earticle

영어

Electroencephalogram (EEG) research has gained widespread use in various research domains due to its valuable insights into human body movements. In this study, we investigated the optimization of motion discrimination prediction by employing an artificial intelligence deep learning recurrent neural network (gated recurrent unit, GRU) on unique EEG data generated from specific movement types among EEG signals. The experiment involved participants categorized into five difficulty lev-els of postural control, targeting gymnasts in their twenties and college students majoring in physical education (n=10). Machine learning tech-niques were applied to extract brain-motor patterns from the collected EEG data, which consisted of 32 channels. The EEG data underwent spectrum analysis using fast Fourier transform conversion, and the GRU model network was utilized for machine learning on each EEG frequen-cy domain, thereby improving the performance index of the learning operation process. Through the development of the GRU network algo-rithm, the performance index achieved up to a 15.92% improvement com-pared to the accuracy of existing models, resulting in motion recognition accuracy ranging from a minimum of 94.67% to a maximum of 99.15% between actual and predicted values. These optimization outcomes are attributed to the enhanced accuracy and cost function of the GRU net-work algorithm’s hidden layers. By implementing motion identification optimization based on artificial intelligence machine learning results from EEG signals, this study contributes to the emerging field of exercise re-habilitation, presenting an innovative paradigm that reveals the inter-connectedness between the brain and the science of exercise.