EEG-to-gait decoding via phase-aware representation learning

Summary

Researchers developed NeuroDyGait, a two-stage phase-aware EEG-to-gait decoding framework that models temporal continuity and cross-subject variability. The system substantially outperformed existing methods on benchmark datasets while maintaining inference latency below 5 milliseconds, satisfying real-time brain-computer interface requirements.