on an elastic band around the chest. Wires were lead
over the participants’ backs such that it minimized inconvenience and
maximized freedom of movement. The first channel of the PET EEG
was used to record EEG from FPz, referenced against linked mas-
toids [(A1 + A2) / 2]. The ground was placed on the left side of the
forehead. Disposable SilverSilver-Chloride (Ag=Ag
+
Cl
-
) electrodes
(Arbo H124-SG electrodes, Tyco) were used for EEG recording. All
electrode sites were prepared with alcohol and Nuprep.
Ball impact was recorded using a microphone (AV-JEFE TCM 160),
which was mounted on top of the putter. The microphone signal was
recorded on the second channel of the PET EEG. Participants used
their own putter.
Procedure
Assessment. All participants first participated in an assessment ses-
sion. This session was included to determine the participants’ per-
sonalized event-locked EEG profile. A warm-up round was used to
determine the participantspersonalized 50% successful putting dis-
tance (PD
50
). Participants performed series of 10 putts, which were
scored as successful holedor unsuccessful not holed. After each se-
Reformatted author copy of:
Arns, M., Kleinnijenhuis, M., Fallahpour, K., & Breteler, R. (2008). Golf Performance Enhancement
and Real-Life Neurofeedback Training Using Personalized Event-Locked EEG Profiles. Journal of
Neurotherapy, 11(4), 1118. doi:10.1080/10874200802149656
ries, the percentage of successful putts in that series was deter-
mined. According to this percentage, participants had to in-
crease=decrease their putting distance in the next series. This pro-
cess was repeated until participants scored 50% accuracy. The dis-
tance at which this occurred first was taken as the PD
50
. The PD
50
was used as putting distance in the assessment of the event-locked
EEG profile and during the subsequent trainings.
In the assessment session, participants performed eight series of 10
putts (total 80 putts, approximately 40 successful and 40 unsuccess-
ful) while both EEG and ball impact were recorded. The experiment-
ers recorded the outcome (successful or unsuccessful) manually.
These data were used to generate each participant’s personal and
individual profile using event-locked averaging of the EEG preand
postball impact in different frequency bands. This provided the indi-
vidual EEG profiles for successful versus unsuccessful putts, which
could vary from participant to participant.
Training. During training sessions, participants received feedback on
their brain activity. The training consisted of three sessions (over dif-
ferent days) consisting of four series of 80 putts from their PD
50
in an
ABAB design (no feedbackfeedbackno feedbackfeedback). The
feedback consisted of a continuous NoGo tonedelivered to the par-
ticipant through notebook speakers that was terminated when the
participant reached his or her personally determined optimal EEG
profile.
EEG was recorded from FPz referenced against linked mastoids dur-
ing training. From the EEG, amplitudes of the individually assessed
frequency bands were extracted. The NoGo tone terminated when all
the amplitudes to be rewarded exceeded the preset reward thresh-
olds, whereas the amplitudes to be inhibited were below the preset
inhibit thresholds. Besides the individually determined rewards and
inhibits, termination of the tone was prevented during the occurrence
of excessive 50Hz noise, which was used as a correlate of imped-
ance (reflected as > 10µV of 50Hz), EMG or EEG powerwhich, on
FPz, usually indicates an eye blink. When the tone ceased it was set
to be absent for at least 1.5sec, except when an eye blink occurred.
Reformatted author copy of:
Arns, M., Kleinnijenhuis, M., Fallahpour, K., & Breteler, R. (2008). Golf Performance Enhancement
and Real-Life Neurofeedback Training Using Personalized Event-Locked EEG Profiles. Journal of
Neurotherapy, 11(4), 1118. doi:10.1080/10874200802149656
All instructions were standardized. The participants were instructed to
do the following:
1. If they felt ready, initiate putting as soon as possible after the
tone ceased.
2. Make the putt within 1.5 sec from the moment when the feed-
back sound ceased.
3. Carry out the putt when the decision was made to do so, irre-
spective of the possible !return of the NoGo tone. !All putts were
scored manually as being successful or unsuccessful.
Data Analysis
Assessment. The EEG data from the assessment were bandpass fil-
tered using BioReview software (Theta: 48 Hz, Alpha: 812 Hz,
sensorimotor rhythm [SMR]: 1215 Hz, Beta: 1530 Hz, Alpha-1: 8
10 Hz, Alpha-2: 1012 Hz, Beta-1: 1522.5 Hz, and Beta-2: 22.5 30
Hz). Note that the EEG was also filtered in the SMR frequency band,
however given the recording locationof coursethis is not SMR but
should be seen as low beta. The frequency band amplitudes were
averaged locked to the event of ball impact for successful and unsuc-
cessful putts separately (e.g., the EEG data of approximately 40 suc-
cessful events were aligned on the exact timing of the ball impact and
then averaged over the event-related EEG).To establish a personal-
ized training profile, the eventlocked amplitude spectra for successful
and unsuccessful responses were printed with 1sec preputt and 0.5-
sec postputt interval and rated by two raters (see Figure 1).
Neurofeedback Training. Training results were averaged over partici-
pants and evaluated in a 3×2×2 (Session × Feedback × Series) anal-
ysis of variance (ANOVA). In addition, post hoc 2 × 2 (Feedback ×
Series) ANOVAs were carried out for each of the training sessions.
Reported effects for ANOVA are Pillai’s Trace.
