Zero-Shot EEG-to-Gait Decoding via Phase-Aware Representation Learning

• URL: http://arxiv.org/abs/2506.22488v1
• Date: Tue, 24 Jun 2025 06:03:49 GMT
• Title: Zero-Shot EEG-to-Gait Decoding via Phase-Aware Representation Learning
• Authors: Xi Fu, Weibang Jiang, Rui Liu, Gernot R. Müller-Putz, Cuntai Guan,
• Abstract summary: We propose NeuroDyGait, a domain-generalizable EEG-to-motion decoding framework.<n>It uses structured contrastive representation learning and relational domain modeling to achieve semantic alignment between EEG and motion embeddings.<n>It achieves zero-shot motion prediction for unseen individuals without requiring adaptation and superior performance in cross-subject gait decoding on benchmark datasets.
• Score: 9.49131859415923
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Accurate decoding of lower-limb motion from EEG signals is essential for advancing brain-computer interface (BCI) applications in movement intent recognition and control. However, challenges persist in achieving causal, phase-consistent predictions and in modeling both inter- and intra-subject variability. To address these issues, we propose NeuroDyGait, a domain-generalizable EEG-to-motion decoding framework that leverages structured contrastive representation learning and relational domain modeling. The proposed method employs relative contrastive learning to achieve semantic alignment between EEG and motion embeddings. Furthermore, a multi-cycle gait reconstruction objective is introduced to enforce temporal coherence and maintain biomechanical consistency. To promote inter-session generalization, during fine-tuning, a domain dynamic decoding mechanism adaptively assigns session-specific prediction heads and learns to mix their outputs based on inter-session relationships. NeuroDyGait enables zero-shot motion prediction for unseen individuals without requiring adaptation and achieves superior performance in cross-subject gait decoding on benchmark datasets. Additionally, it demonstrates strong phase-detection capabilities even without explicit phase supervision during training. These findings highlight the potential of relational domain learning in enabling scalable, target-free deployment of BCIs.

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