One   approach   to   understanding   the   effects   of  exercise  on  the  brain  and  the  cortical  processes  underlying  peak  performance  is  to  measure  brain  activity  using  electroencephalography. Electroencephalography is a noninvasive technique that  uses  highly  conductive  silver  or  silver  chlochloride  (Ag/AgCl) electrodes to record brain activity,  which is also referred to as electroencephalographic (EEG) activity.

The  EEG  recording  is  actually  a  measure  of electrical  signals  that  are  produced  by  neural cells in the brain and are measurable at the scalp. Because  the  electrical  signals  must  pass  through the dura mater, cerebrospinal fluid, skull, and skin before  reaching  the  electrodes,  these  signals  are recorded  in  a  small  unit  of  measurement  called microvolts. EEG can be measured using either single  electrodes  (flat  metal  disks)  that  are  attached at particular locations on the head or an electrode  cap  that  has  fixed  electrodes  sewn  into  the  cap. The  electrodes  are  adhered  to  the  scalp  using  a specific  type  of  gel  or  paste  that  maximizes  conductance.  The  electrodes  are  connected  by  wires to  an  amplifier  and  a  computer  that  records  the brain  activity.  The  internationally  standardized 10–20  system  developed  by  Herbert  Jasper  in 1958  is  frequently  used  to  communicate  particular scalp locations used to record EEG. The locations of the sites are judged relative to landmarks on  each  individual’s  scalp,  including  the  bridge of  the  nose,  the  bony  protuberance  at  the  base of  the  skull,  and  the  midpoints  of  the  ears.  This original system included 21 electrodes that reflect measurement at frontal (F), temporal (T), parietal (P),  occipital  (O),  and  central  (C)  regions  of  the brain  (Figure  1).  In  this  system,  the  odd  number indicates that the locations are on the left side of the brain and the even numbers indicate that the locations are on the right side of the brain. More modern EEG systems can include electrode placements that record information from 32, 64, 128, or  up  to  256  sites.  In  addition  to  the  electrodes that are used to record EEG from sites of interest, an  additional  electrode  called  the  reference  electrode is used to subtract out basal activity so that the resultant recording from the sites of interest is reflective only of activity at those sites. This electrode is typically placed on the bridge of the nose or on an earlobe.


Figure 1   Jasper 10–20 System for Electrode Identification Source: Adapted from Teplan, M. (2002). Fundamentals of EEG measurement. Measurement Science Review, 2(2), 1–11.

There are several computer software programs that can be used to record and analyze EEG data. In  addition  to  recording  EEG  activity,  some  software programs can also be used for creating and presenting  different  types  of  visual  and  auditory stimuli so that EEG activity can be recorded relative to behavioral responses to those stimuli. More advanced  software  programs  integrate  EEG  data with  measures  using  other  neuroimaging  techniques  like  magnetic  resonance  imaging  (MRI), positron  emission  topography  (PET),  and  single photon  emission  computed  tomography  (SPECT) data. When collecting EEG data, there are several important  things  to  remind  participants  of  when preparing  for  the  test.  Participants  should  avoid taking  certain  medications  that  may  alter  the brain’s  electric  activity,  such  as  sedatives,  muscle relaxants,  and  sleeping  aids.  Participants  should also avoid caffeine and exercise within 12 hours of the test. The use of hair spray, gels, oils, or other hair  chemicals  should  be  avoided  on  testing  day. The participant should be instructed to be as still as possible and remain quiet when assessing resting EEG. However, current EEG technology does permit EEG measures to be taken during exercise and sport performance. When the EEG data is collected,  analog  filters  are  used  to  limit  the  recording of low and high frequency signals outside the range  of  interest,  such  as  low  frequency  signals from  breathing.  Nonetheless,  prior  to  analyzing the EEG data, several steps must be taken to further ensure that the collected data is clean. Clean data  most  purely  reflects  the  EEG  data  itself  and does  not  include  data  that  reflect  muscle  movement.  Thus,  in  addition  to  the  EEG  electrodes, participants  typically  wear  two  electrodes  placed around the outside of the eye to record eye blinks. Eye blinks and other muscle movement (e.g., from a cough, tensing muscles in the jaw) alter the EEG signal in a readily observable fashion and portions of  the  recording  that  contain  these  artifacts  can be excluded prior to analysis using either manual or automated techniques. Once the data has been cleaned, it is then digitally filtered, which decreases extraneous data in the signal while maintaining the integrity of the EEG data. At this point, the data is ready for analysis.