Reformatted author copy of:
Arns, M., Kleinnijenhuis, M., Fallahpour, K., & Breteler, R. (2008). Golf Performance Enhancement
and Real-Life Neurofeedback Training Using Personalized Event-Locked EEG Profiles. Journal of
Neurotherapy, 11(4), 1118. doi:10.1080/10874200802149656
Fig 1. Event-locked amplitude spectra for successful vs. unsuccessful responses.
Reformatted author copy of:
Arns, M., Kleinnijenhuis, M., Fallahpour, K., & Breteler, R. (2008). Golf Performance Enhancement
and Real-Life Neurofeedback Training Using Personalized Event-Locked EEG Profiles. Journal of
Neurotherapy, 11(4), 1118. doi:10.1080/10874200802149656
Results
Assessment. The average PD
50
was found to be 149 cm (SD = 30
cm). The average percentage of successful putts in the assessment
was 48.7% (SD 1⁄4 5.1%). Event-locked averaging of the EEG re-
vealed a clear EEG pattern for each of the participants where for the
successful versus unsuccessful putts clear patterns were observed in
the last second before ball impact. As hypothesized, these EEG pro-
files were quite different for most of the participants. Figure 1 shows
three examples of the EEG profiles. The obtained training settings for
each participant, which were used in the subsequent training are
shown in Table 1. After rating of all the individual profiles, the conclu-
sions of the raters were compared and revealed only one minor dif-
ference in the training protocols. Consequently, the interrater reliabil-
ity was 91%.
Neurofeedback Training. Accuracy scores for the three training
sessions are summarized in Figure 2. A 3 × 2 × 2 (Session ×
Feedback × Series) repeated measures ANOVA was performed
on the accuracy scores. The effects of session, F(2, 4) =
288.068, p < .000, and feedback, F(1, 5) = 16.757, p = .009,
were found to be highly significant. The main effect of feedback
indicates significantly larger accuracies in the feedback series
compared to the no-feedback series and therefore demonstrates
a clear effect of the feedback. The main effect of session indi-
cates that the accuracy performance was different over the three
sessions. The main effect of series or interactions was not signif-
icant.
Table 1. Obtained training settings for each participant used during the training
TABLE 1. The obtained training settings for each participant used during the training.
Participant Theta Alpha SMR Beta Alpha 1 Alpha 2 Beta 1 Beta 2
AH < 18 < 18 < 8 < 15
AV < 15 < 6 < 9 < 6
EB < 18 < 14 < 12
FK < 15 < 10 < 8 < 10 < 8
HK < 20 < 10 < 10 < 13
IW < 25 < 9 < 10
Note. SMR ¼ sensorimotor rhythm.
FIGURE 1. The event-locked amplitude spectra for successful and unsuccessful responses.
Scientific Articles 15
Reformatted author copy of:
Arns, M., Kleinnijenhuis, M., Fallahpour, K., & Breteler, R. (2008). Golf Performance Enhancement
and Real-Life Neurofeedback Training Using Personalized Event-Locked EEG Profiles. Journal of
Neurotherapy, 11(4), 1118. doi:10.1080/10874200802149656
Figure 2. The putting accuracy over four sessions.
To investigate where these effects occurred, we performed post
hoc 2 × 2 (Feedback × Series) ANOVAs for each of the sessions
individually. In Session 1, a significant effects of series, F(1, 5) =
8.378, p = .034, was found. In Session 2, a highly significant ef-
fect of feedback was found, F(1, 5) = 111.938, p < .001, and post
hoc t-tests revealed that the first series of the no-feedback condi-
tion differed from the first series of the feedback condition, t(5) =
4.862, p = .005, and the second series of the feedback condi-
tion, t(5) = 6.145, p = .002. No other post hoc differences were
found. The ANOVA of the third session revealed no significant
effects.
Discussion
This study showed that differential EEG profiles exist for suc-
cessful versus unsuccessful golf putts for each individual. Our
data indicate a large variability in these success profiles between
different participants. Furthermore, we also showed that when
participants are trained on their personalized brain profiles relat-
Reformatted author copy of:
Arns, M., Kleinnijenhuis, M., Fallahpour, K., & Breteler, R. (2008). Golf Performance Enhancement
and Real-Life Neurofeedback Training Using Personalized Event-Locked EEG Profiles. Journal of
Neurotherapy, 11(4), 1118. doi:10.1080/10874200802149656
ed to successful golf putts they can learn to improve their putting
performance, demonstrating the relationship between these per-
sonal brain profiles and putting performance. This was demon-
strated in a controlled ABAB design, showing that participants
scored up to 25% more putts in the feedback condition (B) com-
pared to the no-feedback condition (A). The EEG training loca-
tion we used was FPz, whereas most published studies have fo-
cused on laterality (e.g., right vs. left temporal EEG). In a pilot
study, the event-locked averaging method showed clearer pat-
terns than laterality measures (the ECG and 2 channels EEG),
and therefore the 1 channel of EEG was chosen for this study.
