Using background EEG to predict baseball batting performance




Pluta, Anthony III

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In this thesis, I sought to determine whether frequency bands in the human electroencephalogram could be used to predict baseball batting performance. Past electroencephalographic (EEG) studies have found that alpha power in the human electroencephalogram predicts subsequent performance. Specifically, Mathewson and colleagues (2012) found that background brain activity, in particular, frontal alpha, had a direct correlation with one’s ability to learn a video game. Here, we decided to see if a similar result would hold true for baseball batting performance. We used a portable electroencephalographic (EEG) data collection system to record EEG data prior to batting practice. Participants sat quietly in a room with the portable EEG unit affixed to their head. Participants then stared in silence at a fixation cross in the center of a computer screen for 30 seconds and then counted backwards from 1000 by 7’s for 30 seconds as a masking task while background EEG was recorded. Player’s were then immediately given live batting practice and with performance judged by three different coaches on four different criteria. The four criteria were: batting mechanics, power, contact, and the batter’s ability to recognize good and bad pitches. Post-hoc, a frequency decomposition was performed on each participant’s EEG data to obtain power in all frequency bands. A correlation analysis of EEG power and batting performance showed that beta power and not alpha power predicted the subsequent performance of the batter. Importantly, a high correlation and significance show that predicting a batter’s performance with a portable EEG system, specifically the MUSE Headband, is highly plausible.



Baseball, Sport Science, Kinesiology, EEG, Performance, Predicting Performance, Beta