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Zainuddin et al. / International Medical Device and Technology Conference 2017
eISBN 978-967-0194-93-6 FBME
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The biomarkers of brain activity, physiology and biomechanics in
cycling performance: A literature review
Nurul Farha. Zainuddin a, Abdul Hafidz. Omar a,b, Izwyn. Zulkapri a, Mohd Syafiq. Miswan c
a Faculty of Biosciences and Medical Engineering, Universiti Teknologi Malaysia, 81310 UTM Johor Bahru, Johor, Malaysia
b Sports Technology and Innovation Centre, Universiti Teknologi Malaysia, 81310 UTM Johor Bahru, Johor, Malaysia
c Faculty of Sports Science and Recreation, Universiti Teknologi MARA, 02600 Arau, Perlis, Malaysia
* Corresponding author: nurulfarha26@gmail.com
ABSTRACT
Generally, in sports performance, the relationship between movement science and physiological function has been conducted integrating
neuronal mechanism over the past decades. However, understanding those interaction between neural network and motor performance
comprehensively in achieving optimal performance mainly in cycling is still lacking. The purpose of this study was to discuss the issues in
neuroscience related to brain activity, physiological and biomechanics in achieving optimal performance in cycling. As sports technology
improves, more objective measurement can be demonstrated in solving specific issue in cycling. In this review, the focus on brain activity will be
based on the evaluation of alpha and beta brainwaves as well as the alpha/beta ratio. Understanding of the mechanism and interaction between
brain activity, physiology and biomechanics in competitive cycling will be acquired. Moreover, the biomarkers of brain activity related to
cycling performance from previous studies will be identified and discussed.
_______________________________________________________________________________________
INTRODUCTION
As athletes, coaches, trainers and professionals aim on what
makes a difference between winning and losing, the emerging field of
sports neuroscience seeks to produce better understanding between
brain and behavior (Park et al., 2015). According to Yarrow et al.
(2009), apart from physical fitness, elite athletes must develop sport
specific cognitive skills that integrate with perception, cognition and
action. When talking about competitive cycling, especially during
high-pressurized situations, brain function plays an important role in
regulating physiological and biomechanical functions (Cheron et al.,
2016). These interactions are present in the mechanism involving
brain imaging from a neuroscience perspective. Exercise functions in
cycling are prominently explored and investigated using variables
such as maximum oxygen consumption (VO2max), power (Ludyga et
al., 2016b), and blood lactate (Hottenrott et al., 2013). Since cycling is
considered a continuous cyclic movement due to pedalling,
researchers have recently started to investigate how brain activity was
influenced and changed with cadence (Ludyga et al. 2016a; Ludyga et
al., 2016b; Ludyga et al. 2016). It involved the mechanisms of brain
acvitiy as well as physiological and biomechanical functions that have
been thought as the activation of motor units signalling from the
central nervous system (Ludyga et al., 2016). This area of study was
found to be limited in sports applications and thus even more lacking
in cycling. This was due to the methodological limitations in solving
brain function issues especially in more dynamic movements
(Brümmer et al., 2011). Therefore, the focus of this review was to
discuss the issues in neuroscience in the context of brain activity
interactions with physiological and biomechanical functions in
achieving optimal performance in cycling. Additionally, the common
methodological issues in brain activity and EEG biomarkers related to
cycling performance will also be discussed.
INTERACTION BETWEEN BRAIN ACTIVITY,
PHYSIOLOGICAL FUNCTIONS AND BIOMECHANICAL
MECHANISMS
Neurophysiology is the term much utilized in exploring
relationship between brain activity and physiology. It expresses the
link between multiple signal neurophysiologic from different neural
generators at different brain regions. It is often related to the neuron’s
ability to adapt to training and exercise which consequently leads to
neural efficiency (Ludyga et al., 2016b). Individuals who possess
neural efficiency are thought to have high cognitive task due to
increased proficiency in brain corticol function (Neubauer & Fink,
2009). It was evident that pedalling with high intensity during training
can prolongcycling performance and contribute to maximal aerobic
power (Hottenrott et al., 2013). Additionally, the different level of
athletes’ competitiveness, which was affected by their individual
training levels and participation in competition, caused variation in
their brain function activity (Nakata et al., 2010).
On another note, research has been conducted by Tytell et al.
(2011) with their aim of discovering the link between brain activity
and biomechanical mechanism. They found that interaction between
the two comprised elements of neural curcuits, muscles, enviroment
and body. The body itself represents the motor output in which the
movement was executed. According to Li (2004), he discovered that
cycling movement controlled by the central nervous system would
influence neuromuscular control to regulate the body’s postures and
pedalling cadence. This connection between the central nervous
system and neuromuscular control was due to the presence of signals
from the sensory stimulus which is required before the movement
could be executed. Furthermore, the same researchers explained that
the interactions between neural circuits, body, muscles and
environment are crucial to understand despite it being difficult to
predict (Tytell et al., 2011).