Previous studies investigating success profiles in sports people
have mainly focused on group data (Crews & Landers, 1993;
Hatfield et al., 1984; Konttinen, Landers, & Lyytinen, 2000;
Landers et al., 1991; Salazar et al., 1990). In this study we clear-
ly demonstrated that different people under similar task condi-
tions show personalized success patterns in the EEG in the 1-
sec interval prior to putting a golf ball. Some participants in our
study indeed showed increased prefrontal alpha before ball im-
pact as the optimal mental state, as previous literature suggests
(Crews & Landers, 1993; Salazar et al., 1990). However, in other
participants, increased SMR or low beta (Participant 1 in Figure
1) was associated with the optimal prefrontal brain state. Others
showed a phase shift in their prefrontal alpha and theta activity
(Participant 3 in Figure 1) for unsuccessful putts (compared to
the successful putts), suggesting that for these participants the
timing of the activity pattern is poor in unsuccessful putts. From
these data it cannot be concluded whether these personal pro-
files are related to the individuals’ alpha peak frequency or reflect
different underlying neural networks for all participants. The ex-
ample of Participant 3 tends to suggest the latter possibility, but
more research is required to investigate that further.
From Figure 2 one can see that the trend for increased perfor-
mance is present in Session 1 but does not reach significance,
Reformatted author copy of:
Arns, M., Kleinnijenhuis, M., Fallahpour, K., & Breteler, R. (2008). Golf Performance Enhancement
and Real-Life Neurofeedback Training Using Personalized Event-Locked EEG Profiles. Journal of
Neurotherapy, 11(4), 1118. doi:10.1080/10874200802149656
as participants had difficulty during the feedback in that it was
very hard to learn to putt on command rather than putt at will in
their own routine. In the second session, highly significant differ-
ences were found between the feedback and no-feedback condi-
tion. The decline in performance in the second no-feedback se-
ries excludes that nonspecific (practice) effects alone could ac-
count for the increase in performance. A tentative explanation of
the results from Session 3 could be that the Feedback 1 condi-
tion in served as a reminder, because an (insignificant) increase
in performance is observed. In the remainder of Session 3 the
participants’ performance remains stable over conditions, sug-
gesting they learned to invoke their personalized success profile.
The results showed a significant main effect of session. The put-
ting accuracy in Session 1 was lower as compared to Sessions 2
and 3. However, because Session 3 resulted in lower accuracies
than Session 2, this effect cannot be explained as a learning ef-
fect alone. A probable explanation for the effect of session con-
cerns the training location. We were unable to finish all training
sessions in the same location but switched locations from out-
doors to indoors in the second session for most participants be-
cause of weather conditions. It was observed that in indoor loca-
tions the participants were able to achieve higher accuracies
compared to the outdoor location. These differences between
sessions should therefore be interpreted as related to external
factors such as indoors versus outdoors but also to individual
factors such as having a good or a bad day. The real training ef-
fect is demonstrated by the controlled ABAB design, effectively
controlling for these interday differences.
The event-locked averaging of EEG spectral content proved to
be a valid and promising tool to investigate personalized brain
profiles related to optimal performance, in a within-subject de-
sign. The difference between this method and event-related po-
tentials (ERPs) is that in this study EEG power of different fre-
quency bands was averaged as opposed to averaging the raw
Reformatted author copy of:
Arns, M., Kleinnijenhuis, M., Fallahpour, K., & Breteler, R. (2008). Golf Performance Enhancement
and Real-Life Neurofeedback Training Using Personalized Event-Locked EEG Profiles. Journal of
Neurotherapy, 11(4), 1118. doi:10.1080/10874200802149656
signal seen in ERP research. We propose that this method could
also be used very well in clinical applications (e.g., epilepsy and
attention deficit hyperactivity disorder [ADHD]). In ADHD, for in-
stance, with this method attentive and inattentive states can be
dissociated within the individual, and attentive states could be
rewarded in real life based on this personal profile. For epilepsy,
participants could be followed long term to obtain a personal
EEG profile serving as a marker for seizures (e.g., excess nega-
tivity, correlation dimension, SMR). On detection of the obtained
personal marker, the patient could be warned of a seizure about
to come and initiate precautionary measures (e.g., the neu-
rofeedback at that specific moment, in real life) to counteract the
epileptic seizure.
We hypothesize that the learning procedure employed in this
study is more related to classical conditioning rather than a pure
operant conditioning. The contextual situation (standing with the
putter on a green with the putting hole in view and ready to putt)
is used as a contextual stimulus and is paired to the optimal
mindset. This learning procedure relies more on pairing the op-
timal mindset to the contextual situation (classical conditioning)
than on shaping the behavior (operant conditioning). This might
explain the fast acquisition of the learned skill as evidenced by
the absence of a difference between the feedback and no feed-
back series in Session 3. This also implies that this acquired skill
is only learned for this contextual situation and not for others,
whereas regular neurofeedback often requires overtraining to
achieve generalization whereby the self-regulation skills can also
be applied in daily live (e.g., SCP control). Therefore, the real-life
methodology we applied in this study holds great promise for
clinical applications by having a clinical effect within fewer ses-
sions and being more specific with respect to the contextual situ-
ation in that no overlearning is required and skills are acquired
for only situations where they are required. However, the usabil-
ity of this approach should be investigated further for clinical ap-
Reformatted author copy of:
Arns, M., Kleinnijenhuis, M., Fallahpour, K., & Breteler, R. (2008). Golf Performance Enhancement
and Real-Life Neurofeedback Training Using Personalized Event-Locked EEG Profiles. Journal of
Neurotherapy, 11(4), 1118. doi:10.1080/10874200802149656
plications. One particularly interesting issue would be to see
whether with increasing experience the duration of the tone
would decrease. This was not been monitored in this study, yet a
decrease would further support the validity of this learning pro-
cedure, comparable to the early studies of Kamiya (1968), who
taught participants to initiate a state change.
References
American Psychiatric Association. (in press). Diagnostic and Statistical Manual of
Mental Disorders (5th ed.). Washington, DC: Author.
