Athletes’ State Monitoring under Data Mining and Random Forest

Abstract

The study aims to train athletes to be in top form and at their best in the competition. Based on the relevant theoretical research, archers are taken as the research subjects, the characteristics of archery are analyzed, and the electroencephalogram (EEG) features of the athletes in different stages of precompetition training are monitored. And the athletes’ competitive state monitoring model based on random forest (RF) is implemented and tested. The experimental results show that the athletes’ dominant frequency of brain band α, EEG entropy, central fatigue index, excitation inhibition index, and cerebral state index in precompetition training is significantly different from those in training ( P < 0.05 ).The monitoring model implemented classifies athletes’ competitive states. Compared with the support vector machine (SVM) classification model, its classification accuracy is higher than 90%. The overall classification accuracy is 89.74%, more significant than SVM. The research provides a reference for monitoring athletes’ competitive states and helps them regulate their states in real time.

Application and Development of EEG Acquisition and Feedback Technology: A Review

“…Its applications extend beyond traditional research, spanning healthcare, brain-computer interfaces (BCIs), cognitive augmentation, and neuropsychological assessments. The fusion of EEG with artificial intelligence, machine learning, and advanced data analytics is poised to reveal novel dimensions of brain function, leading to more precise diagnostics and tailored interventions for neurological disorders [106][107][108][109][110][111][112][113]. Furthermore, the synergy of EEG technology with other biometric data, such as eye tracking and heart rate variability, promises a holistic comprehension of cognitive and emotional states.…”

“…These algorithms are crucial for understanding and interpreting the brain's electrical activity [102][103][104][105]. Several commonly used EEG classification algorithms include support vector machines (SVM), K-nearest neighbors (KNN), random forests, convolutional neural networks (CNN), and recurrent neural networks (RNN) [106][107][108][109][110][111][112][113].…”

“…The overall classification accuracy stands at 89.74%, surpassing that of SVM. This study offers valuable insights into monitoring athletes' competitive states, aiding them in real-time adjustments of their performance levels [111].…”