EEG techniques can be used to detect psychological and physiological responses during sport activities; however, these responses can only be measured in a limited number of sports, which does not include open skill and team sports such as soccer, tennis, basketball, volleyball, and baseball. Target sports executing closed skill tasks, which only require simple body motions, have been used to date. For example, the spectral power in background EEG has been examined during the pre-shot period of shooting (Hatfield et al. 1984; Bird 1987; Hillman et al. 2000; Loze et al. 2001; Kerick et al. 2001, 2004), archery (Salazar et al. 1990; Landers et al. 1991, 1994), and dart-throwing (Radlo et al. 2002). These studies have been listed in a review article by Hatfield and colleagues (2004). A Fast Fourier Transform is used to analyze spectral power algorithm and this decomposes the electrical signal into its frequency components; therefore, the amplitude of each designated frequency can be measured and expressed as an absolute or relative power. Absolute power represents the mean power in each frequency band selected, while relative power represents the relationship between the power in the selected frequency bands and the total power (Crews and Landers 1993). Alpha (about 8–12 Hz) and beta (about 14–30 Hz) band oscillations generally decrease over sensorimotor cortical areas during motor preparation and the execution of voluntary self-paced movements. This phenomenon is termed event-related desynchronization (ERD) (for a review, see Pfurtscheller and Lopes da Silva 1999).

In addition to target sports including shooting, archery, and dart-throwing, golf putting has also been often used to investigate the relationship between sports performance and the brain because the putting swing and motion are not very dynamic and pure EEG signals can be recorded during the swing. Ability in golf putting is associated with synchronizing sensory information in planning and control for the appropriate motor response (Craig et al. 2000). To successfully perform golf putting, the golfer has to consider the distance from the hole to the ball, ball direction, putting force, and environmental conditions such as slope and grain direction. Accordingly, the visual system must orient to and process the most salient perceptual cues necessary to ascertain both distance and direction information, while working memory matches stroke tempo with the requisite stroke force (Mann et al. 2011).

Crews and Landers (1993) recorded EEG activity in 34 highly skilled golfers with electrodes attached over the motor and temporal cortices during the 3 s prior to golf putting. They analyzed three parameters from the EEG activity: slow shift, 40 Hz (gamma) band activity, and relative power spectrum. They found a decrease in the three parameters in the left hemisphere, motor cortex activity as the players prepared to putt, and during the last second preceding the putt, and increased alpha activity in the right hemisphere correlated with decreased error.

Baumeister and colleagues (2008) compared theta (4.75–6.75 Hz), alpha-1 (7–9.5 Hz), alpha-2 (9.75–12.5 Hz), and beta-1 (12.75–18.5 Hz) spectral powers in golf putting between expert golfers and unskilled novices. Skill-dependent differences were observed in frontal theta and parietal alpha-2 spectral power, which suggested that golfers develop task solving strategies including focused attention and an economy in parietal sensory information processing, resulting in a more successful performance.

Babiloni and colleagues (2008) examined EEG spectral power in expert golfers during putting. They reported that high-frequency alpha power (about 10–12 Hz) was smaller in amplitude over the frontal midline, and also smaller in the arm and hand region of the right primary sensorimotor area during successful putting than during unsuccessful putting; the greater the reduction in alpha power, the smaller the error in unsuccessful putts. The results of this study suggest that novices may use alpha power to evaluate their performances.

Baumeister and colleagues (2010) compared brain activity during motor tasks between real and virtual (the Nintendo Wii) golf putting in ten golfers. The score and EEG activity were recorded continuously, with a significantly better score being recorded in real putting. Theta spectral power at frontal electrodes was significantly larger in real putting than in the virtual putting, and alpha-2 power at the parietal electrodes was also significantly larger in real putting than in virtual putting. They suggested that putting performance and brain activity were influenced by the choice of a real or virtual environment; the increase in frontal theta power indicated more focused attention, and higher alpha-2 power was related to the quantity of sensory information processing in real putting.

