Biomechanical assistance for basketball training movements based on cross-domain EEG physical fitness classification
Abstract
Cross-domain EEG signals offer valuable insights into the cortical neuron activity patterns and the functional dynamics of the central nervous system (CNS). Within the framework of biomechanics, EEG has emerged as a critical tool to investigate the interplay between neural control and physical performance. This study explores EEG complexity parameters in specific brain regions of 24 elite athletes under three distinct states: Rest, unloaded exercise, and loaded exercise. By integrating biomechanical and electrophysiological analyses, the study uncovers functional adaptations in the parietal and occipital regions, key centers for somatosensory and visual processing, respectively, in high-performance athletes. The findings reveal no significant gender differences in EEG complexity under these conditions, but highlight the effects of long-term specialized training in enhancing CNS adaptability. This adaptation is reflected in a reduced reliance on visual input, a trait distinguishing elite athletes from non-athletes. Despite the small sample size, correlations between three nonlinear EEG parameters—maximum Lyapunov exponent, approximate entropy, and Lempel-Ziv complexity—and CNS fatigue were observed. These parameters provide a robust framework for monitoring CNS fatigue and assessing the effects of exercise on neural function. This study bridges biomechanics and neural analysis, offering a novel perspective on CNS functionality under varying exercise states. The results contribute a theoretical foundation for the development of biomechanical guidance systems tailored for basketball training, with implications for optimizing athletic performance and promoting CNS health.
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