ORIGINAL PAPER
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Based on the nature of cycling, physiological variables of a well-
trained cyclist with three to five years of training for 60-240 minutes a
day should possess 70-75 ml/kg/min or 5.0-5.3 L/min of VO2max and
300-450 power output (Jeukendrup et al., 2000). In order to obtain
optimal physiological function, specific training is required to
improve the athlete’s performance. As far as mechanical efficiency is
concern, particularly in cycling, the biomechanical application is
critical. In competitve cycling, power output becomes an indicator of
mechanical efficiency to determine cyclist performance during
training which eventually predicts their physiological performance
(Reed et al., 2016). Therefore, recent issues on how these functions
can influence brain activity is highly in demand. In a high-pressurized
situation especially during competition, athletes require high central
activation to maintain and sustain high loads (Bailey et al., 2008).
Consequently, a decreases of central activation often leads to fatigue
and subsequently poor performance since it is related to brain function
central mechanism (Timothy D. Noakes, 2012). Previous findings
have confirmed that in endurance competitions, the maintenance of a
high central activation is required to prevent the reduction of power
output and the termination of exercise resulting from central fatigue
(Shober & Schumann, 1991) as cited in Ludyga et al. (2016a).
METHODOLOGICAL ISSUES IN STUDIES ON BRAIN
ACTIVITY
The methodological issues in studies on brain activity in sports
application have been highlighted since a decade ago (Thompson et
al., 2008). Investigations of brain activity have transformed from
using high cost equipment with limited feasibility in resolving rapid
variations of activity (Enders et al., 2016), such as positron emission
tomography (PET) and functional magnetic resonance imaging
(fMRI) (Brümmer et al., 2011), to the use of electroencephalographic
(EEG) recording which is cheaper, easy to wear, lightweight and has
high temporal resolution (Reis et al., 2014). The EEG is also known
for its non-invasive nature of high density recording in providing
quantitative feedback to practitioners and coaches (Chapman et al.,
2008; Cheron et al., 2016; Lopes da Silva, 2010; Reis et al., 2014;
Thompson et al., 2008). However, for application in sports setting,
there is still a need for improved hardware and software that are able
to minimize artifacts. The artifacts that potentailly occur are muscle
artifact, skin artifact, electrode movement, eye movement, ECG
artifact, respiration artifact, tongue movement, electrical inteference,
and restriction of mobility (Thompson et al., 2008).
The use of EEG in sporting research, either in the context of
exercise or for competitive purposes, has always been more dominant
among less dynamic sports. Less dynamic movement during
locomotion, such as static or cyclic motion, was found to be able to
minimize artifacts. In fact, these type of movements enable research to
be conducted in the lab setting. However, recent development in
mobile EEG technology provides an opportunity in tackling many
issues related to neuroscience and sporting behavior despite having
challenges to move out from the lab (Park et al., 2015).
THE BIOMARKERS OF EEG RELATED TO CYCLING
PERFORMANCE
Table 1 The biomarkers of EEG related to cycling
Brainwaves
Previous
study
Variables
Corresponding
function
Alpha/beta
ratio
(Ludyga et
al., 2016)
Effects of high
cadence
Vigilance
alpha/beta
ratio
(Ludyga et
al., 2016b)
High vs low
aerobic power
Neural efficiency
Alpha, beta
(Ludyga, et
al., 2016a)
High versus low
cadence upon
aerobic
Sensorimotor
processing,
arousal
performance
Alpha, beta,
gamma
(Enders et
al., 2016)
High intensity
cycling exercise
Fatigue, motor
control
Beta
(Jain et al.,
2013)
Pedaling
Sensorimotor
processing
Alpha, beta
(Hottenrott
et al.,
2013)
Cadence and
relationship with
heart rate, blood
lactate and RPE
Fatigue
Alpha, beta
(Comani et
al., 2014)
Attentional
focus, optimal
performance
Arousal,
attentional focus,
motor commands
In this part, it is the author’s intention to discuss the issues of EEG
biomarkers related to cycling performance. As previously mentioned,
studies on application of sports neuroscience in more dynamic natured
sports are still rare. A previous study had summarized five major EEG
biomarkers based on type of brainwaves which are delta, theta, alpha,
beta, gamma (Cheron et al., 2016). For the purpose of this review,
only biomarkers of EEG specifically for cycling performance was
highlighted. Based on Table 1, most studies conducted on brain
activity in cycling context are based on alpha and beta waves as well
as alpha/beta ratio. Therefore, the discussion will emphasize on these
two brainwaves and their ratio. These brainwaves mostly focused on
basic movement in cycling in which cyclists have to pedal to move the
bicycle. When a cyclist is pedalling, all reactions and mechanisms
from the human physiological and biomechanical aspects as well as
brain activity are involved.