Spontaneous EEG

There are four main wave forms measured by EEG: alpha,  beta,  theta,  and  delta.  Alpha  activity  consists of waveforms that occur 8 to 12 times per second (Hz) and is interpreted as reflecting a relaxed state. Alpha activity tends to be low during mentally  challenging  situations.  In  sport  psychology research,  alpha  activity  has  been  associated  with being relaxed and focused and mentally prepared for performance. EEG data has been used to distinguish mental states between novice and expert performers prior to task execution like a golf putt or free throw. Imagery training has also been linked to  increased  alpha  activity.  Biofeedback  training has  been  used  to  teach  participants  to  increase alpha  activity  with  the  expectation  that  this  will result in better performance.

Beta  waves  (13–30Hz)  are  the  most  common type  of  EEG  activity  during  wakefulness  and  are present  during  mental  thought  and  activity,  particularly  during  decision-making  processes.  Theta waves (4–8Hz) appear during drowsiness and light sleep.  Delta  waves  (.5–3.5Hz)  are  found  during periods of deep sleep and are characterized by very irregular and slow wave patterns. Delta waveforms are  of  particular  interest  to  researchers  exploring the effects of exercise on sleep quality.

In addition to interest in activity at a given site, there  is  also  interest  in  examining  hemispheric asymmetry  in  EEG  responses.  In  the  sport  performance  literature,  expert  performers  typically display  a  quieting  of  left  hemisphere  activation, as  inferred  by  greater  EEG  alpha  power.  This has been interpreted as being indicative of expert performers’   ability   to   block   out   distractions, unwanted  emotions,  and  negative  thoughts  prior to  motor  responses  and  suggests  that  there  is  a causal link between this ability and performance. Exercise psychology literature has focused on EEG asymmetry as a marker of affective changes often associated  with  exercise.  The  cerebral  lateralization  hypothesis  suggests  that  anxiety  reductions and  enhanced  affect  caused  by  exercise  are  due to a decrease in right, relative to left, hemisphere activation.

Event-Related Potentials

Event-related  potentials  (ERPs)  are  time-locked waveforms  that  are  identified  after  averaging the  EEG  response  recorded  relative  to  a  particular  event.  The  event-related  portion  of  the  name expresses  that  the  timing  of  the  EEG  activity  is directly  related  (time-locked)  to  an  event;  this  is typically  a  stimulus  presentation  (e.g.,  seeing  or hearing a stimulus) or a voluntary motor response that is made (e.g., pushing a button). The EEG signal is averaged using the presentation of the stimulus  as  the  anchor  so  that  the  patterns  of  activity are synchronized to the same event. The potential portion  of  the  name  expresses  that  ERPs  reflect cumulative electrical potentials generated by neurons in the brain.