Crews, D. J., & Landers, D. M. (1993). Electroencephalographic measures of at-
tentional patterns prior to golf putt. Medicine and Science in Sports and Exercise,
25(1), 116–126.
Gordon, E. (2007). Integrating genomics and neuromarkers for the era of brain-
related personalized medicine. Personalized Medicine, 4(2), 201–215.
Hammond, D.C. (2007). Neurofeedback for the enhancement of athletic perfor-
mance and physical balance. The Journal of the American Board of Sport Psy-
chology, 1–2007, Article 1.
Hatfield, B. D., Landers, D. M., & Ray, W. J. (1984). Cognitive processes during
self-paced motor performance: An electro-encephalographic profile of skilled
marksmen. Journal of Sport Psychology, 6, 42–59.
Haufler, A. J., Spalding, T. W., Maria, S., D., L., & Hatfield, B. D. (2000). Neuro-
cognitive activity during a self-paced visuospatial task: Comparative EEG profiles
in marksmen and novice shooters. Biological Psychology, 53, 131160.
Kamiya, J. (1968). Conscious control of brain waves. Psychology Today, 1, 57–
60.
Klimesch, W. (1999). EEG alpha and theta oscillations reflect cognitive and
memory performance: A review and analysis. Brain Research Reviews, 29, 169–
195.
Konttinen, N., Landers, D. M., & Lyytinen, H. (2000). Aiming routines and their
electrocortical concomitants among competitive rifle shooters. Scandinavian
Journal of Medicine & Science in Sports, 10(3), 169–177.
Landers, D. M., Petruzzello, S. J., Salazar, W., Crews, D. J., Kubitz, K. A., Gan-
non, T. L., et al. (1991). The influence of electrocrtical biofeedback on perfor-
mance in pre-elite archers. Medicine and Science in Sports and Exercise, 23(1),
123129.
Salazar, W., Landers, D. M., Petruzello, S. J., Han, M., Crews, D. J., & Kubitz, K.
A. (1990). Hemispheric asymmetry, cardiac response, and performance in elite
archers. Research Quarterly for Exercise and Sport, 61(4), 351–359.
... 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). ...
... Null findings have been observed with the use of nonfunction-directed verbal instructions in EEG NFT studies, possibly because the verbal instructions did not provide the participants with a specific strategy (i.e., how to regulate a certain brain region) to achieve an optimal mental state. For example, the participants were asked to develop their own strategies (Kao et al., 2014) and to feel ready (Arns et al., 2008) to control specific EEG activities (i.e., Fz 4-7 Hz, Cz 12-15 Hz, and Fz 10-12 Hz) J o u r n a l P r e -p r o o f PERFORMANCE during EEG NFT. Similarly, the instruction "pay attention to reducing the theta power alongside high-alpha power" was used in an EEG NFT study that aimed to improve golf putting performance (Ring et al., 2015). ...
Article
Full-text available
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.
... Meanwhile, Gruzelier et al. (2014) found that alpha and theta SP-NFT improved dancers' creativity but without significantly impacting dance performance or anxiety (Gruzelier et al., 2014). Arns et al. (2008) applied SP-NFT in golf during the preparation stage, finding a 25% decrease in participants' average score (Arns et al., 2008). In addition, Cheng et al. (2015a) found that SP-NFT significantly improved golf putting performance in mean distance, standard deviation, and successful training ratio (Cheng et al., 2015a). ...
... Meanwhile, Gruzelier et al. (2014) found that alpha and theta SP-NFT improved dancers' creativity but without significantly impacting dance performance or anxiety (Gruzelier et al., 2014). Arns et al. (2008) applied SP-NFT in golf during the preparation stage, finding a 25% decrease in participants' average score (Arns et al., 2008). In addition, Cheng et al. (2015a) found that SP-NFT significantly improved golf putting performance in mean distance, standard deviation, and successful training ratio (Cheng et al., 2015a). ...
Article
Full-text available
Neurofeedback training (NFT) is a non-invasive, safe, and effective method of regulating the nerve state of the brain. Presently, NFT is widely used to prevent and rehabilitate brain diseases and improve an individual’s external performance. Among the various NFT methods, NFT to improve sport performance (SP-NFT) has become an important research and application focus worldwide. Several studies have shown that the method is effective in improving brain function and motor control performance. However, appropriate reviews and prospective directions for this technology are lacking. This paper proposes an SP-NFT classification method based on user experience, classifies and discusses various SP-NFT research schemes reported in the existing literature, and reviews the technical principles, application scenarios, and usage characteristics of different SP-NFT schemes. Several key issues in SP-NFT development, including the factors involved in neural mechanisms, scheme selection, learning basis, and experimental implementation, are discussed. Finally, directions for the future development of SP-NFT, including SP-NFT based on other electroencephalograph characteristics, SP-NFT integrated with other technologies, and SP-NFT commercialization, are suggested. These discussions are expected to provide some valuable ideas to researchers in related fields.
