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Exploration of Motion Prediction between Electroencephalography and Biomechanical Variables during Upright Standing Posture

바로서기 동작 시 EEG와 역학변인 간 동작 예측의 탐구

  • Received : 2024.04.16
  • Accepted : 2024.06.04
  • Published : 2024.06.30

Abstract

Objective: This study aimed to explore the brain connectivity between brain and biomechanical variables by exploring motion recognition through FFT (fast fourier transform) analysis and AI (artificial intelligence) focusing on quiet standing movement patterns. Method: Participants included 12 young adult males, comprising university students (n=6) and elite gymnasts (n=6). The first experiment involved FFT of biomechanical signals (fCoP, fAJtorque and fEEG), and the second experiment explored the optimization of AI-based GRU (gated recurrent unit) using fEEG data. Results: Significant differences (p<.05) were observed in frequency bands and maximum power based on group and posture types in the first experiment. The second study improved motion prediction accuracy through GRU performance metrics derived from brain signals. Conclusion: This study delved into the movement pattern of upright standing posture through the analysis of bio-signals linking the cerebral cortex to motor performance, culminating in the attainment of motion recognition prediction performance.

Keywords

Acknowledgement

This work was supported by the Ministry of Education of the Republic of Korea and the National Research Foundation of Korea (NRF-2019S1A5A2A01047204).

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