Priming a Hitters Brain: An EEG and Smash Factor Study

Athlete assessment and performance modalities have previously reached into varying disciplines. Mostly centering around the musculoskeletal, musculotendinous, and psychological approaches, assessments to assess and increase the ability for an athlete's brain to perform during a skill have been greatly underlooked.

With the use of wireless electroencephalogram (EEG) technology, it is possible to observe the phenomena that happen across the brain's cerebral cortex, and therefore find weakpoints and interventions to increase the efficiency and overall performance of brain function during skill.

In this study, we assessed the cerebral cortex at the time of pitch recognition in hitters pre- and post-juggling, and observed their average increase in smash factor throughout the paradigm.

EEG Overview

An EEG is a tool that can measure voltage differences in the cerebral cortex of the brain through the scalp, granting insight into cerebral activity.

If we take a deeper look into the brain, we can observe individual neurons that will fire synchronously as large groups to conduct a message; the summation of these signals (excitatory and inhibitory) is the neural activity detectable by an EEG.

This signal may also be influenced by electrical activity generated by muscles such as the eyes, scalp, tongue, and most notably in a hitter, the jaw and neck. While generally these electromyogram (EMG) activity occur at a much higher frequency (80-3000 Hz), the interference of these waves can artificially inflate lower frequencies that an EEG cannot decipher. 

The Solution

Previous Driveline research with EEG utilized a 12.5-30 Hz range to assess beta-waves. In this study, we purposely did not include this range to limit EMG contamination. This contamination would occur from jaw-neck muscle bursts—inflating 15-40 Hz power bands—and to get a true reading of neural clutter, we focused on parietal alpha (α, 8-12 Hz) and sensorimotor beta mu (μ, 8-12 Hz).

Parietal alpha is a classic visual-cortex readiness marker, and drops as the visual system locks on to the ball.
Sensorimotor mu being suppressed represents a motor-prep "go" signal, and is well-validated in pre-movement EEG.

While beta waves are generally the go-to for alertness, mental engagement, and conscious processing, low-beta waves (12-18 Hz) often behave identically to mu (8-12 Hz) over central sites—making this decision both efficacious and reliable in obtaining these measurements.

Layman's terms:

An EEG measures brain waves, and allows insight into what is happening in the brain.

What is Neural Clutter?

Neural clutter refers to, in this case, the negative impact that disorganized environments and excessive, irrelevant information have on the brain's ability to process information and function effectively, leading to reduced focus, impaired memory, and increased stress.

It is theorized that neural clutter negatively impacts information flow through the visual cortex, therefore affecting downstream perception-action coupling and subsequent motor function. So, in order to improve information flow and positively impact the perception-action loop that corresponds to hitting, one must provide an intervention to reduce or dampen the amplitude of neural clutter present during skill work.

Layman's terms:

Too much sensory input causes overload and congestion in information flow. We provided a large influx of sensory and motor information to reduce future input—think habituation, or getting used to a smell or sound so you no longer notice it.

Driving Questions

• Does neural priming affect session results (e.g. increase in smash factor; decrease in neural clutter)

• Do participants in the neural priming group see any lasting benefit from the primers

  • e.g. what portion of gains are priming and what portion (if any) are from them developing/improving coordination via the primers

EEG Findings

The seated baseline testing observed that the experimental athlete started with ~6 dB lower absolute α/μ than the control athlete. Only one baseline was performed at the initiation of the study (figure 1).

Week 1 (no juggling) showed both athletes to have elevated cortical power during hitting, but control overshot experimental by ~24 percentage points (control Δ%: +151±15, experimental Δ%: +128±12; p=0.003). Week 2 and week 3 pre-hitting juggling ensued, showing experimental's NCI to be flat while control kept climbing (control Δ%: +186±18 —> +208±18, experimental Δ%: +142±13 —> +132±11; p<0.001). 

The final week—washout—removed the juggling stimulus, and showed that control cortical power increased sizably while experimental held steady (control Δ%: +297±25, experimental Δ%: +134±10; p<0.001). The 160 percentage point gap widened (figure 2).

Smash Factor Findings

Linear mixed-effects modeling revealed no significant main effect of group (Priming vs. Control) on change in smash factor ((β = –0.018, SE = 0.014, p = .22). Additionally, neither Week (β = 0.00025, SE = 0.0046, p = .96) nor Phase (Intervention: β = –0.015, SE = 0.016, p = .36; Washout: β = –0.022, SE = 0.030, p = .48) significantly influenced performance. The interaction terms between Group and Phase were also non-significant (Intervention: β = 0.009, SE = 0.018, p = .63; Washout: β = 0.007, SE = 0.028, p = .81), indicating that the priming intervention did not yield differential improvements compared to the control group over time.