... These studies have led to a broad understanding of the chronometry of processes within the visual-motor cascade, including evidence that higher attentional states, indexed by less default-mode processing and lower alpha power, are associated with better processing of visual stimuli (e.g., Macdonald, Mathan, & Yeung, 2011). These findings and the ease-of-use of EEG have led to a growing number of studies evaluating expertise in activities such as marksmanship (Berka, Behneman, Kintz, Johnson, & Raphael, 2010;Janelle & Hatfield, 2008;Hatfield, Haufler, Hung, & Spalding, 2004;Hillman, Apparies, Janelle, & Hatfield, 2000), golf putting (Arns, Kleinnijenhuis, Fallahpour, & Breteler, 2008;Babiloni et al., 2008), table tennis (Hülsdünker, Ostermann, & Mierau, 2019), badminton (Hülsdünker, Strüder, & Mierau, 2017), and archery (Seo et al., 2012;Landers, Han, Salazar, & Petruzzello, 1994). Moreover, because the EEG power spectra and ERPs associated with sensory processing, decision making, and error recognition in these tasks correlate with trial-by-trial response speed (Hülsdünker et al., 2019) and different levels of accomplishment (Hatfield et al., 2004), they may provide potentially useful biomarkers in closedloop neurofeedback and neurostimulation approaches (Gruzelier, 2014;Paulus et al., 2009). ...
... Finally, although highly exploratory in the context of this study, the FRN, which is known to occur in EEG recordings after a participant recognizes an error, was also found to occur at earlier latencies and with lower mean amplitudes on successive days of practice. Such changes in sensory processing have been observed with learning in other domains, including perceptual learning (Appelbaum, Wade, Vildavski, Pettet, & Norcia, 2006), visual search (Arns et al., 2008;Aggarwal et al., 2006), and reward learning (dos Santos Mendes et al., 2012). As observed in Figure 7, subsequent differences in the later positive P3 component may also have indexed changes on a trial-by-trial or visit-by-visit basis. ...
Article
Full-text available
The fusion of immersive virtual reality, kinematic movement tracking, and EEG offers a powerful test bed for naturalistic neuroscience research. Here, we combined these elements to investigate the neuro-behavioral mechanisms underlying precision visual–motor control as 20 participants completed a three-visit, visual–motor, coincidence-anticipation task, modeled after Olympic Trap Shooting and performed in immersive and interactive virtual reality. Analyses of the kinematic metrics demonstrated learning of more efficient movements with significantly faster hand RTs, earlier trigger response times, and higher spatial precision, leading to an average of 13% improvement in shot scores across the visits. As revealed through spectral and time-locked analyses of the EEG beta band (13–30 Hz), power measured prior to target launch and visual-evoked potential amplitudes measured immediately after the target launch correlate with subsequent reactive kinematic performance in the shooting task. Moreover, both launch-locked and shot/feedback-locked visual-evoked potentials became earlier and more negative with practice, pointing to neural mechanisms that may contribute to the development of visual–motor proficiency. Collectively, these findings illustrate EEG and kinematic biomarkers of precision motor control and changes in the neurophysiological substrates that may underlie motor learning.
... Based on the immediacy of this transfer effect, it seems advisable to apply FM theta NFT directly before athletic training or competition. Similarly to a NFT study that applied real-life neurofeedback during golf putting (Arns et al., 2007), FM theta NFT might also be combined with the motor movement that is aimed to be improved if the sport discipline allows for an artifact-free EEG measurement. Compared to often long-lasting mindfulness interventions (Tang et al., 2010), NFT has the advantage that it can induce effects even with a short training session and that it involves direct feedback about one's brain activity, supporting users to learn effective self-regulation quickly. ...
Article
Full-text available
Flow is defined as a cognitive state that is associated with a feeling of automatic and effortless control, enabling peak performance in highly challenging situations. In sports, flow can be enhanced by mindfulness training, which has been associated with frontal theta activity (4-8 Hz). Moreover, frontal-midline theta oscillations were shown to subserve control processes in a large variety of cognitive tasks. Based on previous theta neurofeedback training studies, which revealed that one training session is sufficient to enhance motor performance, the present study investigated whether one 30-minute session of frontal-midline theta neurofeedback training (1) enhances flow experience additionally to motor performance in a finger tapping task, and (2) transfers to cognitive control processes in an n -back task. Participants, who were able to successfully upregulate their theta activity during neurofeedback training (responders), showed better motor performance and flow experience after training than participants, who did not enhance their theta activity (non-responders). Across all participants, increase of theta activity during training was associated with motor performance enhancement from pretest to posttest irrespective of pre-training performance. Interestingly, theta training gains were also linked to the increase of flow experience, even when corresponding increases in motor performance were controlled for. Results for the n -back task were not significant. Even though these findings are mainly correlational in nature and additional flow-promoting influences need to be investigated, the present findings suggest that frontal-midline theta neurofeedback training is a promising tool to support flow experience with additional relevance for performance enhancement.
... Currently, there is a definite and growing interest, in both clinical and research domains, towards this neuromodulation technique evidenced by its application to a large sample of psychiatric ailments such as addiction (Cox et al., 2016;, posttraumatic stress disorder (Reiter et al., 2016) and schizophrenia (Balconi & Vanutelli, 2019;Rieger et al., 2018). It has even extended to enhancing healthy subjects' abilities, such as improving performance (Arns et al., 2008;Crivelli et al., 2019). ...