There are several different kinds of ERPs. ERPs that  occur  in  response  to  a  stimulus  are  called sensory-evoked  potentials;  ERPs  that  occur  relative  to  a  motor  response  are  called  motor  potentials.  A  common  experimental  paradigm  that  is used  to  assess  ERPs  is  the  oddball  paradigm.  In this paradigm, the participant is asked to watch a computer monitor on which frequent stimuli (e.g., an X) and infrequent stimuli (e.g., an O) are displayed. The participant is instructed to respond as quickly  as  possible  to  one  stimulus  (e.g.,  the  X) by  pressing  a  button  but  is  asked  not  to  respond when the other stimulus (the O) is displayed. The EEG  signal  is  time-locked  to  the  presentation  of the stimulus, which allows sensory-evoked potentials  to  be  observed.  Interpretations  of  ERP  data  are  based  on  the  amplitude  of  the  potential  (the vertical  distance  from  the  baseline  activity  to  the peak or trough of the component) and the latency of  the  potential  (the  elapsed  time  to  the  peak amplitude of the component from the time-locked event). The amplitude and latency of the sensory evoked potential differ depending on whether the stimulus was the frequently occurring stimulus or the  rarely  occurring  stimulus  and  also  depending upon whether the participant responded correctly, for  example,  pressing  the  button  when  was presented  or  incorrectly  by  pressing  the  button when O was presented to the stimulus. The motor potential is observed when the EEG signal is timelocked  to  the  initiation  of  the  motor  response  to press the button.

Two  additional  types  of  ERPs  are  referred  to as slow ERPs because they occur over a relatively longer period of time than do the sensory-evoked and  motor  potential.  Contingent  negative  variation (CNV) is observed when a participant is given a warning stimulus prior to presentation of a stimulus that is to be responded to, and its amplitude is increased by attention and decreased by distraction. A readiness potential is evident in the period prior to a voluntary movement and its amplitude has been shown to be related to motivation and to movement speed.

Once the data have been cleaned and averaged, the resultant waveforms are examined to identify the  components  of  interest.  The  names  of  the components  of  the  ERPs  reflect  the  direction  of the waveform (positive, P, or negative, N) relative to  the  baseline  activity  prior  to  the  presentation of the stimulus and the approximate timing of the component relative to the stimulus. As previously described,  components  of  the  ERP  are  quantified based upon their amplitude and latency.

One ERP component that is of interest to sport and  exercise  psychology  is  the  P300.  The  P300 is  a  positive  waveform  that  occurs  between  250 and  500  ms  after  the  stimulus  presentation.  This component  has  been  shown  to  be  linked  to  the allocation of attention. The amplitude of the P300 is  thought  to  be  indicative  of  increased  attention while  the  latency  provides  evidence  of  the  time necessary to evaluate the stimulus (speed of cognitive processing). When considered simultaneously, the amplitude of the P300 is expected to be larger and the latency shorter when participants are using relatively  few  attentional  resources  to  evaluate  a stimulus and perform a task. The CNV (slow ERP) occurs approximately 260 to 470 ms after a warning stimulus and is a sustained negative component of the waveform. The CNV is thought to be indicative of a participant’s expectancy or anticipation of a stimulus. In the sport and exercise psychology literature, higher amplitude CNV has been linked to quicker reaction time.


  1. Crabbe, J. B., & Dishman, R. K. (2004). Brain electrocortical activity during and after exercise:A quantitative synthesis. Psychophysiology, 41(4), 563–574.
  2. Hatfield, B. D., & Kerick, S. E. (2007). The psychology of superior sport performance: A cognitive and affective neuroscience perspective. In G. Tenenbaum & R. C. Eklund (Eds.), Handbook of sport psychology (3rd ed., pp. 84–112). Hoboken, NJ: Wiley.
  3. Niedermeyer, E., & Lopes Da Silva, F. H. (2005). Electroencephalography: Basic principles, clinical applications, and related fields. Philadelphia: Lippincott, Williams & Wilkins.
  4. Petruzzello, S. J., Ekkekakis, P., & Hall, E. E. (2006). Physical activity, affect, and electroencephalogram studies. In E. O. Acevedo & P. Ekkekakis (Eds.), Psychobiology of physical activity (pp. 111–128). Champaign, IL: Human Kinetics.
  5. Teplan, M. (2002). Fundamentals of EEG measurement. Measurement Science Review, 2(2), 1–11.
  6. Thompson, T., Steffert, T., Ros, T., Leach, J., & Gruzelier, J. (2008). EEG applications for sport and Methods, 45(4), 279–288.

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