Babiloni and colleagues (2011) analyzed the coordination of cortical activity, as reflected by functional coupling of alpha rhythms across cortical regions, in 12 expert golfers. They showed that intra-hemispheric low-frequency alpha coherence (8–10 Hz) in bilateral parietal-frontal regions and parietal-central regions was higher in amplitude in successful than in unsuccessful putts. The same phenomenon was confirmed in intra-hemispheric high-frequency alpha coherence (10–12 Hz) in bilateral parietal-frontal regions. These findings suggest that the intra-hemispheric functional coupling of cortical alpha rhythms between the visuo-spatial parietal area and other cortical areas is implicated in the fine motor control of a golfer’s performance.

Mann and colleagues (2011) recorded movement-related cortical potentials (MRCPs) and the quiet eye period (QE) to assess the potential mechanisms underlying the psychomotor skills that differentiate expert and near-expert performers. MRCPs are recorded before self-initiated voluntary movement, and reflect movement preparation processing not involving cognitive processing for an imperative stimulus (reviewed in Shibasaki and Hallett 2006). These potentials begin with a slow rising negativity, called the Bereitschaftspotential (BP), and progress to a steeper, later negativity, which starts approximately 500 ms before movement onset, and is called the negativity slope (NS’). According to Vickers and Adolphe (1997), the QE refers to a gaze behavior observed immediately prior to movement in aiming tasks, and a temporal period in which task-relevant environmental cues are processed and motor plans are coordinated for the successful completion of an upcoming task. Mann and colleagues (2011) categorized 20 golfers into two groups, those with a low handicap (LH: experts) and those with a high handicap (HH: non-experts). Differences were observed in QE duration and BP, with experts exhibiting a prolonged QE duration and greater cortical activation in the right-central region than those of the non-experts. A significant relationship between cortical activation and QE duration was also noted. Their findings suggest a motor programming/preparation function for the QE duration.

Reinecke and colleagues (2011) compared brain activity results from laboratory and field conditions during golf putting in 12 university students. They reported that a significant difference was only observed in theta power at the F4 electrode between two conditions. The results of their study indicated the possibility to extend the limitations of the EEG methodology in its application to sports and exercise sciences.

Taking these studies into consideration, alpha band activity appears to be related to sports performance, especially successful or unsuccessful trials. In other words, the functional characteristics of athletes’ brains may be explained by the coupling of alpha rhythms during actual sporting activities. In general, alpha rhythms reflect functional modes of the basal forebrain, thalamus, and cortical loops that facilitate/inhibit the transmission and retrieval of both sensorimotor and cognitive information into the brain (Babiloni et al. 2011). However, there appears to be a difference in the functional meaning between low-frequency (about 8–10 Hz) and high-frequency (about 10–12 Hz) alpha band activities. Low-frequency alpha band activity reflects arousal, attentive readiness, and effort (Klimesch 1999; Pfurtscheller and Lopes da Silva 1999), while high-frequency alpha band activity is related to the task-related oscillation of specific neural systems for sensorimotor or semantic information (Klimesch 1999). Based on these findings, alpha band activity in the brain may be one of the key factors determining actual sports performance. On the other hand, theta band activity may be directly associated with attention processing and working memory rather than motor control. Previous studies suggest that the anterior cingulate cortex (ACC) may be the generator of frontal theta band activity based on the findings obtained from EEG and MRI (Gevins et al. 1997), a dipole source model (Onton et al. 2005), and LORETA (Sauseng et al. 2007). The ACC was shown to be involved in a range of executive functions such as processing information and decision making; however, most investigators view this subcortical region as an important component of the human attentional control system (Baumeister et al. 2010).

Several studies have focused on the characteristics of athletes’ brains, by comparing their results with those obtained from non-athletes (novices). Neuroimaging studies using fMRI and structural MRI have examined specific brain activities in golfers. fMRI, which measures blood oxygenation level-dependent (BOLD) signals, has been used not only as a tool for mapping brain activity, but also as a means of studying the dynamics of neural networks by tracking fMRI response characteristics across various spatial and temporal scales (Logothetis et al. 2001). As mentioned above, fMRI cannot be used to directly record brain activity during actual sports performance; however, this technique can be used to demonstrate differences in the underlying mechanisms of brain activity between experts and non- experts.