Chapter
Addiction is a chronic relapsing disorder. Despite pharmacological and psychological interventions during rehabilitation, a majority of patients still relapse. In this seventh chapter, we present neuromodulation techniques as a complementary intervention for addiction. Firstly, while deep brain stimulation (DBS) has shown promising results, its cost–benefit–risk ratio is nonetheless too high to be implemented in routine clinical care. Secondly, repeated transcranial magnetic stimulation (rTMS) and transcranial direct courant stimulation (tDCS) over the dorsolateral prefrontal cortex (DLPFC) have shown reduced craving and relapses, but the results are mixed. To improve efficacy, new perspectives envisioned that the insula could be a promising target for rTMS and DBS in combination with cognitive remediation and while participants are exposed to key conditioned stimuli. Additionally, neurofeedback could be a useful tool in teaching patients to actively regulate their neural activity, although better controlled experimental designs and rigorous measures of brain changes are needed. Despite the heterogeneity of studies, neuromodulation techniques as complementary tools to conventional care seem to constitute a turning point in the management of addictions.
... Neurofeedback has been proven to be efficient in the therapy of many different mental disorders such as attention-deficit hyperactivity disorder (Gevensleben et al., 2009), schizophrenia (Gruzelier, 2000), and anxiety (Moore, 2000). Also improved artistic (Egner & Gruzelier, 2003;Raymond, Sajid, Parkinson, & Gruzelier, 2005), sport-related (Arns, Kleinnijenhuis, Fallahpour, & Breteler, 2008;Landers et al., 1991), and cognitive (Angelakis et al., 2007;Egner & Gruzelier, 2004) functioning due to neurofeedback has been demonstrated in healthy participants. ...
Article
Full-text available
Introduction: Regarding the neurofeedback training process, previous studies indicate that 10%-50% of subjects cannot gain control over their brain activity even after repeated training sessions. This study is conducted to overcome this problem by investigating inter-individual differences in neurofeedback learning to propose some predictors for the trainability of subjects. Methods: Eight healthy female students took part in 8 (electroencephalography) EEG neurofeedback training sessions for enhancing EEG gamma power at the Oz channel. We studied participants’ preexisting fluid intelligence and EEG frequency sub-bands’ power during 2-min eyes-closed rest and a cognitive task as psychological and neurophysiological factors, concerning neurofeedback learning performance. We also assessed the self-reports of participants about mental strategies used by them during neurofeedback to identify the most effective successful strategies. Results: The results revealed that a significant percentage of individuals (25% in this study) cannot learn how to control their brain gamma activity using neurofeedback. Our findings suggest that fluid intelligence, gamma power during a cognitive task, and alpha power at rest can predict gamma-enhancing neurofeedback performance of individuals. Based on our study, neurofeedback learning is a form of implicit learning. We also found that learning without a user’s mental efforts to find out successful mental strategies, in other words, unconscious learning, lead to more success in gamma-enhancing neurofeedback. Conclusion: Our results may improve gamma neurofeedback efficacy for further clinical usage and studies by giving insight about both non-trainable individuals and effective mental strategies.
... For example, a personalized NFT study was conducted on amateur golfers engaged in a putting task. The authors extracted EEG profiles associated with successful putts and used this to establish personalized NFT profiles (Arns, Kleinnijenhuis, Fallahpour, & Breteler, 2007). The use of these profiles led to better putting performance than under the control condition. ...
Chapter
The brain is the main locus of control for our behaviour and psychological states. Superior sport performance occurs when both the physical and mental dimension of an athlete converge in an adaptive manner to meet the challenges of the task. This chapter provides an overview of how the brain works for athletes at different levels of expertise and discusses how brain activity can be controlled to achieve superior sports performance. The multi-action plan model provides an alternative perspective for understanding the relationship between performance effectiveness and the utilization of resources in sports performance. The chapter discusses several limitations arising from the current literature and makes suggestions for future research in the hope of establishing a consensus on protocols to be followed in future studies. It provides an overview of neurofeedback training (NFT) studies used to investigate sports performance and proposes various criteria that might be used to assess the effects of NFT in sports domains.
Preprint
Full-text available
Neurofeedback training (NFT) refers to a training where the participants voluntarily aim to manipulate their own brain activity using the sensory feedback abstracted from their brain activity. NFT has attracted attention in the field of motor learning for its potential to become an alternative or additional training method for general physical training. In this study, a systematic review of NFT studies for motor performance improvements in healthy adults and a meta-analysis on the effectiveness of NFT were conducted. To identify relevant studies published between January 1st, 1990 to August 3rd, 2021, a computerized search was performed using the databases, Web of Science, Scopus, PubMed, JDreamIII, and Ichushi-Web. Thirty-two studies were identified for the qualitative synthesis and 13 randomized controlled trials (286 subjects) for the meta-analysis. The meta-analysis revealed significant effects of NFT for motor performance improvement examined at the timing after the last NFT session (standardized mean difference = 0.96, 95% CI = 0.40–1.53), but with the existence of publication biases and substantial heterogeneity among the trials. Subsequent subgroup meta-analysis demonstrated reliable benefits when the NFT is performed longer than 1 week. The effectiveness of NFT for each motor performance measurement (e.g., speed, accuracy, and hand dexterity) remains unclear because of high heterogeneity or due to small sample size. Further accumulation of empirical NFT studies for motor performance improvement will be necessary to provide reliable evidence about the NFT effects on specific motor skills and to safely incorporate NFT into real-world scenarios.
Chapter
Concentration level plays a significant role while performing cognitive actions. There are many ways to predict the concentration level, such as with the help of physical reflection, facial expressions, and body language. Self- evaluation on the scale of 0 to 1 can also be used to measure the concentration level. In this paper, a publicly available dataset is used for classifying the concentration level using students’ brain signals recorded through Electroencephalogram (EEG) device while performing different tasks that require varied concentration level. The study aims to find the appropriate Machine Learning (ML) model that predicts the concentration level through brain signal analysis. For this purpose, five different ML classifiers are used for comparative analysis, namely: Adaboost, Navie Bays, Artificial Neural Networ (ANN), Support Vector Machine (SVM) and Decision Tree. The ANN model gives the highest accuracy, i.e. 71.46% as compared to other classifiers for the concentration level measurement.
