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

TL;DR

This paper introduces NeuroDyGait, a phase-aware EEG-to-gait decoding framework that improves real-time lower-limb motion prediction for BCI applications by modeling temporal and domain relationships, demonstrating superior performance on benchmark datasets.

Contribution

The novel two-stage framework explicitly models phase continuity and domain relationships, enabling accurate, real-time EEG-to-gait decoding with interpretability and cross-subject generalization.

Findings

Outperforms recent models like EEG2GAIT on benchmark datasets.

Achieves inference latency below 5 ms per window for real-time use.

Provides interpretable neural correlates of gait phases.

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. This study presents NeuroDyGait, a two-stage, phase-aware EEG-to-gait decoding framework that explicitly models temporal continuity and domain relationships. To address challenges of causal, phase-consistent prediction and cross-subject variability, Stage I learns semantically aligned EEG-motion embeddings via relative contrastive learning with a cross-attention-based metric, while Stage II performs domain relation-aware decoding through dynamic fusion of session-specific heads. Comprehensive experiments on two benchmark datasets (GED and FMD) show substantial gains over baselines, including a recent 2025 model EEG2GAIT. The framework generalizes to unseen subjects and maintains inference latency below 5 ms per window, satisfying real-time BCI requirements. Visualization of learned attention and phase-specific cortical saliency maps further reveals interpretable neural correlates of gait phases. Future extensions will target rehabilitation populations and multimodal integration.