Examination of the time-series data revealed that although the Priming group began with numerically higher initial exit velocities and bat speeds, improvements over the intervention period were negligible or slightly negative. Conversely, the Control group demonstrated gradual improvement, reducing the initial performance gap. However, these differences did not reach statistical significance, with considerable overlap in the standard error margins. Additional metrics, such as On-Plane Efficiency and AA-LA Mismatch, showed trends that either favored the control group or indicated negligible differences. The relationship between bat speed and exit velocity remained consistent across groups (r ≈ 0.8), with no notable divergence between regression lines, further reinforcing the minimal impact of the priming intervention.

Discussion

Smash factor

Contrary to expectations and EEG finding, the smash factor findings do not support the hypothesis that the priming intervention enhances hitting performance in the metrics examined compared to standard training alone. Statistical analyses uniformly indicate a lack of significant differences between groups, with small and inconsistent effect sizes that frequently favored the control condition. This suggests that the priming stimulus, as implemented, neither produced acute enhancements nor contributed to long-term adaptations beyond standard training variability.

Several methodological limitations may have contributed to these null findings. The small sample size (n = 5 per group) substantially limited statistical power, making it difficult to detect potentially meaningful differences that fell within measurement error margins. Additionally, baseline differences between groups likely obscured subtle intervention effects, despite efforts to adjust through change-score analyses. Future research employing a larger sample size, careful stratified randomization to ensure baseline equivalence, and clearly defined time windows for acute versus chronic adaptations is necessary to adequately explore priming interventions.

EEG

The observed findings provide evidence that juggling prior to batting practice effectively moderates the accumulation of cortical "neural clutter," as indexed by combined parietal α and motor μ power. The consistent divergence between control and experimental conditions suggests that brief visuomotor priming activities—such as juggling—may induce a state of neural efficiency characterized by stable, lower cortical activation, potentially reflecting reduced attentional and motor execution demands.

The ~6 dB lower absolute baseline α/μ power observed in the experimental athlete indicates an inherently more "neural-efficient" starting state compared to the control athlete. While this inherent difference might affect absolute neural clutter levels, the within-subject percent-change normalization used for the Neural-Clutter Index (NCI) robustly controls for baseline disparities, thus ensuring that observed session differences are intervention-driven rather than baseline-dependent.

The significant and progressive divergence in cortical activation during the intervention weeks (Weeks 2–3) highlights the ability of juggling to stabilize cortical activity over repeated exposures to batting. Notably, the experimental athlete's NCI remained stable (~140%), whereas the control athlete experienced a progressive increase, culminating in a substantial 160 percentage point gap at washout. This persistent stabilization three weeks post-intervention strongly supports the durability of neural priming effects.

From a practical coaching perspective, these results underscore the utility of brief juggling routines as a low-cost, low-time investment strategy for promoting neurological efficiency. Coaches could incorporate such routines to mitigate cumulative neural stress across competitive seasons, potentially enhancing athletes’ readiness and responsiveness.

Nonetheless, several limitations warrant cautious interpretation. The small sample size limits generalizability, and inherent individual differences—such as baseline cortical activation levels, temperament, neuromuscular strategies, and headset fit—may confound results. 

Future Direction

For smash factor to be a notable metric, the size of the study would have to significantly increase to create higher effect sizes, as well as decrease the noise and variability associated with day-to-day skill work.

For replication, an EEG headset suitable for high velocity movement (i.e. a skullcap EEG) would limit movement artifacts.

However, we believe smash factor is not the correct interpretation for this study, and an afferent measure directly assessing brain response—specifically, gaze-tracking—is a logical next step to observe with a juggling intervention. By employing gaze-tracking technology, we can directly measure the immediate visual response to the intervention, contrasting with the indirect, downstream neural effects observed in the current EEG-based paradigm. Such an approach would provide direct, actionable insights into how juggling may enhance visual processing and attentional focus during batting practice.

References

Malik, J., Stemplewski, R., & Maciaszek, J. (2022). The Effect of Juggling as Dual-Task Activity

on Human Neuroplasticity: A Systematic Review. https://doi.org/10.3390/ijerph19127102

Stoykov, M. E., & Madhavan, S. (2015). Motor priming in neurorehabilitation. Journal of

Neurologic Physical Therapy : JNPT, 39(1), 33–42. https://doi.org/10.1097/NPT.0000000000000065

Xu, Xize et al. (2024). Spatial context non-uniformly modulates inter-laminar information flow in the primary visual cortex.

Neuron: Volume 112, Issue 24, 4081 - 4095.e5. https://doi.org/10.1016/j.neuron.2024.09.021