Article
Full-text available
The aim of this study was firstly to identify alpha band EEG sources playing a functional role in the performance differences between elite and amateur table tennis players use of visuo-spatial cues to guide response selection. EEG was recorded from 206 elite and amateur table tennis athletes from across the International Table Tennis Federation. EEG was obtained during eyes closed (EC) and eyes open rest (EO) and during a 4-minute video task (VT). The VT was filmed from the player’s perspective to simulate match-play against a top 100 world ranked player. Participants imagined playing against the on-screen player. Players also completed a visuo-spatially cued version of the Go-NoGo continuous performance task (vsCPT). eLORETA compared EEG source activity between an age and gender matched sample of 16 elite and 16 amateur players. Activity at maximal source differences was then correlated with behavioural vsCPT performance measures. EEG source differences between elite and amateur players reached a maximum between 10.50 and 11.75 Hz (upper alpha) in the VT condition with loci in right BA6 (supplementary motor area, sensory selection for motor control) and right BA13 (insula cortex, salience detection). Source activity estimates correlated significantly with superior processing speed and perceptual sensitivity under increased processing demands on the vsCPT. Upper alpha synchronisation in right BA6 and right BA13 when actively processing an opponents’ match specific motion is greater in elite than amateur players and indicates superior visuo-spatial guided response selection. Secondly, we sought to use Neurofeedback (NFB) training, a form of operant conditioning based on reward-learning, to produce measurable changes in the efficiency of visual spatial attention networks within a group of aspiring elite table tennis athletes within an associated region of interest, right BA40. The relationship of learning during sLoreta NFB (sLNFB) training to a strengthening of connectivity in the targeted cortical network was measured by the EEG activity of fifteen adolescent table tennis players. A learning index was used to establish a relationship between sLNFB training, learning, and post-sLNFB EEG. A motor decision (Go-NoGo) task was undertaken pre- and post-NFB training to determine if changes in cortical activity translated to improved visuo-spatial cued motor control performance. Results indicated significant changes in cortical activity in regions related to visuo-spatial and motor processing in addition to regions directly related to learning. Increased response inhibition accuracy on Go-NoGo task was strongly and significantly correlated to post NFB changes in brain activity. We concluded that the current sLNFB protocol changes cortical activity throughout functionally connected nodes of task- relevant networks. Furthermore, some of these changes are directly related to behavioural performance enhancement depending on cognitive processing within these networks. The findings provide support for sLNFB training as a tool for enhancing visuo-spatial and motor processing performance in aspiring elite table tennis players.
Article
Full-text available
The harsh reality is that many medical treatments do not work as expected in a significant percentage of patients, and occasionally there are serious side effects. A new paradigm of personalized medicine is emerging, which proactively tailors treatment to each individual's biological and psychological profile. The first proof-of-concept phase of personalized medicine has now been achieved. However, it has thus far focused on the use of genomic markers and on disorders of the body. The complexity of the brain is likely to require a shift from a single genetic marker focus to a more integrated approach in which additional brain-related information (neuromarkers) is taken into account. Codevelopment of genomic neuromarkers with new compounds in a personalized medicine approach will lead to increased drug R&D and treatment benefits. The emerging genomic neuromarker potential has begun to be incorporated into the template for the next version of the Diagnostic and Statistical Manual (DSM-V). The statistical power of large subject numbers in databases in general (and standardized databases in particular) provides an ideal source for elucidating the best genomic-neuromarker profiles (explaining most of the main-effects variance), which will empower a brain-related personalized medicine into mainstream clinical practice.
Article
Full-text available
Previous sport research on elite athletes has shown systematic changes in psychophysiological measures, such as heart rate (HR) deceleration and hemispheric asymmetries in EEG activity, in the few seconds prior to executing a motor response. These changes are believed to be due to a more focused attention on the external environment. Using archery (an attentive state), this investigation was designed to examine: (a) whether hemispheric asymmetry and HR deceleration would occur during the aiming period, and (b) if they did, whether this would affect performance. HR and left and right temporal EEG were recorded from 28 right-handed elite archers for 16 shots. The results indicated that (a) there was no HR deceleration; (b) during the aiming period, EEG alpha activity formed the dominant frequency and this was significantly greater in the left than in the right hemisphere; (c) there were no significant right hemisphere EEG changes in spectral power from 3 s before the shot to arrow release, but there were significant left hemisphere increases at 10, 12, and 24 Hz; and (d) at 1 s prior to the shot, there were no significant right hemisphere spectral power differences between best and worst shots, but there were significant left hemisphere differences at 6, 12, and 28 Hz. The relationships among hemispheric asymmetry, HR deceleration, attentional processes, and shooting performance are discussed.