There is contradicting finding on the effect between low cadence
training and high cadence training among cyclist. The brain cortical
activity showed changes in the frontal area for low cadence training at
baseline and after the intervention. On the other hand, for high
cadence training, the alph/beta ratio did not show changes after the
intervention period. This demonstrated that exercising at high
pedalling frequencies allowed cyclists to complete similar load with
less brain cortical acitvity. Consequently, this leads to neural
efficiency which ensures reservation of cortical resources for prolong
workloads (Ludyga et al., 2016).
It has been supported by the other study which stated that
reducing alpha and overall spectral power will produce similar
improvements in aerobic power for low and high cadence training.
However, high cadence training could prepare cyclists to maintain
high performance in endurance. In addidtion, it can also improve the
central and peripheral adaptations delaying fatigue among cyclists
(Ludyga et al., 2016a).
Compared to resting state, the alpha/beta ratio decreases during
cycling exercise as beta power increases more than alpha power
(Ludyga et al., 2016b). Increased beta activity reflects higher cortical
activation which might be the result of greater processing demands
during exercise and the tendency of the sensorimotor system to
maintain the network (Engel & Fries, 2010). As alpha power serve as
an inverse indicator of mental alertness or arousal, it can be assumed
that a lower level of arousal at rest is due to greater relaxation ability
in subjects with higher maximal oxygen consumption (VO2max)
(Nielsen, Hyldig, Bidstrup, González-Alonso, & Christoffersen,
2001). It was supported by Jacobs (2001) who further explained about
beta power with high cadence training will decrease as a response of
EEG to relaxation.
High cortical activation is necessary to provide high performance
and power output in cycling. As explained by Noakes (2011), cycling
performance is controlled by the central nervous system regulatory
mechanism. This control mechanism does not restrict the functions of
the heart or skeletal muscles but it regulates the power output by
controlling the number of recruited muscle fibres or motor units
involved in the working muscles. Training at different cadences seems
to be the key to respond to differrent requirement during a race. In
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order to increase power output at higher cadences, higher cortical
brain activation is necessary (Hottenrott et al., 2013).
While most researchers focused on changes in brain activity
caused by movement in cycling, one group of researchers conducted a
study on how different attention could change cyclist’s brain activity
(Comani et al., 2014). The study proved that the right attentional
focus can determine optimal performance during high-fatigue or
stressful situation. They claimed that when cyclists focused on the
external environment, it would lead to superior performance.
The results of EEG coherence of the alpa beta band showed that
the alpha band indicates lower arousal state and are accommmpanied
with higher alpha power eventually required in goal-directed
behavior. It was further explained that it is related to attentional focus
on the components of action and the feeling of muscle fatigue. On the
other hand, the beta band from this study indicates that there is an
involvement with sensorimotor processing that is associated with
resistance to movement, voluntary action as well as emotional
capacity in coping with fatigue. Thus, future researchers may look
into different sports as they may require different attentional focus
during competition. Apart form that, there is also a need to study the
physiological and biomechanical influence towards a cyclist’s
attentional focus that may potentially lead to neural effeciency.
Fig. 1 EEG Biomarkers and its corresponding function of cycling
Based on previous studies, this review came out with a simple
framework to present potential EEG biomarkers and its correponding
function with regards to cycling performance and exercise. According
to Fig. 1, the alpha and beta brainwaves along with the alpha/beta
ratio play important roles in identifying the mechanism of cyclists’
physiological functions and required movement to achieve optimal
performance. Nevertheless, this framework is limited to studies shown
in Table 1. Therefore, it is essential to form such adaptation from
theoretical aspects in determining mutually defined terms towards
specific physiological and biomechanical perspectives. Furthermore,
identification from specific brain region is also critical as it differs
from each other in terms of its function.
CONCLUSION
There is a lot of areas in relation to brain activity that can
siginificantly contribute to improvement of cycling performance as
well as in reaching peak performance. Generally, the methodology
used in future studies should also attempt to minimize artifacts and
possible noise. Specifically to cycling performance, current researches
have highlighted physiological function and basic movements in
cycling. However, whether it is critical to the outcome of a race, there
is a great need to identify what makes a difference between a winner
and a loser. At resting state, trained and untrained athletes have
different EEG rhythms (Del Percio et al., 2009), consequently may
suggest individual difference of potential.
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