A Novel Hybrid Deep Neural Network for Predicting Athlete Performance Using Dynamic Brain Waves

Abstract

The exploration of the performance of elite athletes by cognitive neuroscience as a research method has become an emerging field of study in recent years. In the research of cognitive abilities and athletic performance of elite athletes, the tasks of an experiment are usually performed by athletics task of closed skills rather than open skills. Thus, little has been conducted to explore the cognitive abilities and athletic performance of elite athletes with open skills. This study is novel as it attempts at predicting how table tennis athletes perform by collecting their dynamic brain waves when executing specific plays of table tennis, and then putting the data of dynamic brain waves to deep neural network algorithms. The method of this study begins with the collection of data on the dynamic brain waves of table tennis athletes and then converts the time domain data into frequency domain data before improving the accuracy of categorization using a hybrid convolutional neural networks (CNN) framework of deep learning. The findings hereof were that the algorithm of hybrid deep neural networks proposed herein was able to predict the sports performance of athletes from their dynamic brain waves with an accuracy up to 96.70%. This study contributes to the literature in cognitive neuroscience on dynamic brain waves in open skills and creates a novel hybrid deep CNN classification model for identifying dynamic brain waves associated with good elite sports performance.