Key result
The differential entropy feature of the gamma band achieved the best classification accuracy of 74.91% in distinguishing excellent from poor shooting performance during the aiming period.
Why the study?
Neural markers that can effectively and stably characterize various shooting performances remained unclear.
Observational (n=5)
No
Differential entropy of the gamma band in EEG signals can effectively classify shooting performance with 74.91% accuracy, suggesting its potential as a neural marker for shooter evaluation.
Gamma-band EEG markers may aid shooter selection; leaves open validation in larger prospective trials before practice adoption.
The frequency-domain characteristics of electroencephalogram (EEG) during shooting used to evaluate and improve shooting performance has become a research hotspot in recent years. However, neural markers that can be effectively and stably used to characterize various shooting performances remain unclear. In this study, a real shooting experiment was designed to study the effective EEG neural markers for different shooting performances. EEG data were obtained during the aiming period,the frequency domain characteristics such as spectrum, power spectrum, and differential entropy corresponding to Delta wave, Theta wave, Alpha wave, Beta wave, and Gamma wave were extracted respectively. Classical statistical analysis and machine learning methods were used to investigate the effectiveness of the characteristics. The results revealed that the overall energy of the EEG signals was low and stable for the participants with excellent shooting performance. The differential entropy feature of the gamma band as a effective neural marker had achieved the best classification accuracy of 74.91% throughout the aiming period. The results verified that the brain state of shooters is stable and their brains exhibit higher cognitive information processing efficiency during accurate shootings. The feasibility of using EEG neural markers to evaluate shooting performance is a novel avenue for the selection and performance evaluation of high-performance shooters.
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Li et al. (2023) conducted an observational in Shooting performance (n=5). Shooting performance (excellent vs poor) was evaluated on Classification accuracy of shooting performance using EEG frequency-domain features. The differential entropy feature of the gamma band achieved the best classification accuracy of 74.91% in distinguishing excellent from poor shooting performance during the aiming period.
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