Article
In Study 1, electrocortical arousal (EEG alpha activity) was assessed at 4 standardized sites from 14 male and 3 female 22–48 yr old right-handed international-caliber markspersons during rifle-shooting performance. The task consisted of the execution of 40 shots at a conventional indoor target from the standing position. During each shot preparation, a significant increase in left temporal and occipital alpha activity was demonstrated, while the right hemispheric activity remained constant. Hemispheric laterality ratios evidenced a significant shift toward right-brain dominance as the time to trigger pull approached. In Study 2, 13 male and 2 female 17–45 yr old right-handed markspersons performed the same shooting task and, additionally, the resultant EEG performance patterns were contrasted to those observed during the mental processing of stereotyped left- and right-brain mental tasks. Observed EEG patterns during shooting replicated the results of Study 1 and indicated that the laterality indices derived during shooting exhibited a more pronounced shift to right-brain processing than did those derived during right-brain mental task performance. Findings are discussed in terms of the implications from bilateral or split-brain cognitive process theory. (35 ref) (PsycINFO Database Record (c) 2012 APA, all rights reserved)
Article
The purpose of the present research was to determine whether EEG biofeedback training could improve archery performance as well as self-reported measures of concentration and self-confidence. Experienced pre-elite male (N = 16) and female (N = 8) archers were randomly assigned to one of three treatment conditions: (a) correct feedback (i.e., greater left hemisphere low frequency activity), (b) incorrect feedback (i.e., greater right hemisphere low frequency activity), and (c) no feedback control. The pretest and posttest consisted of 27 shots, with EEG data collected for the left and right temporal hemispheres (T3, T4). Feedback subjects were then given EEG biofeedback, while control subjects rested for 30 min. Analyses indicated that only the performance measure was significant. The correct feedback group significantly improved performance, while the incorrect feedback group showed a significant performance decrement from pre- to posttest (Ps less than 0.05). The control group showed no significant pre-post differences in performance. EEG analyses showed differences that were consistent with the training given to the incorrect, but not the correct, feedback group. Overall, the results provide some support for the use of known relationships between EEG and performance as an effective means of providing biofeedback to affect the performance of pre-elite archers.
Article
The purpose of this investigation was to determine the attentional focus patterns associated with golf putting performance. Highly skilled golfers (N = 34) were assessed using electroencephalographic (EEG) measures of the motor and temporal cortices during the 3 s prior to the golf putt. Players completed 40, 12-ft putts and performance was measured in cm error from the hole. Three measures of EEG were analyzed: slow shift, 40 Hz, and relative power spectrum; representing readiness to respond, focused arousal, and general cortical activity, respectively. All three EEG measures suggested a decrease in left hemisphere, motor cortex activity as the player prepared to putt. Relative power measures also showed significant increases in right hemisphere activity in both the motor and temporal cortices. During the last second preceding the putt, increased right hemisphere alpha activity correlated with and predicted less error. Hemispheric differentiation was also reduced as subjects prepared to putt and few, but important, differences existed between the motor and temporal cortices. An important distinction occurred in the alpha band. In the motor cortex left hemisphere alpha increased significantly over time while in the temporal cortex, right hemisphere alpha increased as subjects approached stroke initiation. Differences that existed between the attentional patterns from the present study and past sport studies may relate to the use of one versus two hands to initiate the response.
Article
Evidence is presented that EEG oscillations in the alpha and theta band reflect cognitive and memory performance in particular. Good performance is related to two types of EEG phenomena (i) a tonic increase in alpha but a decrease in theta power, and (ii) a large phasic (event-related) decrease in alpha but increase in theta, depending on the type of memory demands. Because alpha frequency shows large interindividual differences which are related to age and memory performance, this double dissociation between alpha vs. theta and tonic vs. phasic changes can be observed only if fixed frequency bands are abandoned. It is suggested to adjust the frequency windows of alpha and theta for each subject by using individual alpha frequency as an anchor point. Based on this procedure, a consistent interpretation of a variety of findings is made possible. As an example, in a similar way as brain volume does, upper alpha power increases (but theta power decreases) from early childhood to adulthood, whereas the opposite holds true for the late part of the lifespan. Alpha power is lowered and theta power enhanced in subjects with a variety of different neurological disorders. Furthermore, after sustained wakefulness and during the transition from waking to sleeping when the ability to respond to external stimuli ceases, upper alpha power decreases, whereas theta increases. Event-related changes indicate that the extent of upper alpha desynchronization is positively correlated with (semantic) long-term memory performance, whereas theta synchronization is positively correlated with the ability to encode new information. The reviewed findings are interpreted on the basis of brain oscillations. It is suggested that the encoding of new information is reflected by theta oscillations in hippocampo-cortical feedback loops, whereas search and retrieval processes in (semantic) long-term memory are reflected by upper alpha oscillations in thalamo-cortical feedback loops.
Article
The present study focused on an examination of competitive shooters' aiming process during a rifle shooting task. The barrel movements of the rifle, as detected by a laser system during the last 1000-ms time period preceding the triggering, were recorded from six elite and six pre-elite shooters. Electrocortical slow potentials (SPs) from frontal (Fz), centro-lateral (C3, C4), and occipital (Oz) brain areas were recorded to get an additional insight into the underlying covert processing. The results suggested that the elite shooters did not pull the trigger until they reached a sustained rifle position. In the pre-elite shooters the rifle appeared to be in a less stable position, and their strategy was to take advantage of the first appropriate moment of steadiness without a sustained rifle position so they could pull the trigger. The observed pre-trigger readiness potential (RP) shifts at Fz and Oz were more positive among the elite shooters relative to the pre-elite shooters, reflecting their more pronounced covert effort, rather than increasing preparedness for the trigger pull. The present study lends support for the view that a successful aiming strategy is mainly based on sustained rifle balancing. With regards to the brain slow potentials, it can be concluded that the RP shift does not specifically reflect the preparation for the trigger pull.